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• Published: 23 February 2022

Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

• Asaf Tzachor   ORCID: orcid.org/0000-0002-4032-4996 1 , 2 ,
• Medha Devare   ORCID: orcid.org/0000-0003-0041-4812 3 , 4 ,
• Brian King   ORCID: orcid.org/0000-0002-7056-9214 3 ,
• Shahar Avin   ORCID: orcid.org/0000-0001-7859-1507 1 &
• Seán Ó hÉigeartaigh   ORCID: orcid.org/0000-0002-2846-1576 1 , 5  

Nature Machine Intelligence volume  4 ,  pages 104–109 ( 2022 ) Cite this article

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Global agriculture is poised to benefit from the rapid advance and diffusion of artificial intelligence (AI) technologies. AI in agriculture could improve crop management and agricultural productivity through plant phenotyping, rapid diagnosis of plant disease, efficient application of agrochemicals and assistance for growers with location-relevant agronomic advice. However, the ramifications of machine learning (ML) models, expert systems and autonomous machines for farms, farmers and food security are poorly understood and under-appreciated. Here, we consider systemic risk factors of AI in agriculture. Namely, we review risks relating to interoperability, reliability and relevance of agricultural data, unintended socio-ecological consequences resulting from ML models optimized for yields, and safety and security concerns associated with deployment of ML platforms at scale. As a response, we suggest risk-mitigation measures, including inviting rural anthropologists and applied ecologists into the technology design process, applying frameworks for responsible and human-centred innovation, setting data cooperatives for improved data transparency and ownership rights, and initial deployment of agricultural AI in digital sandboxes.

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Acknowledgements

This paper was made possible through the support of a grant from Templeton World Charity Foundation. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation.

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Asaf Tzachor, Shahar Avin & Seán Ó hÉigeartaigh

School of Sustainability, Reichman University (IDC Herzliya), Herzliya, Israel

• Asaf Tzachor

Platform for Big Data in Agriculture, CGIAR, Cali, Colombia

Medha Devare & Brian King

International Institute for Tropical Agriculture, CGIAR, Ibadan, Nigeria

Medha Devare

Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK

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A.T., M.D., B.K., S.A. and S.Ó.H, developed the paper jointly and all contributed equally to the writing of the text.

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Tzachor, A., Devare, M., King, B. et al. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat Mach Intell 4 , 104–109 (2022). https://doi.org/10.1038/s42256-022-00440-4

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Artificial Intelligence and Sustainability pp 53–64 Cite as

AI for Sustainable Agriculture: A Systematic Review

• Mohamed Ahmed Alloghani 8 , 9  
• First Online: 26 November 2023

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Part of the book series: Signals and Communication Technology ((SCT))

In the contemporary realm of agricultural research, the integration of artificial intelligence (AI) with traditional practices is progressively emerging as a focal theme. Nonetheless, the scholarly landscape has demonstrated a notable dearth in examining the intricate relationship between AI and the tripartite principles of sustainable agriculture: economic viability, environmental stewardship, and social responsibility. Utilizing the PRISMA methodology, this systematic review endeavors to bridge this gap, offering an exhaustive examination of the aforementioned relationship. The analysis revealed that while many studies have explored individualized AI applications, few have situated these advancements within a holistic sustainability framework. The findings underscore AI’s potential, when aptly channeled, to address the multifaceted challenges inherent in sustainable agriculture. In essence, this review highlights AI’s transformative capacity to redefine agricultural frameworks, emphasizing its central role in guiding agriculture towards a future deeply rooted in sustainability principles.

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Alloghani, M.A. (2024). AI for Sustainable Agriculture: A Systematic Review. In: Artificial Intelligence and Sustainability. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-45214-7_3

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Research Article

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

Roles Conceptualization, Formal analysis, Investigation, Software, Visualization, Writing – original draft

Affiliation Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary

Roles Conceptualization, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – review & editing

* E-mail: [email protected]

Roles Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

Affiliations Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary, North-West University, Vanderbijlpark, South Africa

• Priya Rani Bhagat, 
• Farheen Naz, 
• Robert Magda

• Published: June 9, 2022
• https://doi.org/10.1371/journal.pone.0268989
• Reader Comments

There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000–2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI’s role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.

Citation: Bhagat PR, Naz F, Magda R (2022) Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis. PLoS ONE 17(6): e0268989. https://doi.org/10.1371/journal.pone.0268989

Editor: Ardashir Mohammadzadeh, University of Bonab, ISLAMIC REPUBLIC OF IRAN

Received: November 18, 2021; Accepted: April 29, 2022; Published: June 9, 2022

Copyright: © 2022 Bhagat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data for bibliometric analysis was extracted from SCOPUS database using relevant keywords provided within the study.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

As the world tries to battle through the novel challenges and obstacles caused by the Covid-19 pandemic and every sector faced detrimental impact of the pandemic at different stages of a business process. The agriculture sector is one of these sectors which faced several disruptions including labor supply for production, reduced transportation of agriculture produce, and risks associated with agriculture markets. Therefore, artificial intelligence (AI) systems and solutions will find contemporary applications in the field of agriculture and sustainable farming practices. Evidently, the pandemic is not a temporary disruption of normal but a milestone that will change the course of modern-history towards a sustainability driven society [ 1 , 2 ]. Sustainability is a multidimensional discipline that has been the popular research interest of scholars for the past few decades [ 3 , 4 ]. Because of its multidisciplinary attribute sustainability encompasses a wide variety of subjects ranging from climate, environment, green economy, food safety, sustainable agriculture, clean technologies etc. In a 2018 research article, Ávila et al. highlighted that in the field of education in sustainability, expansive research in every contributing domain is necessary to embark on the trail to global development [ 5 ].

The concept of “sustainable agriculture” has received increased attention in recent years with the rising technological improvements. In a 2020 keyword citation burst analysis on ecological modernization approach by Rocchi et al., “sustainable agriculture” consistently shows very high citation burst in the past and current literature [ 6 ]. Due to its high popularity among researchers the more recently published bibliometrics studies conduct in the realm of sustainable agriculture explore the interactions between sustainable agri-food systems with the economy, society, and policy making [ 7 ], agriculture systems modernization approach [ 6 ], big data in sustainable agriculture [ 8 ]. In terms of Artificial Intelligence in agriculture, recent articles provide bibliometric analysis of the crossover between remote sensing technologies and agriculture [ 9 ], advanced information and communication technology in agriculture [ 10 ], global trends in precision agriculture technology [ 11 ].

In the scope of this review, sustainable agriculture is defined as, the agricultural practices that ensure fulfillment of present day and future food and nutrition requirements of the society, while maximizing the net advantage towards the ecosystem, society, and earth when all its implication on costs and benefits are monitored [ 12 ]. Artificial intelligence (AI) was first coined by John McCarthy in 1956 and then many different definitions arise over the years but in the scope of this study, it is defined in the rational approach as a system that automates intelligent behaviour or acquires intelligence over time using computational programming and gives rational outputs to perform specific tasks without much human intervention [ 13 ].

The impact of the covid-19 pandemic on the agriculture sector is detrimental. There were various studies conducted in the past that observed the disruptions in the agriculture sector and provided sustainable solutions by employing AI technologies in creating sustainable agriculture. However, there is a dearth of studies that provided a state-of-the-art review of AI technologies application in sustainable agriculture. Therefore, this study was conducted to identify the current stage of knowledge concerning AI and sustainable agriculture and provided bibliometric and network analysis in this field. Also, this bibliometric research is motivated to bring together the highlights of the progress made in the application of AI in sustainable agriculture practices to inspire future applications of AI in this field. Hence, it is imperative for future researchers to reflect upon the past decade for academic literature in the field to be able to innovate according to the demands of the current pandemic situation [ 2 ]. This study attempts to compile, analyze, and identify the properties of the scholarly articles and reviews indexed in the Scopus database specifically for the keywords- artificial intelligence, Sustainable agriculture made available between the duration of 2000 to 2021. This study involves both quantifying and bringing out qualitative inferences from the size and features of previously published scholarly works from one or more source databases such as Scopus indexed database. The qualitative questions we intend to explore in the scope of this study are as follows:

• What is the number of scientific articles and reviews published between the year 2000–2021 in the scope of AI and sustainable agriculture?
• What is the growth trajectory in the number of works published in the area of AI and Sustainable agriculture?
• What is the geographic distribution of the research work produced around the world in the field of study?
• Which are the prominent journals and who are the eminent authors involved in the research of AI and sustainable agriculture?

Following this introduction, this article is structured in the following manner: in section 2 the materials and methods used are elaborated. Further, in section 2.1 the bibliometric analysis approach is explained, followed by section 3 which provides an overview and results, and finally, the article closes with sections 4, 5, and 6 providing a discussion about future research options, implications in the present and conclusion respectively.

2. Research methodology

The approach taken to conduct this study is a comprehensive bibliometric analysis of the previously published works of literature that incorporate the usage of AI in the field of sustainable agriculture between the duration of 2000 and 2021. A total of 637 articles were extracted from the Scopus database as a CVS file, out of which only 465 relevant journal articles and review papers were considered for maintaining the veracity of the resulting conclusions. Scopus as a source index, is highly regarded amongst academicians and researchers for searching legitimate scientific articles as it facilitates searching and extraction of specific keywords from the titles, citations, abstracts or keywords from the publications listed in the database [ 14 ].

Bibliometric analysis has been regarded as a reliable method to perform quantitative and empirical study and compilation of previously published works of literature in any field [ 15 ]. Pritchard in 1969 first vaguely defined bibliometrics as analyzing books, written documents, article or media communication by application of statistical and mathematical tools [ 16 ]. Broadus then in 1987 defined bibliometrics as “the quantitative study of physical published units, or of bibliographic units, or of the surrogates for either [ 17 ]". In the scope of this study it encompasses analysis methods such as citation network analysis, geographic network analysis, prominent countries and authors ranked and prominent word cloud. These tools provide a visual representation of the development of literature in the specified field over the course of time and highlight the impactful trending areas of research in the duration selected [ 18 ].

Visualization of the networks established from the large number of articles extracted is a key step in the bibliometric process which was done using multidimensional scaling there are various software tools available in the market like R package, iGraph package, VOSviewer and Biblioshiny [ 19 ]. Due to constant innovation and increased accessibility of advanced web-based and electronic bibliographic and referencing applications, the outputs from bibliometric analysis have greatly improved in quality [ 20 ]. Within the scope of this study, VOSviewer has been used to generate and visually represent the network of authors and countries as the VOSviewer platform provides the most suitable options for easily displaying the bibliometric maps and is easily understandable by any type of audience. Biblioshiny is used in the study to generate the prominent keyword cloud. It is an extension of the Bibliometrix R package web-interface used to visualize the clusters in the database.

2.1 Research method: Bibliometric study

The procedure followed in this study was standard comprehensive bibliometric analysis which starts from keyword search in the Scopus search syntax as shown in the first step of Fig 1 . The keywords indicate that articles and publications containing “Artificial Intelligence” or “Machine Learning” or “Robotics” etc. AND “Sustainable Agriculture” in their titles, keywords, or abstracts. The search was run, and the total number of articles obtained within the timespan of 2000 to 2021 and before any type of filtration was 637. The output was then refined for any redundancies. Thereafter, only journal articles, review papers, selecting only English papers, and within year 2000–2021 were considered for the study, bringing the total number of articles to 465.

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https://doi.org/10.1371/journal.pone.0268989.g001

This total consists of about 85.8% journal articles and 14.2% review papers. Further, about 46.6% of the publications were made during or after the onset of the Covid-19 pandemic. This set of indexes was then exported to Excel, VOSviewer, and Biblioshiny to obtain the network analysis, assessment of the growth trajectory, geographic distribution, identification of prominent authors, and keyword assessment. The ranking of country-wise publication of articles and citations were determined using Microsoft Excel features.

3. Results and overview

The Table 1 describes the important properties of the data set used for the study which helps in determining the overall scholarly or academic impact that has been made in the field of AI utilized in sustainable agriculture in the duration of 2000 to 2021. The total size of the literary works extracted for the study is 465. One of the most notable properties of this data set is that out of a total of 2005 authors who publish research in this field only 15 of them have produced single author documents. When compared to bibliometric analysis in other popular fields of academic study, these statistics have lower numbers which indicate that there is a need for more academic research and publication to provide knowledge and encourage the use of AI widely.

https://doi.org/10.1371/journal.pone.0268989.t001

3.1 Year-wise article statistics

Further, Fig 2 is used to visually depict the growth trajectory and shift of research interest in the use of AI in sustainable agriculture. The academic literature in this discipline had steady but moderate growth from the year 2000 to 2015. After which evidently, there was a greater impact Industry 4.0 had on sustainable agriculture practices and hence we can see the trend gradually increasing since 2017 and we see an approximately 255.7% increase in Scopus indexed articles and review published from 2019 to 2020 and mid 2021. This growth confirms a surge in scholarly inclination towards addressing the uses of AI in sustainable agriculture practices.

https://doi.org/10.1371/journal.pone.0268989.g002

3.2 Country wise article statistics

In terms of the geographic distribution of the literary works, as per Table 2 about 54.8% of the publications are produced from China ranking it at number 1 out of top 20. The next countries leading knowledge production in use of AI in sustainable agriculture are USA, India, Iran, and France. The rest of the list also comprises of highly developed Anglo-American-European nations such as, Italy, UK, Germany, Spain, Australia, Netherlands, Turkey, Canada, Switzerland and Portugal, and other developing Asian economies such as Malaysia, Indonesia and Pakistan.

https://doi.org/10.1371/journal.pone.0268989.t002

3.3 Country wise citation statistics

The country wise citation ranking in Table 3 , shows USA and China received a maximum number of citations 1938 and 1141, respectively. Then, the following countries with a considerable number of received citations are UK, Netherlands, and Germany. Whereas, Algeria shows the highest average article citation at 68.

https://doi.org/10.1371/journal.pone.0268989.t003

3.4 Ranking of key journals in the selected discipline

It is important for promising scholars to identify the role of prominent journals which are facilitating knowledge dissemination in the discipline of the use of AI in sustainable agriculture so that they can approach the industry leaders and foster further innovation and research potentials. In our analysis, we identified the top 20 journals that span into a wide variety of research disciplines ( Table 4 ). To highlight a few, Sustainable practices (Sustainability), agricultural economy (Agronomy, Agronomy For Sustainable Development), technology in agriculture (Computers and Electronics in Agriculture-81 papers), environmental research (Science of the Total Environment, Journal of Cleaner Production, Agricultural Water Management), emerging technologies (Remote Sensing, Applied Sciences), Multidisciplinary (IEEE Access), environmental and sustainability studies (Land Use Policy), Environmental Monitoring and Assessment (Agriculture Ecosystems and Environment, Ecological Indicators) and the list includes furthermore areas. This indicates that there is a growing inclination towards cross-disciplinary exploration in attaining AI systems that can help make agriculture more sustainable in the future.

https://doi.org/10.1371/journal.pone.0268989.t004

3.5 Ranking of key authors in the selected discipline

Using biblioshiny package in R-software, we have generated Table 5 , which indicated the most prominent authors publishing in the domain of AI utilization in sustainable agriculture and farming. Based on the filtered database the papers, articles, and reviews are produced by 2005 authors in total. The top published authors come from China, USA, India, Iran and France based on their affiliate universities. The low number of articles even from the top authors suggest that this field of study is still in its emerging stages and increasing authorship and co-authorship has the potential of drawing further interest in this discipline.

https://doi.org/10.1371/journal.pone.0268989.t005

3.6 Ranking of key institutions in the selected discipline

The top ten affiliations by the number of articles produced are shown in Table 6 . It is essential to determine which distinguished organizations and universities are leading the research scenario in the field of AI utilization in sustainable agriculture and related disciplines. According to the output from the analysis, Wageningen University, Netherlands has produced 13 scientific articles in the scope of the topic under study. The following China Agricultural University, and Northwest A&F University are from China, and each have published 12 and 10 scientific articles, respectively.

https://doi.org/10.1371/journal.pone.0268989.t006

3.7 Keyword analysis

The result of the keyword analysis using Biblioshiny shows precisely the various technologies within the scope of artificial intelligence which are being used in sustainable agriculture. These technologies are–machine learning, agricultural robots, neural networks, artificial neural networks, remote sensing, precision agriculture and support vectors machines. In addition to it, Fig 3 provided the major keywords and its occurrence in the selected articles. It is evident apart from the keywords of artificial intelligence and agriculture which are under study, the most projected key words are agricultural robots, decision making, machine learning, remote sensing, artificial neural systems, logic algorithms and other related technologies.

https://doi.org/10.1371/journal.pone.0268989.g003

The projected set of keywords also helps in determining the various thematic areas in which the majority of the articles belong. The popular thematic areas determined are automation of agricultural production using AI, machine learning in the field of food production, modernization of farming methods using technology and weather forecasting solutions for sustainable agriculture.

3.8 Network analysis

The Fig 4 , is a VOSviewer generated network illustration of the most prominent published author collaborations for AI and Sustainable agriculture scientific articles that are indexed in Scopus from 2000–2021. The eight different color clusters represent the different domains and the interaction and collaboration of authors from different domains to produce multi-disciplinary scientific articles. The size of each circle denoted for each author represents the amount of academic literature, citations they have produced in the field of AI and sustainable agriculture respectively.

https://doi.org/10.1371/journal.pone.0268989.g004

Based on the originating country of publication and using VOSviewer software the geographic network distribution is obtained in the form of Fig 5 . The network also reinforces the findings from the bibliometric analysis of the top 20 countries that produce scientific studies in the domain of AI and sustainable agriculture. The different colored clusters represent the various disciplines of study and their interaction with other disciplines in the scope of the study. Further, the circles representing China, the United States, India, the UK, and Germany confirm the earlier findings. On further exploration of Fig 5 , it is observed that the clusters created between the United States of America, European Union member states and United Kingdom are more dense and diverse as compared to the interaction with and amongst Asian countries.

https://doi.org/10.1371/journal.pone.0268989.g005

4. Discussion

AI enhancements have made different sectors of production highly effective in doing so by providing sustainable solutions. Initially, the applications of AI technologies were intended to increase productivity but the trend had begun to gradually shift towards researching for sustainability and green technologies that would automate and reduce resource consumption in the production process. Jha et al. in their review of automation used in agriculture, highlighted that the younger farmers and producers are more inclined towards investing in automation technologies than the older generations [ 21 ]. Over utilization of pesticides and agrochemicals has led previously fertile lands to turn barren, this can be avoided by implementing Artificial Neural Network methods as suggested in the results obtained by Elahi et al. in their 2019 test on rice crops [ 22 ]. Studies have also emerged in utilization of data intelligence automation tools like extreme machine learning in precision farming decision support system to determine the accurate yield of crops of small holder farms of produce such as coffee [ 23 , 24 ].

The aim of this article is to compile and reach various inferences from the bibliometric analysis of the literature published in the duration of 2000–2021. The research is innovative and a novel study trying to explore the AI techniques utilised specifically in sustainable agriculture practices which have not been done before. The resulting research framework is intended to provide a basis for future research in an efficient and organised manner.

4.1 AI in sustainable agriculture

The use of AI in sustainable agriculture has the potential to transform aspects of farming such as image sensing for yield mapping, yield prediction, skilled and unskilled workforce, increasing yield and decision-support for farmers and producers [ 25 ]. Based on a 2019 article by Alreshidi, AI is widely being implemented in the following ways to make agriculture more sustainable, these are- climate monitoring, automatically climate-controlled greenhouses, crop quality monitoring, livestock management, predictive analysis and comprehensive farm management systems are a few [ 26 ]. The AI products that are highly in demand from customers of sustainable agriculture backgrounds are chatbots for help with information on farming practices, digital plant health diagnosis applications, remote sensing instruments and irrigation management solutions [ 27 ].

Klyushin & Tymoshenko, (2021) proposed an optimization approach for drip irrigation system optimization to attain sustainable agriculture by using AI methods [ 28 ]. On the other hand, Zhang et al. (2021b) emphasized on informatics and material science to find sustainable solutions in sustainable agriculture by using nanotechnology and AI [ 29 ]. Aggarwal & Singh (2021) asserted on technology assistance in precision farming and discussed the implications of AI and internet of things (IoT) in agriculture to assess water requirements, humidity, need for fertilizers etc [ 30 ]. Spanaki et al. (2021) addressed the issues concerning food security and proposed an AI technique as a solution by adopting a design science methodology [ 31 ]. Mohr & Kühl (2021) investigated the barriers in AI acceptance in agriculture and applied technology acceptance model [ 32 ]. Nevertheless, Mahto et al. (2021) used artificial neural network (ANN) to forecast prices of agriculture commodities and compared of performance of their model with ARIMA model for sustainable agriculture [ 33 ].

4.2 Machine learning in sustainable agriculture

Machine learning was defined in the 2018 book “Foundations of Machine Learning” as computational processes that utilize historic data and past experiences to modify, improve, repair and predict future performance accurately [ 34 ]. Machine learning in sustainable agriculture latest utilization is in optimizing supply chains [ 35 ], in-field monitoring [ 36 ], soil temperature prediction [ 37 ] and sustainable soil management [ 38 ]. The different types of machine learning technologies that can be implemented to foster sustainable production are decision trees, neural networks, polynomial predictive methods and K-nearest neighbors [ 39 ]. Traditional methods of soil suitability assessments for sustainable agriculture can prove to be expensive and time taking especially in remote areas where data about the properties of the soil is unavailable there machine learning technologies are gaining popularity for large scale land suitability assessments [ 40 ].

Qin et al. in 2018 successfully explored the predictive abilities of machine learning on estimating the economic optimum nitrogen rate for corn crops using data from 47 test conducted throughout the American corn belt in USA and found that more robust data was required to make accurate estimations using machine learning based models [ 41 ]. Liakos et al. in 2018 conducted a review of machine learning in agri-tech and found that the artificial and deep neural network method of machine learning was a popular choice across all categories of agriculture processes but specifically in the categories of livestock management, water management and soil management [ 42 ]. A 2019 comparative study between four different machine learning techniques by Ju et al., concluded that while conducting estimations on corn and soybean yields, Convolutional Neural Network or CNN was the most accurate [ 43 ].

4.3 Robotics in sustainable agriculture

Robotics in agriculture is mainly utilized to speed up repetitive and mundane tasks in the production process like spraying, mowing, seeding, harvesting, weed control, picking and finally in sorting products and packaging. Automation provided by robotics in combination with cloud computing, block-chain and big data has also found utility in supply chain of fresh produce. In the realm of making farming sustainable, field robots are used in precision farming by targeted weed control functions replacing treatment of crops and soil with harmful and excessive chemical sprays [ 44 ]. The concept of Agriculture 5.0 has been gaining momentum as a term used to define the incorporation of artificial intelligence and robotics in data-driven farming systems [ 45 ].

Sarri et al. in 2020 reported the results of SMASH project at its design stages of AgroBot with four modules combined to physically control weeds and protection of crops [ 46 ]. Robotic solutions are not only popular in the research community but also amongst the members of the industry who want to invest and implement these sustainable systems [ 47 ]. With the widespread utility, there is an emergence in new research interest in safe human-robot interactions in agricultural settings. Benos et al., express that due to automation and programmed robots, human safety is a concern and that efforts must be taken to make robots extra sensitive to perceiving human proximity and ensure risk-free work environment [ 48 ].

The article by Linaza et al., from 2021 summarises the recent research projects in European Union on the use of robotics and highlights that the use of robots can not only make it precise but also solve the labour shortages caused rise in the average age of farmers [ 49 ]. Furthermore, Mondejar et al., described that usage of robotics in agriculture can solve major food shortage issues and help achieve United Nations sustainable development goals without depleting non-renewable resources rapidly [ 50 , 51 ]. However, to bring the robotic technologies to the commercial level some barriers that must be overcome are improved speed and accuracy. There is a scarcity of research funding when compared to investment interests in industrial manufacturing and military equipment that’s why the body of study is small and the process from development to implementation is slow and on small scale [ 52 – 54 ].

4.4 Resulting observations made in the use of AI techniques in sustainable agriculture

There are a number of other AI solutions that can be implemented in ensuring sustainability in food production like predictive analytics, decision support systems, genomics tracing, artificial neural networks, fuzzy logic, neuro-fuzzy logic, Bayesian Network, and remote sensing. Studies have suggested that advanced bio-sensing technologies in sustainable agriculture will facilitate early diagnosis of diseases and plant pathogens even in asymptomatic plants hence reducing the loss of crops and production [ 55 ]. Unmanned Arial Vehicles or drones integrated with advanced machine learning help in continuous weed management enabling selection decisions and reducing herbicide diffusion in the environment [ 56 ]. Also, Rasmussen et al., in 2021 studied the effect of engaging unmanned arial vehicles which receive data from satellite imagery to perform weed mapping of a particular variety of weed and the result showed that it provides higher resolution images and makes an individual variety of weed to be detected in the crops [ 57 ].

AI and genomics research can enhance defect detection and improve production gains per unit of time with genetic historical data for making accurate predictions [ 58 – 60 ]. Artificial neural networks and fuzzy logic on the other hand are systems that can be continuously trained in making prediction models for sustainable agriculture [ 21 ]. A recent 2021 stimulation study by Pazouki, devised a system for surface irrigation using fuzzy expert systems and meta-heuristic optimization algorithms which enhanced the regular system and improved its practicality, performance and reduced the requirement for manual labour [ 61 ]. Hence proving the wide application of the system in irrigation and water management for sustainable agriculture.

On the other hand, the application of fuzzy logic was used in another study by Soylu and Çarman in 2021 where they developed an automatic fuzzy-based slip control system for tractors on the field by continuously recording how many times slippage occurred during the process of tilling, which proved to reduce slippage by 42% and reduced fuel consumption by 44% [ 62 ]. A few limitations of the artificial intelligence-based systems were highlighted in the recent 2021 study by Meroni et al., in which they stated that the difference between the accuracy of machine learning enabled systems and regular bench-marking systems were not significantly dispersed and the performance of machine learning systems reduced if the data was small [ 63 ].

Table 7 briefly discussed some recent studies conducted in the selected field of research. Based on previous literature and identified techniques used in providing solutions for sustainable agriculture, a research framework is developed in this study ( Fig 6 ).

https://doi.org/10.1371/journal.pone.0268989.g006

https://doi.org/10.1371/journal.pone.0268989.t007

Based on the results and further propositions from the above stated recent studies, we have attempted to propose a framework for optimizing future research in the utilization of AI technology in sustainable agriculture. We propose that, from Fig 6 depicted below, researchers can create novel topics and thematic areas utilizing the classification of operations performed in agriculture at various stages. Then choose relevant and correlatable AI technologies as in Fig 6 , that have been identified based on existing academic literature and research its application on the distinct processes identified in attaining sustainable agriculture practices.

The suggested framework can also provide a foundation to attract new researchers to the direction of researching this avenue as it is observed that the size of existing scholarly research is relatively very small between 2000–2021. There is an ethical need to motivate further scholastic interest in the development of modern technology usage in the food supply chain to ensure food safety for future generations owing to rapid depletion of resources and global environmental and climate changes.

5. Future research

The potential for future research is vast in the scope of sustainability studies involving advanced technologies and sustainable agriculture as these fields interact frequently in the market’s demand for newer innovations. In terms of bibliometric exploration of previous literary works, further scrutiny could be performed by using sophisticated R bibliometrix programming for functions such as co-occurrence analysis, co-citation analysis, cluster and stream analysis etc. The size of the dataset used for the studies can also be increased to include more technologies such as block chain, Internet of Things, cloud computing, and supply-chain solutions, which are extensively utilized in the agriculture industry. Further studies could also combine scholarly articles and reviews from more than one index source such as Scopus and Web of Science together for extracting a larger dataset. But this kind of dataset will require further steps to clean the dataset from duplications and redundant information.

6. Implications

The implications of the results from this study can be of significant impact as it provides the targeted scholars, readers and researchers with a comprehensive compilation of the body of research conducted previously in the area of sustainable agriculture and AI. The results imply that the interest in the exploration of diverse applications of AI in sustainable agriculture is steadily rising. Distinguished authors from multidisciplinary backgrounds and expertise are inclined towards combining it with other thematic domains around the world.

It also indicates that there is potential to publish more literary and research articles as the body of literature are not as large compared to other bibliometric studies. This article also brings the attention of the readers to this untapped opportunity, and they can get motivated to fill this research gap. The proposed research framework in Fig 6 , also has the potential to further provide a streamlined approach to building pertinent scholarly research topics in this particular thematic area.

7. Conclusion

In our analysis, we classified the keywords and used them to extract 674 relevant articles and reviews which gave us the following results: there is a rising academic interest in the field of AI usage in sustainable agriculture with a drastic improvement from 2019 to 2020. China, the USA and Australia are leaders in producing top works of literature and authors in the domain. These results were obtained by performing data cleansing and classifying the data set for network analysis using functions of Microsoft Excel, VOSviewer, and Biblioshiny. By analysing the results it can be determined that there is huge potential for the application of AI to attain sustainability, especially in predicting the yield, crop protection, climate control, crop genetic control, and produce supply-chain, wherein the prominent researchers and institutions need to collaborate further and form more networks to bring radical progress in the field.

As we proceed to the future, focusing on sustainability will dictate all aspects of life on earth. The first and foremost challenge with climate change will be ensuring food safety and availability for all. Although the green revolution and industrial revolution in the past had exponentially improved food production capacities, these approaches also exerted intolerable pressure on the agricultural lands, natural resources, and the ecosystem, the implications of which are unsustainable and irreversible. The 4th industrial revolution with its hi-tech capabilities promised sustainable alternatives to traditional agriculture practices which can potentially reduce or slow down the depletion of the earth’s resources. As the Covid-19 pandemic shocked and paused the usual shenanigans of every sector for a while, it is a good time to review the body of work in AI and sustainable agriculture before making our way forward.

Supporting information

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Artificial Intelligence (AI) in Agriculture

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Original research article, artificial intelligence in postharvest agriculture: mapping a research agenda.

• 1 Centre for Applied Data Science, University of Johannesburg, Johannesburg, South Africa
• 2 Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg, South Africa
• 3 Department of Applied Information Systems, University of Johannesburg, Johannesburg, South Africa

Introduction: The implementation of artificial intelligence (AI) in postharvest agriculture has significantly improved in recent decades, thanks to extensive scientific research. The study aimed to identify research gaps and hotspots for future research based on keyword co-occurrence and clustering analyses, as well as to discuss the results and highlight the research trends.

Methods: This study analyses research trends in AI application in postharvest agriculture using novel scientometric tools such as the Bibliometrix R package, biblioshiny, and VosViewer. The research analysed 586 published papers on AI application in postharvest agriculture research between 1994 and September 2022, retrieved from the Scopus database.

Results and discussion: The results showed that publications on AI applications in postharvest agriculture research have been increasing for almost 30 years, with significant growth in the subject area in the last decade. China, the USA, and India were found to be the top three most productive countries, accounting for 52.4%, 22%, and 18.6% of the total selected publications, respectively. The analysis also revealed that topics such as the Internet of Things, cold chain logistics, big data, decision-making, and real-time monitoring have low development degrees in the knowledge domain. This study demonstrated increased research on AI applications in postharvest agriculture, aiming to reduce postharvest losses, enhance food nutrition and quality, and mitigate food insecurity. It also provides valuable scientific references on AI applications in postharvest agriculture research for researchers and scholars. By identifying research gaps and hotspots, this study can guide future research in AI applications in postharvest agriculture to further improve the industry.

1. Introduction

Despite various efforts to combat global hunger, food insecurity, and the ability to provide adequate quality, quantity, and safe food for the world’s growing population remain enormous challenges in many countries ( Pawlak and Kołodziejczak, 2020 ; Fadiji et al., 2021 ). With the world’s population projected to exceed 9 billion by 2050, food demand is expected to increase by nearly 100% ( Fukase and Martin, 2020 ; Pawlak and Kołodziejczak, 2020 ). Consequently, to meet future demands, food production and agricultural productivity will have to rise by about 70%, which could double to meet the growing future demand ( Cole et al., 2018 ; Sharma et al., 2020 ). Hence, an integrated and innovative approach to the global effort to ensure sustainable food production and consumption is required. The agricultural sector is critically important in alleviating food insecurity, improving nutrition, and reducing postharvest losses ( Emami et al., 2018 ).

The postharvest stage is the final and most critical in agriculture and requires close attention because time and money have been used to cultivate food products. An ineffective postharvest stage or negligence may result in severe postharvest losses and consequent financial loss ( Prusky, 2011 ). Postharvest loss includes food loss across the food supply chain from harvesting food crops until consumption (regarded as food waste at the consumption level; Kumar and Kalita, 2017 ), broadly categorized as weight loss due to spoilage, quality loss, nutritional loss, seed viability loss, and commercial loss ( Kumar and Kalita, 2017 ). In severe cases, postharvest losses have been estimated to be up to 80% of the total production; in Africa, postharvest losses could be as high as 40%, which is significant given the low agricultural productivity in the region ( Abass et al., 2014 ; Naziri et al., 2014 ; Kumar and Kalita, 2017 ; Minten et al., 2021 ). These losses can potentially reduce the economic value of food crops or make them unsuitable for human consumption, consequently impairing food security and nutrition ( Prusky, 2011 ; Saima et al., 2014 ; Hailu and Derbew, 2015 ; Kumar and Kalita, 2017 ; Singh et al., 2022 ). A promising solution mentioned challenges is incorporating newer technology that increases food production while decreasing postharvest losses is critical to maintaining sustainable living standards and improving food security ( Singh et al., 2022 ).

In postharvest agriculture, artificial intelligence (AI) has set an impeccable record ( Kakani et al., 2020 ; Meshram et al., 2021 ). AI is a general term that includes machine learning (ML), deep learning (DL), and neural networks (NN). Machine learning is an approach to achieving artificial intelligence, while deep learning is a subfield of machine learning that includes convolutional and recurrent neural networks ( Dokic et al., 2020 ). Given its outstanding performance combined with increased application in different sectors, AI has the potential to complement existing approaches and techniques to minimize the massive postharvest losses problem, and postharvest agriculture is one area ripe for disruption with AI implementation. In agriculture, postharvest handling is the stage of crop production immediately following harvest, including cooling, cleaning, sorting, and packing to help reduce the fast deterioration of crops ( El-Ramady et al., 2015 ). In particular, the quality of horticultural products is critical in determining market acceptance and, as a result, directly affecting the storage and postharvest processing operations. There are pieces of evidence on the use of these techniques (AI, ML, and DL) on various agricultural products, especially in detecting defects, classification, sorting or grading, autonomous decision-making, predictive analytics, quality control, etc. For instance, Yang et al. (2022) developed an image recognition system based on AI to sort apple fruit efficiently. Takruri et al. (2020) employed machine learning algorithms to estimate the freshness and quality of apples in terms of age using polarization images. Amoriello et al. (2022) applied artificial neural networks (ANN) to predict the quality parameters of strawberry fruit. In another study, Makkar et al. (2017) analyzed the quality of fruits and vegetables based on color, shape, and size and successfully segmented defective fruit regions using neural networks.

Further, researchers have demonstrated the excellent performance of artificial intelligence in agricultural computer vision applications. A deep neural network binary classifier to detect defects in tomatoes was proposed by da Costa et al. (2020) . In another study, Thinh et al. (2019) classified mango fruit in terms of color, volume, size, shape, and fruit density. The study combined computer vision, image processing, artificial intelligence, and artificial neural networks. Using computer vision and artificial intelligence, Chakraborty et al. (2021) proposed a model to prevent the propagation of rottenness in apple, banana, and orange fruits. The model was capable of classifying fresh and rotting fruits. Another study by Roy et al. (2021) performed a real-time segmentation of rotten apples, which led to the classification of fresh apples from rotten apples by utilizing deep learning architecture. Despite the extensive use of artificial intelligence in classification and defect identification, its application has cut across real-time monitoring of fruit quality, food fraud and authentication, and cold chain logistics ( Tsang et al., 2018 ; Loisel et al., 2021 ).

It is evident, therefore, that research on the application of artificial intelligence in postharvest agriculture has grown significantly in the last decade. There are several excellent recent reviews on the application of artificial intelligence in agriculture, including machine learning applications to monitor food safety ( Wang et al., 2021 ), digitalization and artificial intelligence for sustainable food systems ( Marvin et al., 2022 ), improving food quality using artificial intelligence ( Ben Ayed and Hanana, 2021 ; Sahni et al., 2021 ), Internet of Things, big data, and artificial intelligence in agriculture and food industry ( Kamilaris and Prenafeta-Boldú, 2018 ; Liakos et al., 2018 ; Santos et al., 2019 ; Dokic et al., 2020 ; Misra et al., 2020 ; Ren et al., 2020 ; Bal and Kayaalp, 2021 ; Meshram et al., 2021 ), deep learning approaches in horticulture ( Yang and Xu, 2021 ), fruit detection, recognition, and yield estimation ( Koirala et al., 2019 ; Indira et al., 2021 ), food processing applications ( Zhu et al., 2021 ), to mention a few. These reviews provided good insights into the applications, opportunities, and challenges of artificial intelligence in agriculture, but none evaluated a quantitative structured methodology such as bibliometrics in postharvest agriculture. Bibliometrics employs mathematical and statistical methods to assess a specific knowledge domain’s current state and future direction. Therefore, this study aimed to utilize bibliometric analysis to provide a comprehensive insight into artificial intelligence in postharvest agriculture research.

2. Methodology

2.1. bibliometric method and data collection.

Bibliometrics has evolved into an independent discipline over the years. This study uses mathematics, statistics, and logic to organize and analyze aspects of literary works ( Rons, 2018 ; Wang et al., 2021 ). In addition, critical decisions regarding expert and specialized issues are made using bibliometric analysis or methods, as it allows for monitoring scientific developments using various indicators, such as influential authors, journals, countries, academic affiliations, and research collaborations ( Rons, 2018 ). Consequently, providing essential data for researchers to investigate current and future research trends ( Benavides-Velasco et al., 2013 ; Albort-Morant and Ribeiro-Soriano, 2016 ; Rey-Martí et al., 2016 ; Rons, 2018 ; Cucino et al., 2021 ; de Castro et al., 2021 ). Ultimately, the bibliometric method ensures that research is presented transparently, objectively, and methodically ( Donthu et al., 2021 ; Rejeb et al., 2021 ).

The present research data were collected and saved from the Scopus database, particularly on the 22nd of September 2022 at about 5:40 p.m., spanning 28 years from 1994 to 2022. Scopus is a world-renowned repository known for its comprehensive coverage and dependable content. It contains several publications published in journals from major and reputable publishers such as Elsevier, EmeraldInsight, Springer, Multidisciplinary Digital Publishing Institute (MDPI), and Taylor & Francis ( Maflahi and Thelwall, 2016 ; Rejeb et al., 2021 ). The following search string with the “OR” and “AND” operators were used: [(“food quality” OR “food grad*” OR “food trac*” OR “food loss” OR “food waste” OR “food deteriorat*” OR “food discriminat*” OR postharvest OR post-harvest OR “cold chain” OR cold-chain OR coldchain OR “cold supply chain” OR agroprocessing OR agro-processing) AND (“machine learning” OR “artificial intelligence” OR “data mining” OR “data science” OR “deep learning” OR “Big data” OR “Real-time monitoring” OR “Transfer learning”)]. The search was performed in the title, abstract, and keyword fields. The titles and abstracts of these articles were screened, excluding all publications with missing bibliometric data (e.g., abstracts, keywords) and according to the subject area. We selected all languages to give an idea of the language distribution of published documents, resulting in 586 documents. The article selection process ensured that the chosen articles aligned with our research scope. This was achieved through manual exclusion, resulting in a focused and accurate representation of the literature that closely aligns with the search string used.

Figures 1A , B depict the proportion of documents by language and type, respectively. Among these publications, according to language category, English accounted for 95.90%, followed by Chinese (3.07%), Spanish (0.51%), French (0.17%), German (0.17%), and Japanese (0.17%; Figure 1A ).

Figure 1 . (A) The proportion of document distribution by language and (B) the proportion of document distribution by type.

The percentage proportions of the documents according to types are as follows: articles (57.68%), conference papers (30.20%), review (8.36%), book chapters (2.73%), editorial (0.34%), note (0.34%), book (0.17%), and letter (0.17%; Figure 1B ). Figure 2 depicts a summary of the main information of the analyzed data.

Figure 2 . Illustration of the main information of the analyzed data.

Supplementary Table S1 shows the distribution of the patents obtained using the exact search string.

2.2. Data analysis

The raw data from the Scopus search of 586 documents were saved in BibTeX and CSV formats for further analysis. The data were analyzed using bibliometric software, specifically the VosViewer and the “Bibliometrix” package software ( Van Eck and Waltman, 2010 ; Aria and Cuccurullo, 2017 ; Sganzerla et al., 2021 ). The authors conducted a keyword co-occurrence network analysis to understand the current topic thoroughly. The VosViewer software generated maps based on the main keywords, authors, and their relationships. Furthermore, the Bibliometrix was used to illustrate the documents’ scientific trends and productivity, including the most productive authors and most significant articles published.

3. Results and discussion

3.1. results of the descriptive statistics, 3.1.1. publication by year.

Figure 3 shows the annual distribution of the total (586) scientific documents depicting a growing trend since 1994, with a yearly growth rate of 17.79%. The figure is divided into two parts: the initial period and the rapid-growth period. In the initial period (1994–2009), the literature appears limited, where the maximum annual publication did not exceed two documents. Conversely, the rapid-growth period (2009–2022) showed an exponential increase in the yearly publication on artificial intelligence in postharvest agriculture, with 178 documents observed in 2021. At the time of this study, in 2022, the number of published papers was 54% of those published in 2021. This observation indicates rapid and significant growth in the subject area. Figure 4 illustrates the annual average of document citations. It was discovered that 2008 had the highest average number of document citations. This finding corroborates those made for 2009, the start of the rapid-growth era, in the publications by year depicted in Figure 3 .

Figure 3 . Yearly distribution of scientific publications on AI applications in postharvest agriculture research.

Figure 4 . Yearly average citations of scientific publications on AI applications in postharvest agriculture research.

3.1.2. Publication by country

Sixty-nine countries (or regions) were represented in the data on artificial intelligence in postharvest agriculture research from 1994 to 2022. Figures 5A , B show the publication distribution according to country. Figure 5A depicts the distribution of scientific production in all 69 countries, with the deepest blue color indicating the country with the highest number of publications. As can be seen, a high concentration of publications occurs mainly in developed countries. Figure 5B shows the occurrence of the number of publications for the top 20 countries. Among these countries, China has the highest number of publications with 307 documents, which was followed by the USA (129), India (109), Spain (88), Brazil (65), UK (58), Italy (51), Iran (47), South Korea (41), and Australia (35). Similar observations were reported by Dokic et al. (2020) , showing China as a dominating country in production in their study on applying machine learning, neural networks, and deep learning in agriculture. The composition of the top countries shows a mix of developing and developed countries publishing in the research area.

Figure 5 . The distribution of the scientific publications on AI applications in postharvest agriculture research by country. (A) World map distribution on the scientific publications, with the deepest blue colour representing the higher number of publications; (B) Bar chart showing the top 20 scientific publications by country.

From the perspective of citations according to countries, the top 20 countries are shown in Figure 6 . It was observed that China (1,040), Spain (877), USA (357), Iran (253), and South Korea (252) were the top five most cited countries. For both countries’ scientific production and most cited countries, China appeared in the leading position, indicating that China is the fastest-growing country in AI applications in postharvest agriculture research. Furthermore, results revealed that articles published in China were of high quantity.

Figure 6 . The most cited countries of scientific publications on AI applications in postharvest agriculture research.

Figure 7 shows the top 20 countries in terms of publications based on the corresponding author. SCP represents the intra-country publication, while MCP represents the inter-county publication. Generally, for all the top 20 countries, when the nationality of the corresponding author was considered, SCP was higher than MCP. Additionally, China topped for combined SCP and MCP publications (102), followed by USA (32), India (29), Spain (29), and Brazil (21). The ratio of SCP to total publications in the top 20 countries was ~20%–90%, while MCP to total publications was ~10%–80% ( Table 1 ). From Table 1 , Brazil and Ireland have the highest percentage SCP and MCP ratios to total publications of 90% and 80%, respectively. This observation indicated that Ireland strongly preferred international cooperation or collaboration concerning research in AI applications in postharvest agriculture. Furthermore, some countries have similar % SCP and MCP ratios to total publications. For instance, the % SCP and MCP ratios to total publications for India and South were 76% and 24%; Italy, Japan, and Malaysia (56% and 44%), Hong Kong and Turkey (67% and 33%); Germany and Netherlands (60% and 40%), respectively. Interestingly, Greece has the same % SCP and MCP ratios to total publications of 50%. Notably, most countries showed more tendencies for intra-country collaborations than inter-country collaborations regarding research in AI applications in postharvest agriculture.

Figure 7 . Corresponding author’s countries. SCP and MCP represent intra-country and inter-country collaborations or country publications and multiple-country publications, respectively.

Table 1 . Top 20 countries of publication based on the corresponding author.

3.1.3. Publication by institutions

Figure 8 illustrates the top 20 academic institutions contributing the most to AI applications in postharvest agriculture research. Zhejiang University, located in China, had the highest number of publications, with 19 documents, followed by China Agricultural University, with 18 publications, and the University of Kentucky (USA), with 14 publications. These results corroborated the observations on the scientific publications by country shown in Figure 5 , where China was the top country regarding scientific publications, followed by the USA. Additionally, out of the top 20 academic affiliations, 20 and 15% of the institutions were from China and USA, respectively.

Figure 8 . The most relevant affiliations of scientific publications on AI applications in postharvest agriculture research.

3.1.4. Publication by journals (top 10)

The top 10 relevant sources (journals) publishing articles on AI applications in postharvest agriculture are shown in Figure 9 . Generally, it was observed that the top 10 journals published 110 documents, accounting for approximately 19% of the total sampled 586 papers. From Figure 9 , Computers and Electronics in Agriculture rank first on the list, with 29 documents corresponding to about 26% of the documents published by the top 10 journals. This was followed closely by Food Control with 13 published documents and Postharvest Biology and Technology with 12 documents. The highest number of publications by Computers and Electronics in Agriculture may be attributed to the scope of the journal, which provides international coverage of advances in the development and application of computer hardware, software, electronic instrumentation, and control systems for solving problems in agriculture with specific emphasis on postharvest agriculture. Additionally, the journal covers relevant technology areas, including intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Furthermore, given the distribution of the top 10 journals, the scope of the journals either falls within the technology application or the evaluation of food quality. Among the top 10 journals, the lowest number of published documents ( n  = 7) appeared in Food Chemistry , Food Analytical Methods, and ACM International Conference Proceedings Series .

Figure 9 . The top 10 relevant sources of scientific publications on AI applications in postharvest agriculture research.

Figure 10 shows the impact of the relevant sources measured according to the H-index. Computers and Electronics in Agriculture and Food Control , the top 2 in the number of publications, top the list with an H-index of 13 and 8, respectively. Following closely, Meat Science and Sensors each has an H-index of 7, and Postharvest Biology and Technology with an H-index of 6. Although some journals appeared in the top 10 according to the source impact and were not in the top 10 in the most relevant sources, such as Computers and Electronics in Agriculture , Food Control , Postharvest Biology and Technology , Sensors , Sustainability , and Food Analytical Methods appeared in both.

Figure 10 . The source impact of scientific publications on AI applications in postharvest agriculture research.

3.1.5. Most productive authors (top 10)

From 1994 to 2022, 2,160 participated in AI applications in postharvest agriculture research, with only 24 authors of single-authored documents, constituting 1.1%. The top 10 most prolific authors are shown in Figure 11 . It can be noted that Zhang X and Zhang Z are the most productive authors, with an equal number of 9 publications. Zhang X’s 2016 study summarized current methods and technologies in stream data mining with applications in Internet of Things systems for supporting fruit cold chain logistics ( Juric et al., 2016 ). Through real-time temperature monitoring using a Wireless Sensor Network (WSN) and correlation analysis of the various quality indicators, Zhang X’s study from 2017 identified the essential quality parameter(s) in the cold chain logistics of table grapes ( Xiao et al., 2017 ). This was followed closely by Wang J and Wang X, with 8 publications each. Other productive authors include Li Y, Li Z, Wang Z, and Zhang C, each with 7 published documents, and the bottom two authors with 6 published papers were Cancilla JC and He Y.

Figure 11 . The most relevant authors of scientific publications on AI applications in postharvest agriculture research.

Figure 12 shows a graph of the top 10 author’s productivity over time, and it calculates and plots the author’s production (in terms of the number of publications and impact) over time. The graph represents a measure of an author’s relevance over time based on productivity and impact in a subject area ( Forliano et al., 2021 ). These metrics provide an overview of the top 10 most productive authors over the last 16 years (2006–2022). The number of articles published by an author in a given period was used to determine productivity. At the same time, the impact was assessed based on the number of citations received each year.

Figure 12 . Authors’ productivity over time from 2006 to 2022.

The intensity of the color in Figure 12 is proportional to the year of the citation, and the size of the bubbles represents the various authors’ annual output. For instance, Zhang X had approximately 2.73 total citations per year in 2012 (1 document published) and published 1, 3, and 4 articles in 2016, 2017, and 2019, respectively. Before the last decade, only two authors, Li Y and Zhang Z, with 1 article each published in AI applications in postharvest research in 2006 and 2010, respectively ( Figure 12 ). The study by Li Y in 2006 applied data mining for early warning in food supply networks ( Li et al., 2006 ). In 2010, the work by Zhang Z developed a novel system solution for global fresh food tracking services ( Pang et al., 2010 ). As shown in Figure 12 , most are emerging authors with more active publications observed from 2018 to date. Besides, most authors achieved a higher scientific production in 2021. From Figure 12 , five authors had over 15 total citations per year. Wang J had 17 total citations per year in 2020 and 2021; Wang X had approximately 17 in 2021; Zhang C had 28.5 in 2019; Cancilla JC had 18 in 2020; He Y had 29 in 2019.

Supplementary Table S2 shows the 10 most relevant author impacts ordered by h_index. Three measures are provided concerning the local dataset and the top 10 most productive authors: the times cited (TC), the h-index (h_index), the g-index (g_index), and the m-index (m_index). The h-index measures quantity with quality by comparing publications to citations. It is a metric for assessing the overall impact of an author’s scholarly output and performance. The distribution of citations received by the publications of a particular researcher is used to calculate the g-index, which gives more weight to highly cited articles. The last impact measure, the m_index, is another variant of the h_index that displays the h-index per year since its first publication. The m-index is the h_index divided by the number of years a scientist has been active ( Hirsch, 2007 ; Forliano et al., 2021 ). The most cited authors in the dataset are Blasco J (269 citations) and He Y (138 citations), closely followed by Alfian G and Rhee J (128 citations). Zhang X, who was top in the author productivity over time ( Figure 12 ), has 96 citations, h_index, g_index, and m_index of 5, 6, and 0.455, respectively. It is worth mentioning that Cancilla JC and Torrecilla JS, with publication starting year in 2020, both have the highest h_index, g_index, and m_index of 5, 6, and 1.667.

3.2. Top 20 most-cited documents

Table 2 shows the top 20 most cited documents in AI applications in postharvest agriculture based on Scopus’s data analyzed, with total citations ranging from 52 to 146. Ruiz-Garcia et al. (2008) , Tsugawa et al. (2011) , Lorente et al. (2013) , Jiménez-Carvelo et al. (2019) , Zhou et al. (2019) , and Zhai et al. (2020) have ranked top with total citations above 100. Ruiz-Garcia et al. (2008) , Tsugawa et al. (2011) , Zhou et al. (2019) , and Zhai et al. (2020) received 146, 137, 134, and 126 citations, respectively. Both Jiménez-Carvelo et al. (2019) and Lorente et al. (2013) received 105 citations.

Table 2 . Top 20 most cited documents.

The work by Tsugawa et al. (2011) developed a data mining system to obtain metabolite information on Japanese green tea quickly and easily by using two mathematical methods: (1) Pearson’s product–moment correlation coefficient for identification based on retention time and weighted mass spectrum and (2) Soft Independent Modeling of Class Analogy (SIMCA), a supervised classification technique that is based on principal component analysis (PCA) for the annotation of unknown peaks. The developed data analysis tool could provide essential metabolite information accurately and rapidly and offer new insights into food quality evaluation and predictions. Zhai et al. (2020) presented a state-of-the-art review of decision support systems by exploring the upcoming challenges of their utilization in Agriculture 4.0. The authors systematically analyzed the decision support systems from the aspects of interoperability, scalability, accessibility, usability, uncertainty and dynamic factors, re-planning, expert knowledge, and analysis of historical information. Ruiz-Garcia et al. (2008) explored the potential of wireless sensor technology (ZigBee) for monitoring fruit storage and transport conditions. A comprehensive review of the application of deep learning in food was provided by Zhou et al. (2019) , a first in the food domain. An in-depth discussion on deep learning and its ability as a data analysis tool to solve various problems and challenges encountered in the food domain was presented. These challenges include food recognition, calorie estimation, quality detection of fruits, vegetables, meat, and aquatic products, food supply chain, and food contamination. The authors concluded that deep learning had more outstanding performance capabilities than other methods, such as manual feature extractors, conventional machine learning algorithms, and serves as a promising tool in food quality and safety inspection.

The assurance of food authenticity is the primary concern of many consumers and manufacturers of high-quality products and official bodies and authorities in response to the need to protect consumers by detecting potential food fraud. Hence, Jiménez-Carvelo et al. (2019) reviewed alternative data mining/machine learning methods for evaluating food quality and authenticity (food analytics). Here, various methods such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modeling by class analogy (SIMCA), k-nearest neighbors (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS) were identified as the most widely used techniques. Nonetheless, other methods, including support vector machine (SVM), classification and regression tree (CART), and random forest (RF), show a great potential than conventional ones. Automatic detection of fungal infections in fruits and vegetables, particularly during postharvest operations such as storage, handling, and transportation, is critical because only a few infected produce can spread the infection. The study by Lorente et al. (2013) proposed a methodology to select features in multi-class classification problems using the receiver operating characteristic (ROC) curve to detect rottenness in citrus fruits through hyperspectral images. The authors aimed to distinguish fruit with decay symptoms from sound fruit with minor defects.

In summary, It can be observed in Table 2 (top 20 most cited documents) that the studies focused on the following broad areas: real-time monitoring and decision support system for perishable products, food quality evaluation, and food classification (grading and sorting). These areas are broadly discussed in section 3.4.

3.3. Co-occurrence network analysis

3.3.1. co-occurrence—all keywords.

The keyword co-occurrence network analysis helps researchers detect the literature’s core content and depict the knowledge’s structure. It is used to identify “keywords” that co-occur in at least two publications in a period ( Rejeb et al., 2021 ; Zhong et al., 2021 ). This scientometric method helps to generate clusters that provide a broader view of different research foci in a specific knowledge domain ( Rejeb et al., 2021 ). The keyword is a significant part of scholarly publications, which can play a vital role in information retrieval and research. In this study, using VosViewer to analyze all keywords (full counting method), we selected the minimum number of keywords occurrence as 5, of which 337 met the thresholds out of the 4,979 keywords. Figure 13 shows the co-occurrence network of all keywords in AI applications in postharvest agriculture research, while the occurrences and link strengths of all keywords are shown in Supplementary Table S3 . Total link strength (TLS) represents the collaboration intensity of keywords. The link strength between the circles reflects the frequency of the keyword’s co-occurrence. The total link strength is the sum of the link strengths of the keyword over all the other keywords ( Guo et al., 2019 ; Martynov et al., 2020 ). As shown in Figure 13 , we identified five clusters of words with distinct colors. The nodes in the figure indicate a keyword, and the node size corresponds to the co-occurrence frequency of the keyword or the number of publications with the corresponding keywords. The distance between two keywords in the visualization is determined by density, and the higher this density, the closer the distance between the nodes ( Rejeb et al., 2021 ).

Figure 13 . All keywords co-occurrence network.

The red cluster (cluster 1, with 123 items) has artificial intelligence as the highest occurring keyword. Cluster 2, represented by green, contained 86 items, with food quality as the highest occurring keyword. Clusters 3 and 4, represented by blue and yellow, have 67 and 60 items, with machine learning and deep learning as the highest occurring keywords, respectively. Finally, cluster 5, represented by purple color, has only one item (trees) as the only occurring keyword in the cluster.

Supplementary Table S3 shows the top 10 keywords in the co-occurrence all-keyword analysis. As can be seen, the highest occurring keywords in clusters 1 to 4 appeared in the top 10. Machine learning had the highest occurrence (178), which was followed by food quality (146), artificial intelligence (100), and deep learning (94). Other keywords in the top 10 include quality control (71), learning systems (67), food safety (64), fruits (64), learning algorithms (59), and data mining (59). Additionally, it was observed that the total link strength was independent of the keyword occurrences ( Supplementary Table S3 ). Machine learning and data mining had the highest and lowest total link strength, respectively. The total link strength indicates the number of publications in which two keywords occur together.

3.3.2. Co-occurrence—author keywords

Author keywords are those provided by the original authors. Here, we selected the minimum number of keyword occurrences as 5, of which 67 met the thresholds out of 1,653. Figure 14 shows the co-occurrence network of author keywords in AI applications in postharvest agriculture research. The co-occurrence network has 6 clusters represented with distinct colors. Specifically, as was shown in the red cluster (cluster 1, 16 items), keywords such as machine learning, deep learning, random forest, support vector machine, feature extraction, food processing, pattern recognition, etc., are evidently related to the topic of “AI algorithms in food processing.” In this cluster, machine learning was the highest occurring keyword. The green color represents cluster 2 (15 items) has artificial intelligence as the highest occurring keyword. Other keywords in this cluster are cold chain, cold chain logistics, real-time monitoring, wireless sensor network, etc. Cluster 3 was represented in blue and contained 11 items with keywords such as big data, food safety, food security, sustainability, traceability, industry 4.0, etc. The highest keyword in this cluster was food safety, which may be attributed to the topic of “food safety and sustainability.” Clusters 4 and 5, represented by yellow and purple colors, each have 10 items, respectively. Cluster 4 contained keywords such as food, feature selection, and food quality (highest occurring keyword). The keywords in cluster 5 include agriculture, precision agriculture, sensors, smart farming, food waste, etc. In this cluster, food waste was the highest occurring keyword. Finally, Cluster 6 has 5 items and contains keywords such as adulteration, food fraud, transfer learning, and convolution neural network (highest occurring keyword).

Figure 14 . Author keywords co-occurrence network.

Supplementary Table S4 shows the top 10 keywords in the co-occurrence author-keyword analysis. Machine learning, artificial intelligence, deep learning, food quality, food safety, and convolution neural network were the highest occurring keywords in clusters 1, 2, 3, 4, 5, and 6, respectively, appearing in the top 10 author keywords occurrences. Like the all-keywords analysis, machine learning had the highest occurrence (116) in the author-keyword analysis. This was followed by deep learning (63), food quality (43), artificial intelligence (28), computer vision (26), classification (24), food safety (24), image processing (22), big data (19), and convolutional neural network (19). From Supplementary Table S4 , Machine learning and big data had the highest and lowest total link strength, respectively. The total link strength indicates the number of publications in which two keywords occur together.

3.3.3. Co-occurrence—index keywords

Scopus selects index keywords, standardized to vocabularies derived from thesauri owned or licensed by Elsevier. Index keywords, as opposed to Author keywords, consider synonyms, alternate spellings, and plurals ( Golub et al., 2020 ). For the index keywords, we selected the minimum number of keywords occurrence as 5, of which 302 met the thresholds out of the 4,014 keywords. The co-occurrence network of index keywords in AI applications in postharvest agriculture research is shown in Figure 15 . The co-occurrence network contains five clusters. Cluster 1, represented in red, contains 98 items with keywords such as artificial intelligence, data mining, Internet of Things, supply chain, cold chain logistics, food supply, and food waste. In this cluster, artificial intelligence was the highest occurring keyword and part of the top 10 index keywords occurrences ( Supplementary Table S5 ). The green cluster represents the second cluster (72 items) and has deep learning as the highest occurring keyword. Other keywords in this cluster include learning systems, learning algorithms, neural networks, computer vision, image processing, support vector machines, decision trees, and fruits. Cluster 3, with 60 items in blue, has machine learning as the keyword with the highest occurrence. Other keywords in cluster 3 are quality control, artificial neural networks, and fruits. The yellow color represents cluster 4 (59 items) containing keywords such as food quality, metabolism, metabolomics, and algorithms. In this cluster, the occurrence of food quality was highest. The last cluster (cluster 5, 13 items), represented by purple, has forecasting as the highest occurring keyword. Other keywords in cluster 5 include vegetables and predictive analytics.

Figure 15 . Index keywords co-occurrence network.

Supplementary Table S5 shows the top 10 keywords in the co-occurrence index-keyword analysis. As can be seen, the highest occurring keywords in clusters 1 to 4, except cluster 5, appeared in the top 10. Food quality had the highest occurrence (130), which was closely followed by machine learning (101), artificial intelligence (80.), and deep learning (72). Other keywords in the top 10 include quality control (70), learning systems (67), fruits (64), food safety (59), learning algorithms (58), and data mining (57). Here, food quality had the highest total link strength in contrast to the analyses of all keywords and author keywords, where machine learning had the highest total link strength ( Supplementary Tables S3 , S4 ). Furthermore, it was observed that the total link strength was independent of the keyword occurrences ( Supplementary Table S5 ).

3.3.4. Thematic map and thematic evolution

Figure 16 shows the thematic map of the keyword plus the dataset investigated in this study. The map is divided into four quadrants: Niche themes, Motor themes, Basic themes, and Emerging themes based on relevance degree (centrality) and development degree (density). In the analysis, the number of words was set as 1,000, the minimum cluster frequency (per thousand documents) was set as 5, the number of labels was set as 5, and the clustering algorithm used was the “Walktrap” algorithm. Based on Figure 16 , more emphasis should be placed on the topic in the upper right quadrant based on its density and relevance. Here, it was observed that topics such as food quality, machine learning, deep learning, quality control, and learning systems have high density and relevance and should be examined and researched in-depth. The topics in the upper left quadrant have a high development degree and low centrality (i.e., relevance). The basic theme quadrant (lower right quadrant) contains topics such as artificial intelligence, food safety, data mining, food supply, and food waste; these topics have relatively high development and relevance degrees. Although the trend topics in the upper right quadrant are the most promising topics for future research, the topics in the lower right quadrant (basic themes) also have favorable research prospects and are worth investigating. Furthermore, the lower left quadrant contains topics such as the Internet of Things, cold chain logistics, big data, decision making, and real time monitoring. These topics have low development degree and relevance degree.

Figure 16 . Thematic map of the keyword plus.

To show the historical facets of research shift to direct prospects, thematic evolution has become an effective technique in bibliometrix analysis ( Moral-Munoz et al., 2018 ; Chansanam and Li, 2022 ). This technique highlights the most significant research themes and charts theme evolution through time, offering insight into the field’s future path ( Chen et al., 2019 ; Chansanam and Li, 2022 ). Figure 17 depicts the thematic evolution of the keyword plus based on the dataset from 1994 to 2022. Two periods were chosen as cut-off points: 2008 and 2009. These cut-off points were based on the publication results by year ( Figure 3 ), where we observed a significant increase in the number of publications in 2009, which has continued to increase. In the first period (1994–2008), the popular keywords were food processing and artificial intelligence, merged in the next period (2009–2022) as food quality, deep learning, and artificial intelligence. It was observed that the term artificial intelligence in the first period (1994–2008) is separated into two branches in the second period (2009–2022): food quality and artificial intelligence. In comparison, food processing in the first period is categorized into two in the second period: food quality and deep learning. This trend demonstrates that food quality is a core topic in the second period.

Figure 17 . Thematic evolution of the keyword plus.

3.3.5. Co-authorship—countries

Figure 18 shows the co-authorship-countries network. Here, we selected the maximum number of countries per document as 25 and the minimum number of documents of a country as 5, of which out of 79 countries, 38 met the threshold. The network contains seven clusters. Cluster 1, represented with the orange color, contains 10 items, with Iran having the highest occurrence. The second and third clusters (clusters 2 and 3) have 7 and 5 items, represented by the colors green and blue, respectively. In clusters 2 and 3, the countries with the highest occurrence were Italy and Spain, respectively. Clusters 4 and 5 (represented with pink and purple) contain 5 items, with the United States and China as the countries with the highest occurrences, respectively. In cluster 6 (light blue color), the United Kingdom occurred the highest, and the cluster contains 5 items. The last cluster (cluster 7), represented in yellow color contains 2 items, has South Korea occurring the highest, followed by Brazil.

Figure 18 . Network of the co-authorship countries.

In Figure 18 , countries are indicated by a label and by a circle. The more critical a country, the larger its label and its circle. The size of each circle shows the number of papers written by authors from the country. Each link between two circles of different countries indicates a co-authorship between the organizations in those countries. Hence, more papers have been written by authors from China, closely followed by the United States, India, and Spain. This observation corroborates the results shown in Figure 5 . It is with mentioning that the United States network links with all the clusters.

3.4. Identification of a research agenda (opportunities and gaps) in postharvest agriculture

The postharvest stage is the final and most critical agricultural stage that needs extensive attention ( Meshram et al., 2021 ). After completing all stages, from preharvesting to harvesting, negligence in postharvesting may spoil all the efforts and cause severe economic loss. Many studies have shown that, for instance, farmers lose as high as 40% of fruit and vegetables before they reach the final end-users ( Kitinoja et al., 2011 ). Activities that can be considered in the postharvest stage are factors that affect the shelf life of the food and postharvest handling processes to maintain the food quality ( Meshram et al., 2021 ). Hence, postharvest management is a collection of post-production practices that includes cleaning, washing, selection, grading, disinfection, drying, packing, and storage. These remove undesirable elements and improve product appearance while ensuring the product meets established quality standards for fresh and processed products ( El-Ramady et al., 2015 ). Based on the analyzed data and the observed research evolution and trend, we categorized opportunities for future research into three broad categories discussed below.

3.4.1. Food classifications (grading and sorting)

The appearance of food, mainly fruits and vegetables, is an important property that influences its market value, affects consumer choices, and, to some extent, is an indicator of its internal quality ( Naik and Patel, 2017 ). Human graders have typically performed quality inspections and classified agricultural and food products. Some features such as color, texture, size, shape, weight, and visual defects are generally examined to assess the outside quality of food ( Naik and Patel, 2017 ). However, most manual inspections are time-consuming and labor-intensive ( Brosnan and Sun, 2002 ; Naik and Patel, 2017 ). Moreover, the manual classification of food products exacerbates postharvest losses, but technology and emerging algorithms offer solutions to reduce such losses ( Piedad et al., 2018 ). Hence, there is a need for a fast, efficient, and intelligent food grading system. Artificial Intelligence is rapidly becoming a part of agriculture’s technological evolution to increase food production and reduce losses. Artificial intelligence in food classification, grading, and sorting offers an excellent opportunity for reducing the manual work of classification and sorting to improve the quality of food grading. For instance, computer vision and numerous algorithms (such as machine learning and deep learning) help to achieve the required food classification and grading to produce accurate, rapid, consistent, and efficient outcomes over manual work, which can be done automatically, provided some standard grading criteria are made ( Naik and Patel, 2017 ; Behera et al., 2020 ; Bhargava et al., 2022 ). The following are examples of research questions:

a. How can AI technologies aid the identification and classification of the ripening degree of different produce?

b. What are the possibilities of classifying various agricultural produce using higher-level features with more discriminative information with deep learning methods?

In food identification and classification, particularly in fresh produce, previous endeavors have commonly revolved around using various sensors in conjunction with machine learning or deep learning techniques. The primary objective of these efforts has been to decipher and identify the intrinsic features associated with different types of produce. For instance, attributes such as shape, color, texture, and size in fruit and vegetables have been harnessed to execute classification tasks ( Hameed et al., 2018 ). However, classifying fruits and vegetables remains a formidable challenge, primarily due to several factors. These include the irregularities observed in their shapes, variations in size, the dynamic range of colors, and the profound variability inherent within a single category. The latter is particularly pronounced in cases where the maturity phase significantly influences visual characteristics, particularly in the context of fruits ( Hameed et al., 2018 ; Hossain et al., 2018 ).

Addressing the intricate challenges stemming from their inherent diversity, the next generation of AI models is poised to take a transformative approach. These future models will be engineered to accommodate the multifaceted differences encompassing shape, size, color, and ripeness. Deep learning architectures, honed through extensive and diverse datasets, are set to fortify the system’s resilience across various environmental conditions. This resilience is a testament to the AI model’s capacity to withstand the complexities arising from variations in lighting, perspectives, and environmental factors, ensuring a steadfast and reliable performance ( Shahi et al., 2022 ). Moreover, the augmentation of AI’s capabilities extends to the mastery of comprehensive datasets that encapsulate a diverse spectrum of fruit and vegetable characteristics. This embracement of a broad knowledge base enables AI systems to traverse beyond the confines of specific conditions and generalize their discernment. Such adaptability proves indispensable in securing accurate classifications, thus facilitating confident and precise identifications.

Furthermore, incorporating multispectral imaging techniques introduces an innovative dimension ( Gaikwad and Tidke, 2022 ). By extending the scope of analysis beyond the constraints of the visible spectrum, AI-powered systems are endowed with the capability to capture information that transcends human visual perception. This broader perspective facilitates the identification of nuanced distinctions in elements such as shape attributes, color variations, surface textures, ripeness indicators, and even the freshness of the produce. As the amalgamation of AI technologies and multispectral imaging paves the way for a more comprehensive understanding of produce characteristics, the horizon of food classification and identification stands poised for a paradigm shift. These combined advancements address the complexities posed by variability and unlock novel avenues for improved accuracy, efficiency, and reliability, ultimately shaping the future landscape of food classification and identification.

3.4.2. Food quality evaluation

Another aspect that has been identified is food quality. Although supplying similar products is critical, food quality is essential to the consumer’s delight ( Hemamalini et al., 2022 ). Food quality assessment, including freshness assessment, is rapidly becoming a priority for both end-users (consumers) and food processing industries ( Takruri et al., 2020 ). Although it is a complex concept that is frequently measured using objective indices related to the food’s nutritional, microbiological, or physicochemical characteristics or designated expert opinions ( Cardello, 1995 ; Giménez et al., 2012 ), its role in decision-making regarding storage and processing requirements is critical ( Takruri et al., 2020 ). Typically, conventional methods (chemical or biological analyses) are used to evaluate food quality. However, they are time-consuming, costly, require highly skilled or trained technicians (operators), and are often destructive, resulting in a high financial loss. Similarly, the use of nondestructive techniques for evaluating different food quality attributes has grown over the years and has proved to be rapid and accurate in estimating food quality. These include near-infrared spectroscopy, Fourier transform infrared spectroscopy, color and visual spectroscopy, electronic nose and tongue, computer vision (image analysis), ultrasound, X-ray, CT, and magnetic resonance imaging ( Jha, 2010 ; Amoriello et al., 2019 ; Pu et al., 2019 ; Wang et al., 2019 ; Dold and Langowski, 2022 ; Malvandi et al., 2022 ). While nondestructive methods are beneficial for quality testing, their implementation necessitates expertise, skill, and experience; besides, data processing can be computationally expensive ( Abasi et al., 2018 ).

Consumers place a high value on food quality, mainly fresh fruits and vegetables. Besides, it is critical to breeding activities’ success and the fruit industry’s competitiveness and profitability ( Mezzetti et al., 2018 ; Amoriello et al., 2022 ). The entire food supply chain has recently demanded simple and quick quality evaluation systems. Artificial intelligence, machine learning, and deep learning have provided an avenue to effectively evaluate different food quality characteristics and play a crucial role in food safety and quality assurance ( Dharmaraj and Vijayanand, 2018 ; Liu, 2020 ). For instance, the chemical attributes of food, mainly fruits and vegetables (such as total soluble solids, acidity, pH, firmness, etc.) and nutritional properties (such as total phenols, anthocyanins, antioxidant potential, etc.) can be determined efficiently using these techniques.

Furthermore, it is well established that the deterioration of most fruits and vegetables is linked to rapid metabolism, cellular damage, and softening that occurs due to external damage during postharvest handling ( Li and Thomas, 2014 ; Fadiji et al., 2016a , b ; Hussein et al., 2018 ; Opara and Fadiji, 2018 ; Choi et al., 2021 ). AI, ML, and DL can quickly detect small changes or defects that may occur in the produce subjected to mechanical damage during handling, distribution, and storage, allowing easy monitoring and sorting ( Azizah et al., 2017 ). Also, these techniques are becoming valuable tools in conjunction with sensing devices and computer vision to detect pathological disorders in food associated with attacks by viruses, fungi, bacteria, or microbial pathogens ( Ray et al., 2017 ; Cui et al., 2018 ; Gokulnath, 2021 ); physiological stresses, which can lead to disorders such as bitter pit, watercore, mealiness, sunburn, browning, superficial scald, granulation, and internal drying, among others ( Magwaza et al., 2012 ; Zhang et al., 2020 ; Tang et al., 2022 ); morphological disorders that manifest themselves as deformations that make foods have an irregular or abnormal appearance ( Moallem et al., 2017 ; Ireri et al., 2019 ); internal defects ( Arendse et al., 2016 , 2018a , b ; Nturambirwe and Opara, 2020 ; Van De Looverbosch et al., 2020 ; Dubey et al., 2021 ; Çetin et al., 2022 ; Okere et al., 2022 ). The ability to detect or identify various food defects aids in reducing the risk of spoilage and decay. Moreover, identifying and sorting defective produce reduces contamination of healthy produce, mitigating food losses while maintaining high consumer satisfaction.

The following are examples of research questions:

a. How can AI technologies improve the shortcomings of physical field drying of fruits and vegetables?

b. How can AI technologies detect defects in produce such as fruit and vegetables effectively?

c. What is the feasibility of applying AI techniques (deep learning) in combination with other promising techniques, such as thermography and magnetic resonance, for the early detection of defects?

The application and development of Artificial Intelligence (AI) in the domain of food drying have garnered substantial attention. With advanced technologies such as big data and cloud computing, AI is dramatically reshaping the global landscape and transforming everyday life ( Chen et al., 2020 ; Misra et al., 2020 ). AI emerges as a transformative solution as we strive to achieve high-quality end products, reduce operational and energy costs, enhance production rates, and optimize the design and operational parameters of industrial-scale dryers. Its inherent self-learning ability, adaptability, robust fault tolerance, and proficiency in mapping complex and dynamic phenomena make it a potent alternative strategy for various aspects of drying modeling, physicochemical property analysis, and quality optimization.

The utilization of AI in food drying has been particularly impactful in addressing critical challenges. Dry intelligence innovation, energy conservation, and intelligent process control are focal points that demand attention to ensure superior drying outcomes. AI’s capabilities have been harnessed to tackle these challenges, offering innovative approaches that lead to enhanced results, reduced costs, and optimized operational parameters.

AI’s contributions encompass a spectrum of applications, including the modeling, predicting, and optimizing of essential parameters such as heat and mass transfer, thermodynamic performance metrics, quality indicators, and physicochemical properties of dried products. These applications span artificial biomimetic technologies, such as electronic nose and computer vision, and various conventional drying methods. The implementation of AI tools, including Artificial Neural Networks (ANN), fuzzy logic, expert systems, and evolutionary algorithms, either individually or in combination, has proven exceptionally effective in addressing intricate problems within the drying process ( Aghbashlo et al., 2015 ; Sun et al., 2019 ).

The potential of AI in defect detection holds significant promise, particularly in achieving the coveted characteristic of “fast detection.” While computer vision has made substantial strides in defect detection, its scope is often limited to surface-level defects. However, integrating hyperspectral and multispectral imaging with AI technologies presents a compelling avenue for rapid and comprehensive defect detection ( Feng and Sun, 2012 ; Soni et al., 2022 ). Hyperspectral and multispectral imaging technologies are emerging as powerful contenders for rapidly detecting agricultural produce defects. AI, and specifically DL, plays a pivotal role in effectively implementing these “fast” detection systems ( Wieme et al., 2022 ). The advancements enabled by DL in image processing and feature extraction have revolutionized the efficiency of direct defect detection. Despite the potential demonstrated by the application of DL in this domain, research in this area, particularly in the context of adulteration detection, remains relatively scarce. Consequently, there is a pressing need for further research to harness the full potential of AI and DL in realizing these goals.

Additionally, AI’s successful application extends to the quantitative analysis of fruit deterioration, encompassing factors such as severity and degree of damage defects. In conjunction with objective non-destructive measuring instruments, learning methods have been employed to distinguish between different extents of deterioration. However, despite the advancements, this study area remains under-explored in leveraging sophisticated AI algorithms. The quantification of damage and disorders continues to pose challenges in the agriculture industry, warranting heightened attention to utilizing learning algorithms for enhanced accuracy and efficiency.

3.4.3. Real-time monitoring and decision support system

Due to the increased consumer demand for food, food industries are not keeping up with the demand–supply chain and also lacking in food safety. Similarly, until now, retailers lack visibility into the real-time status of foods in stock, resulting in massive food losses and waste. AI technology is a promising tool for monitoring food status in the supply chain ( Wang et al., 2022 ). It is critical to monitor potential food safety hazards throughout the food supply chain to ensure the proper operation of food safety management systems and, consequently, the food products [ International Organization for Standardization (ISO), 2013 ; Focker et al., 2018 ]. Besides, an AI-based system can manage food production and distribution processes more efficiently and effectively based on AI, ML, and DL algorithms ( Kumar et al., 2021 ; Wang et al., 2022 ). For instance, the cold chain, in particular, plays an essential role in the supply chain’s quality control of perishable foods such as fresh fruits and vegetables ( Lu and Wang, 2016 ; Loisel et al., 2021 ). Fluctuating temperatures or breaks could rapidly deteriorate the perishable products and significantly impact the food shelf life ( Ndraha et al., 2018 ; Villa-Gonzalez et al., 2022 ). It is, therefore, critical to maintaining optimum environmental conditions to control the quality of perishable foods in real-time, detect cold chain breaks, and measure their impact on product quality. AI has proven to be more precise and efficient in performance compared to traditional or physic-based systems (models; Mercier and Uysal, 2018 ; Hoang et al., 2021 ; Loisel et al., 2022 ). In the cold chain, wireless temperature sensors and data transmission are expected to be widely used to provide a large amount of data, making real-time analyses and predictions possible ( Loisel et al., 2021 ). Additionally, with the structured infrastructure of the sensor network in IoT (Internet of Things) and robust computations in AI, integrating IoT and AI is a promising way to establish risk monitoring systems in the cold chain for controlling product quality ( Tsang et al., 2018 ).

Another aspect in which AI could be useful is in the area of food traceability. Food traceability involves tracking, tracing, and evaluating food quality along the supply chain and providing consumers with information to increase consumer experience and product confidence in the food supply chain ( Aung and Chang, 2014 ; Islam and Cullen, 2021 ). AI has shown the potential to improve how food is traced, decrease losses and waste, and mitigate the vulnerability to food fraud ( Hassoun et al., 2022 ).

4. Conclusions, research implications, and limitations

This study conducted a bibliometric analysis utilizing techniques such as co-authorship, co-occurrence, co-citations, and visualization networks of AI applications in postharvest agriculture research. The study analyzed 586 documents obtained from Scopus and the most extended study period (1994–2022) to present the state of the art, future trends and identify research tendencies. The review results can be relevant to researchers actively applying AI in postharvest agriculture. Several significant observations were made in this study. First, the number of papers on AI applications in postharvest agriculture has substantially grown, particularly from 2009 to 2022. Country-wise productivity showed China and USA as the top countries in the research area. The distribution of papers published according to journals suggested that Computers and Electronics in Agriculture is the top or leading journal contributing to the knowledge on AI application in postharvest agriculture. Regarding author productivity, Zhang X and Zhang Z are the most prolific authors in the research area. In terms of contributions of academic institutions, results revealed Zhejiang University, located in China, as the most productive institution. The keywords analysis showed that the frequently used keywords include machine learning, food quality, artificial intelligence, deep learning, quality control, learning systems, food safety, fruit, learning algorithms, and data mining. According to the thematic map trend, food quality, machine learning, deep learning, quality control, and learning systems are well-developed themes fundamental for structuring the research field. Whereas Internet of Things, cold chain logistics, big data, decision making, and real time monitoring are weakly developed and marginal areas in the research domain.

The present study can help researchers and practitioners embarking on this topic who want a comprehensive overview of the scientific literature produced. Moreover, scholars can leverage the results of this study to address future studies better, considering the proposed avenues for future research. Additionally, this work can provide a valuable perspective for future research in the studied knowledge domain since it demonstrates the existence of an emerging area of study intended to enhance quality and reduce postharvest losses and waste in horticultural crops.

In the present study, only one database was used: Scopus. Scopus is a widely recognized multidisciplinary abstract and citation database offering comprehensive coverage of academic literature from various disciplines. Its user-friendly interface and powerful search capabilities make it efficient and familiar to researchers. Focusing on one database makes the study’s literature review process time and resource-efficient while ensuring access to peer-reviewed and high-quality content. Moreover, Scopus provides citation analysis and impact metrics to assess research significance. The consistency in data retrieval enhances data management and analysis throughout the study. The specificity of Scopus also aligns with the study’s focus, allowing for in-depth analysis and a comprehensive understanding of the research topic. Nevertheless, while Scopus is a valuable resource, future work could benefit from including additional databases such as the Web of Science (WOS) and Medline to strengthen the study’s robustness and broaden its scope. Furthermore, the keywords used in the search string may have excluded some important papers. Hence, for more enriched results, future studies should include more keywords. Another limitation is the lack of citations for newly published papers. New research takes time to accumulate citations, while older publications have more citations.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

TF, TB, OF, and HT: conceptualization and writing—review and editing. TF and TB: methodology and writing—original draft preparation. OF and HT: funding acquisition. All authors contributed to the article and approved the submitted version.

This work was supported by the research grant from the University Research Committee (URC): Faculty Strategic Intervention at the University of Johannesburg for the Postharvest Applied Data Science Flagship and the Global Excellence Stature Fellowship 4.0 awarded to TF at the University of Johannesburg.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2023.1226583/full#supplementary-material

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Keywords: artificial intelligence, postharvest technology, machine learning, deep learning, food quality

Citation: Fadiji T, Bokaba T, Fawole OA and Twinomurinzi H (2023) Artificial intelligence in postharvest agriculture: mapping a research agenda. Front. Sustain. Food Syst . 7:1226583. doi: 10.3389/fsufs.2023.1226583

Received: 21 May 2023; Accepted: 14 August 2023; Published: 26 September 2023.

Reviewed by:

Copyright © 2023 Fadiji, Bokaba, Fawole and Twinomurinzi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tobi Fadiji, [email protected] ; [email protected]

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Machine Learning in Agriculture: A Comprehensive Updated Review

Lefteris benos.

1 Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; [email protected] (L.B.); [email protected] (A.C.T.); [email protected] (G.D.); [email protected] (D.K.)

Aristotelis C. Tagarakis

Georgios dolias, remigio berruto.

2 Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy; [email protected]

Dimitrios Kateris

Dionysis bochtis.

3 FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

1. Introduction

1.1. general context of machine learning in agriculture.

Modern agriculture has to cope with several challenges, including the increasing call for food, as a consequence of the global explosion of earth’s population, climate changes [ 1 ], natural resources depletion [ 2 ], alteration of dietary choices [ 3 ], as well as safety and health concerns [ 4 ]. As a means of addressing the above issues, placing pressure on the agricultural sector, there exists an urgent necessity for optimizing the effectiveness of agricultural practices by, simultaneously, lessening the environmental burden. In particular, these two essentials have driven the transformation of agriculture into precision agriculture. This modernization of farming has a great potential to assure sustainability, maximal productivity, and a safe environment [ 5 ]. In general, smart farming is based on four key pillars in order to deal with the increasing needs; (a) optimal natural resources’ management, (b) conservation of the ecosystem, (c) development of adequate services, and (d) utilization of modern technologies [ 6 ]. An essential prerequisite of modern agriculture is, definitely, the adoption of Information and Communication Technology (ICT), which is promoted by policy-makers around the world. ICT can indicatively include farm management information systems, humidity and soil sensors, accelerometers, wireless sensor networks, cameras, drones, low-cost satellites, online services, and automated guided vehicles [ 7 ].

The large volume of data, which is produced by digital technologies and usually referred to as “big data”, needs large storage capabilities in addition to editing, analyzing, and interpreting. The latter has a considerable potential to add value for society, environment, and decision-makers [ 8 ]. Nevertheless, big data encompass challenges on account of their so-called “5-V” requirements; (a) Volume, (b) Variety, (c) Velocity, (d) Veracity, and (e) Value [ 9 ]. The conventional data processing techniques are incapable of meeting the constantly growing demands in the new era of smart farming, which is an important obstacle for extracting valuable information from field data [ 10 ]. To that end, Machine Learning (ML) has emerged, which is a subset of artificial intelligence [ 11 ], by taking advantage of the exponential computational power capacity growth.

There is a plethora of applications of ML in agriculture. According to the recent literature survey by Liakos et al. [ 12 ], regarding the time period of 2004 to 2018, four generic categories were identified ( Figure 1 ). These categories refer to crop, water, soil, and livestock management. In particular, as far as crop management is concerned, it represented the majority of the articles amongst all categories (61% of the total articles) and was further sub-divided into:

• Yield prediction;
• Disease detection;
• Weed detection;
• Crop recognition;
• Crop quality.

The four generic categories in agriculture exploiting machine learning techniques, as presented in [ 12 ].

The generic categories dealing with the management of water and soil were found to be less investigated, corresponding cumulatively to 20% of the total number of papers (10% for each category).

Finally, two main sub-categories were identified for the livestock-related applications corresponding to a total 19% of journal papers:

• Livestock production;
• Animal welfare.

1.2. Open Problems Associated with Machine Learning in Agriculture

Due to the broad range of applications of ML in agriculture, several reviews have been published in this research field. The majority of these review studies have been dedicated to crop disease detection [ 13 , 14 , 15 , 16 ], weed detection [ 17 , 18 ], yield prediction [ 19 , 20 ], crop recognition [ 21 , 22 ], water management [ 23 , 24 ], animal welfare [ 25 , 26 ], and livestock production [ 27 , 28 ]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [ 29 ]. Finally, focus has been paid on big data analysis using ML, aiming at finding out real-life problems that originated from smart farming [ 30 ], or dealing with methods to analyze hyperspectral and multispectral data [ 31 ].

Although ML in agriculture has made considerable progress, several open problems remain, which have some common points of reference, despite the fact that the topic covers a variety of sub-fields. According to [ 23 , 24 , 28 , 32 ], the main problems are associated with the implementation of sensors on farms for numerous reasons, including high costs of ICT, traditional practices, and lack of information. In addition, the majority of the available datasets do not reflect realistic cases, since they are normally generated by a few people getting images or specimens in a short time period and from a limited area [ 15 , 21 , 22 , 23 ]. Consequently, more practical datasets coming from fields are required [ 18 , 20 ]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [ 18 , 22 , 23 , 31 ]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [ 16 , 29 ], blind spots of cameras, environmental noise, and simultaneous vocalizations [ 25 ]. Another important open problem is that the vast majority of farmers are non-experts in ML and, thus, they cannot fully comprehend the underlying patterns obtained by ML algorithms. For this reason, more user-friendly systems should be developed. In particular, simple systems, being easy to understand and operate, would be valuable, as for example a visualization tool with a user-friendly interface for the correct presentation and manipulation of data [ 25 , 30 , 31 ]. Taking into account that farmers are getting more and more familiar with smartphones, specific smartphone applications have been proposed as a possible solution to address the above challenge [ 15 , 16 , 21 ]. Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [ 19 , 22 , 24 , 33 ]. As stated in [ 12 ], currently, all of the efforts pertain to individual solutions, which are not always connected with the process of decision-making, as seen for example in other domains.

1.3. Aim of the Present Study

As pointed out above, because of the multiple applications of ML in agriculture, several review studies have been published recently. However, these studies usually concentrate purely on one sub-field of agricultural production. Motivated by the current tremendous progress in ML, the increasing interest worldwide, and its impact in various do-mains of agriculture, a systematic bibliographic survey is presented on the range of the categories proposed in [ 12 ], which were summarized in Figure 1 . In particular, we focus on reviewing the relevant literature of the last three years (2018–2020) for the intention of providing an updated view of ML applications in agricultural systems. In fact, this work is an updated continuation of the work presented at [ 12 ]; following, consequently, exactly the same framework and inclusion criteria. As a consequence, the scholarly literature was screened in order to cover a broad spectrum of important features for capturing the current progress and trends, including the identification of: (a) the research areas which are interested mostly in ML in agriculture along with the geographical distribution of the contributing organizations, (b) the most efficient ML models, (c) the most investigated crops and animals, and (d) the most implemented features and technologies.

As will be discussed next, overall, a 745% increase in the number of journal papers took place in the last three years as compared to [ 12 ], thus justifying the need for a new updated review on the specific topic. Moreover, crop management remained as the most investigated topic, with a number of ML algorithms having been exploited as a means of tackling the heterogeneous data that originated from agricultural fields. As compared to [ 12 ], more crop and animal species have been investigated by using an extensive range of input parameters coming mainly from remote sensing, such as satellites and drones. In addition, people from different research fields have dealt with ML in agriculture, hence, contributing to the remarkable advancement in this field.

1.4. Outline of the Paper

The remainder of this paper is structured as follows. The second section briefly describes the fundamentals of ML along with the subject of the four generic categories for the sake of better comprehension of the scope of the present study. The implemented methodology, along with the inclusive criteria and the search engines, is analyzed in the third section. The main performance metrics, which were used in the selected articles, are also presented in this section. The main results are shown in the fourth section in the form of bar and pie charts, while in the fifth section, the main conclusions are drawn by also discussing the results from a broader perspective. Finally, all the selected journal papers are summarized in Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 , in accordance with their field of application, and presented in the Appendix A , together with Table A10 and Table A11 that contain commonly used abbreviations, with the intention of not disrupting the flow of the main text.

2. Background

2.1. fundamentals of machine learning: a brief overview.

In general, the objective of ML algorithms is to optimize the performance of a task, via exploiting examples or past experience. In particular, ML can generate efficient relationships regarding data inputs and reconstruct a knowledge scheme. In this data-driven methodology, the more data are used, the better ML works. This is similar to how well a human being performs a particular task by gaining more experience [ 34 ]. The central outcome of ML is a measure of generalizability; the degree to which the ML algorithm has the ability to provide correct predictions, when new data are presented, on the basis of learned rules originated from preceding exposure to similar data [ 35 ]. More specifically, data involve a set of examples, which are described by a group of characteristics, usually called features. Broadly speaking, ML systems operate at two processes, namely the learning (used for training) and testing. In order to facilitate the former process, these features commonly form a feature vector that can be binary, numeric, ordinal, or nominal [ 36 ]. This vector is utilized as an input within the learning phase. In brief, by relying on training data, within the learning phase, the machine learns to perform the task from experience. Once the learning performance reaches a satisfactory point (expressed through mathematical and statistical relationships), it ends. Subsequently, the model that was developed through the training process can be used to classify, cluster, or predict.

An overview of a typical ML system is illustrated in Figure 2 . With the intention of forming the derived complex raw data into a suitable state, a pre-processing effort is required. This usually includes: (a) data cleaning for removing inconsistent or missing items and noise, (b) data integration, when many data sources exist and (c) data transformation, such as normalization and discretization [ 37 ]. The extraction/selection feature aims at creating or/and identifying the most informative subset of features in which, subsequently, the learning model is going to be implemented throughout the training phase [ 38 ]. Regarding the feedback loop, which is depicted in Figure 2 , it serves for adjustments pertaining to the feature extraction/selection unit as well as the pre-processing one that further improves the overall learning model’s performance. During the phase of testing, previously unseen samples are imported to the trained model, which are usually represented as feature vectors. Finally, an appropriate decision is made by the model (for example, classification or regression) in reliance of the features existing in each sample. Deep learning, a subfield of ML, utilizes an alternative architecture via shifting the process of converting raw data to features (feature engineering) to the corresponding learning system. Consequently, the feature extraction/selection unit is absent, resulting in a fully trainable system; it starts from a raw input and ends with the desired output [ 39 , 40 ].

A graphical illustration of a typical machine learning system.

Based on the learning type, ML can be classified according to the relative literature [ 41 , 42 ] as:

• Supervised learning: The input and output are known and the machine tries to find the optimal way to reach an output given an input;
• Unsupervised learning: No labels are provided, leaving the learning algorithm itself to generate structure within its input;
• Semi-supervised learning: Input data constitute a mixture of labeled and non-labeled data;
• Reinforcement learning: Decisions are made towards finding out actions that can lead to the more positive outcome, while it is solely determined by trial and error method and delayed outcome.

Nowadays, ML is used in facilitating several management aspects in agriculture [ 12 ] and in a plethora of other applications, such as image recognition [ 43 ], speech recognition [ 44 ], autonomous driving [ 45 ], credit card fraud detection [ 46 ], stock market forecasting [ 47 ], fluid mechanics [ 48 ], email, spam and malware filtering [ 49 ], medical diagnosis [ 40 ], contamination detection in urban water networks [ 50 ], and activity recognition [ 51 ], to mention but a few.

2.2. Brief Description of the Four Generic Categories

2.2.1. crop management.

The crop management category involves versatile aspects that originated from the combination of farming techniques in the direction of managing the biological, chemical and physical crop environment with the aim of reaching both quantitative and qualitative targets [ 52 ]. Using advanced approaches to manage crops, such as yield prediction, disease detection, weed detection, crop recognition, and crop quality, contributes to the increase of productivity and, consequently, the financial income. The above aspects constitute key goals of precision agriculture.

Yield Prediction

In general, yield prediction is one of the most important and challenging topics in modern agriculture. An accurate model can help, for instance, the farm owners to take informed management decisions on what to grow towards matching the crop to the existing market’s demands [ 20 ]. However, this is not a trivial task; it consists of various steps. Yield prediction can be determined by several factors such as environment, management practices, crop genotypic and phenotypic characteristics, and their interactions. Hence, it necessitates a fundamental comprehension of the relationship between these interactive factors and yield. In turn, identifying such kinds of relationships mandates comprehensive datasets along with powerful algorithms such as ML techniques [ 53 ].

Disease Detection

Crop diseases constitute a major threat in agricultural production systems that deteriorate yield quality and quantity at production, storage, and transportation level. At farm level, reports on yield losses, due to plant diseases, are very common [ 54 ]. Furthermore, crop diseases pose significant risks to food security at a global scale. Timely identification of plant diseases is a key aspect for efficient management. Plant diseases may be provoked by various kinds of bacteria, fungi, pests, viruses, and other agents. Disease symptoms, namely the physical evidence of the presence of pathogens and the changes in the plants’ phenotype, may consist of leaf and fruit spots, wilting and color change [ 55 ], curling of leaves, etc. Historically, disease detection was conducted by expert agronomists, by performing field scouting. However, this process is time-consuming and solely based on visual inspection. Recent technological advances have made commercially available sensing systems able to identify diseased plants before the symptoms become visible. Furthermore, in the past few years, computer vision, especially by employing deep learning, has made remarkable progress. As highlighted by Zhang et al. [ 56 ], who focused on identifying cucumber leaf diseases by utilizing deep learning, due to the complex environmental background, it is beneficial to eliminate background before model training. Moreover, accurate image classifiers for disease diagnosis need a large dataset of both healthy and diseased plant images. In reference to large-scale cultivations, such kinds of automated processes can be combined with autonomous vehicles, to timely identify phytopathological problems by implementing regular inspections. Furthermore, maps of the spatial distribution of the plant disease can be created, depicting the zones in the farm where the infection has been spread [ 57 ].

Weed Detection

As a result of their prolific seed production and longevity, weeds usually grow and spread invasively over large parts of the field very fast, competing with crops for the resources, including space, sunlight, nutrients, and water availability. Besides, weeds frequently arise sooner than crops without having to face natural enemies, a fact that adversely affects crop growth [ 18 ]. In order to prevent crop yield reduction, weed control is an important management task by either mechanical treatment or application of herbicides. Mechanical treatment is, in most cases, difficult to be performed and ineffective if not properly performed, making herbicide application the most widely used operation. Using large quantities of herbicides, however, turns out to be both costly and detrimental for the environment, especially in the case of uniform application without taking into account the spatial distribution of the weeds. Remarkably, long-term herbicide use is very likely to make weeds more resistant, thus, resulting in more demanding and expensive weed control. In recent years, considerable achievements have been made pertaining to the differentiation of weeds from crops on the basis of smart agriculture. This discrimination can be accomplished by using remote or proximal sensing with sensors attached on satellites, aerial, and ground vehicles, as well as unmanned vehicles (both ground (UGV) and aerial (UAV)). The transformation of data gathered by UAVs into meaningful information is, however, still a challenging task, since both data collection and classification need painstaking effort [ 58 ]. ML algorithms coupled with imaging technologies or non-imaging spectroscopy can allow for real-time differentiation and localization of target weeds, enabling precise application of herbicides to specific zones, instead of spraying the entire fields [ 59 ] and planning of the shortest weeding path [ 60 ].

Crop Recognition

Automatic recognition of crops has gained considerable attention in several scientific fields, such as plant taxonomy, botanical gardens, and new species discovery. Plant species can be recognized and classified via analysis of various organs, including leaves, stems, fruits, flowers, roots, and seeds [ 61 , 62 ]. Using leaf-based plant recognition seems to be the most common approach by examining specific leaf’s characteristics like color, shape, and texture [ 63 ]. With the broader use of satellites and aerial vehicles as means of sensing crop properties, crop classification through remote sensing has become particularly popular. As in the above sub-categories, the advancement on computer software and image processing devices combined with ML has led to the automatic recognition and classification of crops.

Crop Quality

Crop quality is very consequential for the market and, in general, is related to soil and climate conditions, cultivation practices and crop characteristics, to name a few. High quality agricultural products are typically sold at better prices, hence, offering larger earnings to farmers. For instance, as regards fruit quality, flesh firmness, soluble solids content, and skin color are among the most ordinary maturity indices utilized for harvesting [ 64 ]. The timing of harvesting greatly affects the quality characteristics of the harvested products in both high value crops (tree crops, grapes, vegetables, herbs, etc.) and arable crops. Therefore, developing decision support systems can aid farmers in taking appropriate management decisions for increased quality of production. For example, selective harvesting is a management practice that may considerably increase quality. Furthermore, crop quality is closely linked with food waste, an additional challenge that modern agriculture has to cope with, since if the crop deviates from the desired shape, color, or size, it may be thrown away. Similarly to the above sub-section, ML algorithms combined with imaging technologies can provide encouraging results.

2.2.2. Water Management

The agricultural sector constitutes the main consumer of available fresh water on a global scale, as plant growth largely relies on water availability. Taking into account the rapid depletion rate of a lot of aquifers with negligible recharge, more effective water management is needed for the purpose of better conserving water in terms of accomplishing a sustainable crop production [ 65 ]. Effective water management can also lead to the improvement of water quality as well as reduction of pollution and health risks [ 66 ]. Recent research on precision agriculture offers the potential of variable rate irrigation so as to attain water savings. This can be realized by implementing irrigation at rates, which vary according to field variability on the basis of specific water requirements of separate management zones, instead of using a uniform rate in the entire field. The effectiveness and feasibility of the variable rate irrigation approach depend on agronomic factors, including topography, soil properties, and their effect on soil water in order to accomplish both water savings and yield optimization [ 67 ]. Carefully monitoring the status of soil water, crop growth conditions, and temporal and spatial patterns in combination with weather conditions monitoring and forecasting, can help in irrigation programming and efficient management of water. Among the utilized ICTs, remote sensing can provide images with spatial and temporal variability associated with the soil moisture status and crop growth parameters for precision water management. Interestingly, water management is challenging enough in arid areas, where groundwater sources are used for irrigation, with the precipitation providing only part of the total crop evapotranspiration (ET) demands [ 68 ].

2.2.3. Soil Management

Soil, a heterogeneous natural resource, involves mechanisms and processes that are very complex. Precise information regarding soil on a regional scale is vital, as it contributes towards better soil management consistent with land potential and, in general, sustainable agriculture [ 5 ]. Better management of soil is also of great interest owing to issues like land degradation (loss of the biological productivity), soil-nutrient imbalance (due to fertilizers overuse), and soil erosion (as a result of vegetation overcutting, improper crop rotations rather than balanced ones, livestock overgrazing, and unsustainable fallow periods) [ 69 ]. Useful soil properties can entail texture, organic matter, and nutrients content, to mention but a few. Traditional soil assessment methods include soil sampling and laboratory analysis, which are normally expensive and take considerable time and effort. However, remote sensing and soil mapping sensors can provide low-cost and effortless solution for the study of soil spatial variability. Data fusion and handling of such heterogeneous “big data” may be important drawbacks, when traditional data analysis methods are used. ML techniques can serve as a trustworthy, low-cost solution for such a task.

2.2.4. Livestock Management

It is widely accepted that livestock production systems have been intensified in the context of productivity per animal. This intensification involves social concerns that can influence consumer perception of food safety, security, and sustainability, based on animal welfare and human health. In particular, monitoring both the welfare of animals and overall production is a key aspect so as to improve production systems [ 70 ]. The above fields take place in the framework of precision livestock farming, aiming at applying engineering techniques to monitor animal health in real time and recognizing warning messages, as well as improving the production at the initial stages. The role of precision livestock farming is getting more and more significant by supporting the decision-making processes of livestock owners and changing their role. It can also facilitate the products’ traceability, in addition to monitoring their quality and the living conditions of animals, as required by policy-makers [ 71 ]. Precision livestock farming relies on non-invasive sensors, such as cameras, accelerometers, gyroscopes, radio-frequency identification systems, pedometers, and optical and temperature sensors [ 25 ]. IoT sensors leverage variable physical quantities (VPQs) as a means of sensing temperature, sound, humidity, etc. For instance, IoT sensors can warn if a VPQ falls out of regular limits in real-time, giving valuable information regarding individual animals. As a result, the cost of repetitively and arduously checking each animal can be reduced [ 72 ]. In order to take advantage of the large amounts of data, ML methodologies have become an integral part of modern livestock farming. Models can be developed that have the capability of defining the manner a biological system operates, relying on causal relationships and exploiting this biological awareness towards generating predictions and suggestions.

Animal Welfare

There is an ongoing concern for animal welfare, since the health of animals is strongly associated with product quality and, as a consequence, predominantly with the health of consumers and, secondarily, with the improvement of economic efficiency [ 73 ]. There exist several indexes for animal welfare evaluation, including physiological stress and behavioral indicators. The most commonly used indicator is animal behavior, which can be affected by diseases, emotions, and living conditions, which have the potential to demonstrate physiological conditions [ 25 ]. Sensors, commonly used to detect behavioral changes (for example, changes in water or food consumption, reduced animal activity), include microphone systems, cameras, accelerometers, etc.

Livestock Production

The use of sensor technology, along with advanced ML techniques, can increase livestock production efficiency. Given the impact of practices of animal management on productive elements, livestock owners are getting cautious of their asset. However, as the livestock holdings get larger, the proper consideration of every single animal is very difficult. From this perspective, the support to farmers via precision livestock farming, mentioned above, is an auspicious step for aspects associated with economic efficiency and establishment of sustainable workplaces with reduced environmental footprint [ 74 ]. Generally, several models have been used in animal production, with their intentions normally revolving around growing and feeding animals in the best way. However, the large volumes of data being involved, again, call for ML approaches.

3.1. Screening of the Relative Literature

In order to identify the relevant studies concerning ML in respect to different aspects of management in agriculture, the search engines of Scopus, Google Scholar, ScienceDirect, PubMed, Web of Science, and MDPI were utilized. In addition, keywords’ combinations of “machine learning” in conjunction with each of the following: “crop management”, “water management”, “soil management”, and “livestock management” were used. Our intention was to filter the literature on the same framework as [ 12 ]; however, focusing solely within the period 2018–2020. Once a relevant study was being identified, the references of the paper at hand were being scanned to find studies that had not been found throughout the initial searching procedure. This process was being iterated until no relevant studies occurred. In this stage, only journal papers were considered eligible. Thus, non-English studies, conferences papers, chapters, reviews, as well as Master and Doctoral Theses were excluded. The latest search was conducted on 15 December 2020. Subsequently, the abstract of each paper was being reviewed, while, at a next stage, the full text was being read to decide its appropriateness. After a discussion between all co-authors with reference to the appropriateness of the selected papers, some of them were excluded, in the case they did not meet the two main inclusion criteria, namely: (a) the paper was published within 2018–2020 and (b) the paper referred to one of the categories and sub-categories, which were summarized in Figure 1 . Finally, the papers were classified in these sub-categories. Overall, 338 journal papers were identified. The flowchart of the present review methodology is depicted in Figure 3 , based on the PRISMA guidelines [ 75 ], along with information about at which stage each exclusive criterion was imposed similarly to recent systematic review studies such as [ 72 , 76 , 77 , 78 ].

The flowchart of the methodology of the present systematic review along with the flow of information regarding the exclusive criteria, based on PRISMA guidelines [ 75 ].

3.2. Definition of the Performance Metrics Commonly Used in the Reviewed Studies

In this subsection, the most commonly used performance metrics of the reviewed papers are briefly described. In general, these metrics are utilized in an effort to provide a common measure to evaluate the ML algorithms. The selection of the appropriate metrics is very important, since: (a) how the algorithm’s performance is measured relies on these metrics and (b) the metric itself can influence the way the significance of several characteristics is weighted.

Confusion matrix constitutes one of the most intuitive metrics towards finding the correctness of a model. It is used for classification problems, where the result can be of at least two types of classes. Let us consider a simple example, by giving a label to a target variable: for example, “1” when a plant has been infected with a disease and “0” otherwise. In this simplified case, the confusion matrix ( Figure 4 ) is a 2 × 2 table having two dimensions, namely “Actual” and “Predicted”, while its dimensions have the outcome of the comparison between the predictions with the actual class label. Concerning the above simplified example, this outcome can acquire the following values:

• True Positive (TP): The plant has a disease (1) and the model classifies this case as diseased (1);
• True Negative (TN): The plant does not have a disease (0) and the model classifies this case as a healthy plant (0);
• False Positive (FP): The plant does not have a disease (0), but the model classifies this case as diseased (1);
• False Negative (FN): The plant has a disease (1), but the model classifies this case as a healthy plant (0).

Representative illustration of a simplified confusion matrix.

As can be shown in Table 1 , the aforementioned values can be implemented in order to estimate the performance metrics, typically observed in classification problems [ 79 ].

Summary of the most commonly used evaluation metrics of the reviewed studies.

Other common evaluation metrics were the coefficient of correlation ( R ), coefficient of determination ( R 2 ; basically, the square of the correlation coefficient), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), which can be given via the following relationships [ 80 , 81 ]:

where X t and Z t correspond to the predicted and real value, respectively, t stands for the iteration at each point, while T for the testing records number. Accordingly, low values of MAE, MAPE, and MSE values denote a small error and, hence, better performance. In contrast, R 2 near 1 is desired, which demonstrates better model performance and also that the regression curve efficiently fits the data.

4.1. Preliminary Data Visualization Analysis

Graphical representation of data related to the reviewed studies, by using maps, bar or pie charts, for example, can provide an efficient approach to demonstrate and interpret the patterns of data. The data visualization analysis, as it usually refers to, can be vital in the context of analyzing large amounts of data and has gained remarkable attention in the past few years, including review studies. Indicatively, significant results can be deduced in an effort to identify: (a) the most contributing authors and organizations, (b) the most contributing international journals (or equivalently which research fields are interested in this topic), and (c) the current trends in this field [ 82 ].

4.1.1. Classification of the Studies in Terms of Application Domain

As can be seen in the flowchart of the present methodology ( Figure 3 ), the literature survey on ML in agriculture resulted in 338 journal papers. Subsequently, these studies were classified into the four generic categories as well as into their sub-categories, as already mentioned above. Figure 5 depicts the aforementioned papers’ distribution. In particular, the majority of the studies were intended for crop management (68%), while soil management (10%), water management (10%), and livestock management (12% in total; animal welfare: 7% and livestock production: 5%) had almost equal contribution in the present bibliographic survey. Focusing on crop management, the most contributing sub-categories were yield prediction (20%) and disease detection (19%). The former research field arises as a consequence of the increasing interest of farmers in taking decisions based on efficient management that can lead to the desired yield. Disease detection, on the other hand, is also very important, as diseases constitute a primary menace for food security and quality assurance. Equal percentages (13%) were observed for weed detection and crop recognition, both of which are essential in crop management at farm and agricultural policy making level. Finally, examination of crop quality was relatively scarce corresponding to 3% of all studies. This can be attributed to the complexity of monitoring and modeling the quality-related parameters.

The classification of the reviewed studies according to the field of application.

In this fashion, it should be mentioned again that all the selected journal papers are summarized in Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 , depending on their field of application, and presented in the Appendix A . The columns of the tables correspond (from left to right) to the “Reference number” (Ref), “Input Data”, “Functionality”, “Models/Algorithms”, and “Best Output”. One additional column exists for the sub-categories belonging in crop management, namely “Crop”, whereas the corresponding column in the sub-categories pertaining to livestock management refers to “Animal”. The present systematic review deals with a plethora of different ML models and algorithms. For the sake of brevity, the commonly used abbreviations are used instead of the entire names, which are summarized in Table A10 and Table A11 (presented also in the Appendix A ). The list of the aforementioned Tables, along with their content, is listed in Table 2 .

List of the tables appearing in the Appendix A related to: (a) the categories and sub-categories of the machine learning applications in agriculture ( Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 ) and (b) the abbreviations of machine learning models and algorithms ( Table A10 and Table A11 , respectively).

4.1.2. Geographical Distribution of the Contributing Organizations

The subject of this sub-section is to find out the geographical distribution of all the contributing organizations in ML applications in agriculture. To that end, the author’s affiliation was taken into account. In case a paper included more than one author, which was the most frequent scenario, each country could contribute only once in the final map chart ( Figure 6 ), similarly to [ 83 , 84 ]. As can be gleaned from Figure 6 , investigating ML in agriculture is distributed worldwide, including both developed and developing economies. Remarkably, out of the 55 contributing countries, the least contribution originated from African countries (3%), whereas the major contribution came from Asian countries (55%). The latter result is attributed mainly to the considerable contribution of Chinese (24.9%) as well as Indian organizations (10.1%). USA appeared to be the second most contributing country with 20.7% percentage, while Australia (9.5%), Spain (6.8%), Germany (5.9%), Brazil, UK, and Iran (5.62%) seem to be particularly interested in ML in agriculture. It should be stressed that livestock management, which is a relatively different sub-field comparing to crop, water, and soil management, was primary examined from studies coming from Australia, USA, China, and UK, while all the papers regarding Ireland were focused on animals. Finally, another noteworthy observation is that a large number of articles were a result of international collaboration, with the synergy of China and USA standing out.

Geographical distribution of the contribution of each country to the research field focusing on machine learning in agriculture.

4.1.3. Distribution of the Most Contributing Journal Papers

For the purpose of identifying the research areas that are mostly interested in ML in agriculture, the most frequently appeared international journal papers are depicted in Figure 7 . In total, there were 129 relevant journals. However, in this bar chart, only the journals contributing with at least 4 papers are presented for brevity. As a general remark, remote sensing was of particular importance, since reliable data from satellites and UAV, for instance, constitute valuable input data for the ML algorithms. In addition, smart farming, environment, and agricultural sustainability were of central interest. Journals associated with computational techniques were also presented with considerable frequency. A typical example of such type of journals, which was presented in the majority of the studies with a percentage of 19.8%, was “ Computers and Electronics in Agriculture ”. This journal aims at providing the advances in relation to the application of computers and electronic systems for solving problems in plant and animal production.

Distribution of the most contributing international journals (published at least four articles) concerning applications of machine learning in agriculture.

The “ Remote Sensing ” and “ Sensors ” journals followed with approximately 11.8% and 6.5% of the total number of publications, respectively. These are cross-sectoral journals that are concentrated on applications of science and sensing technologies in various fields, including agriculture. Other journals, covering this research field, were also “ IEEE Access ” and “ International Journal of Remote Sensing ” with approximately 2.1% and 1.2% contribution, respectively. Moreover, agriculture-oriented journals were also presented in Figure 7 , including “ Precision Agriculture ”, “ Frontiers in Plant Science ”, “ Agricultural and Forest Meteorology ”, and “ Agricultural Water Management ” with 1–3% percentage. These journals deal with several aspects of agriculture ranging from management strategies (so as to incorporate spatial and temporal data as a means of optimizing productivity, resource use efficiency, sustainability and profitability of agricultural production) up to crop molecular genetics and plant pathogens. An interdisciplinary journal concentrating on soil functions and processes also appeared with 2.1%, namely “ Geoderma ”, plausibly covering the soil management generic category. Finally, several journals focusing on physics and applied natural sciences, such as “ Applied Sciences ” (2.7%), “ Scientific Reports ” (1.8%), “ Biosystems Engineering ” (1.5%), and “ PLOS ONE ” (1.5%), had a notable contribution to ML studies. As a consequence, ML in agriculture concerns several disciplines and constitutes a fundamental area for developing various techniques, which can be beneficial to other fields as well.

4.2. Synopsis of the Main Features Associated with the Relative Literature

4.2.1. machine learning models providing the best results.

A wide range of ML algorithms was implemented in the selected studies; their abbreviations are given in Table A11 . The ML algorithms that were used by each study as well as those that provided the best output have been listed in the last two columns of Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 . These algorithms can be classified into the eight broad families of ML models, which are summarized in Table A10 . Figure 8 focuses on the best performed ML models as a means of capturing a broad picture of the current situation and demonstrating advancement similarly to [ 12 ].

Machine Learning models giving the best output.

As can be demonstrated in Figure 8 , the most frequent ML model providing the best output was, by far, Artificial Neural Networks (ANNs), which appeared in almost half of the reviewed studies (namely, 51.8%). More specifically, ANN models provided the best results in the majority of the studies concerning all sub-categories. ANNs have been inspired by the biological neural networks that comprise human brains [ 85 ], while they allow for learning via examples from representative data describing a physical phenomenon. A distinct characteristic of ANNs is that they can develop relationships between dependent and independent variables, and thus extract useful information from representative datasets. ANN models can offer several benefits, such as their ability to handle noisy data [ 86 ], a situation that is very common in agricultural measurements. Among the most popular ANNs are the Deep Neural Networks (DNNs), which utilize multiple hidden layers between input and output layers. DNNs can be unsupervised, semi-supervised, or supervised. A usual kind of DNNs are the Convolutional Neural Networks (CNNs), whose layers, unlike common neural networks, can set up neurons in three dimensions [ 87 ]. In fact, CNNs were presented as the algorithms that provide the best output in all sub-categories, with an almost 50% of the individual percentage of ANNs. As stressed in recent studies, such as that of Yang et al. [ 88 ], CNNs are receiving more and more attention because of their efficient results when it comes to detection through images’ processing.

Recurrent Neural Networks (RNNs) followed, representing approximately 10% of ANNs, with Long Short-Term Memory (LSTM) standing out. They are called “recurrent” as they carry out the same process for every element, with the previous computations determining the current output, while they have a “memory” that stores information pertaining to what has been calculated so far. RNNs can face problems concerning vanishing gradients and inability to “memorize” many sequential data. Towards addressing these issues, the cell structures of LSTM can control which part of information will be either stored in long memory or discarded, resulting in optimization of the memorizing process [ 51 ]. Moreover, Multi-Layer Perceptron (MLP), Fully Convolutional Networks (FCNs), and Radial Basis Function Networks (RBFNs) appeared to have the best performance in almost 3–5% of ANNs. Finally, ML algorithms, belonging to ANNs with low frequency, were Back-Propagation Neural Networks (BPNNs), Modular Artificial Neural Networks (MANNs), Deep Belief Networks (DBNs), Adaptive-Neuro Fuzzy Inference System (ANFIS), Subtractive Clustering Fuzzy Inference System (SCFIS), Takagi-Sugeno Fuzzy Neural Networks (TS-FNN), and Feed Forward Neural Networks (FFNNs).

The second most accurate ML model was Ensemble Learning (EL), contributing to the ML models used in agricultural systems with approximately 22.2%. EL is a concise term for methods that integrate multiple inducers for the purpose of making a decision, normally in supervised ML tasks. An inducer is an algorithm, which gets as an input a number of labeled examples and creates a model that can generalize these examples. Thus, predictions can be made for a set of new unlabeled examples. The key feature of EL is that via combining various models, the errors coming from a single inducer is likely to be compensated from other inducers. Accordingly, the prediction of the overall performance would be superior comparing to a single inducer [ 89 ]. This type of ML model was presented in all sub-categories, apart from crop quality, perhaps owing to the small number of papers belonging in this subcategory. Support Vector Machine (SVM) followed, contributing in approximately 11.5% of the studies. The strength of the SVM stems from its capability to accurately learn data patterns while showing reproducibility. Despite the fact that it can also be applied for regression applications, SVM is a commonly used methodology for classification extending across numerous data science settings [ 90 ], including agricultural research.

Decision Trees (DT) and Regression models came next with equal percentage, namely 4.7%. Both these ML models were presented in all generic categories. As far as DT are concerned, they are either regression or classification models structured in a tree-like architecture. Interestingly, handling missing data in DT is a well-established problem. By implementing DT, the dataset can be gradually organized into smaller subsets, whereas, in parallel, a tree graph is created. In particular, each tree’s node denotes a dissimilar pairwise comparison regarding a certain feature, while each branch corresponds to the result of this comparison. As regards leaf nodes, they stand for the final decision/prediction provided after following a certain rule [ 91 , 92 ]. As for Regression, it is used for supervised learning models intending to model a target value on the basis of independent predictors. In particular, the output can be any number based on what it predicts. Regression is typically applied for time series modeling, prediction, and defining the relationships between the variables.

Finally, the ML models, leading to optimal performance (although with lower contribution to literature), were those of Instance Based Models (IBM) (2.7%), Dimensionality Reduction (DR) (1.5%), Bayesian Models (BM) (0.9%), and Clustering (0.3%). IBM appeared only in crop, water, and livestock management, whereas BM only in crop and soil management. On the other hand, DR and Clustering appeared as the best solution only in crop management. In brief, IBM are memory-based ML models that can learn through comparison of the new instances with examples within the training database. DR can be executed both in unsupervised and supervised learning types, while it is typically carried out in advance of classification/regression so as to prevent dimensionality effects. Concerning the case of BM, they are a family of probabilistic models whose analysis is performed within the Bayesian inference framework. BM can be implemented in both classification and regression problems and belong to the broad category of supervised learning. Finally, Clustering belongs to unsupervised ML models. It contains automatically discovering of natural grouping of data [ 12 ].

4.2.2. Most Studied Crops and Animals

In this sub-section, the most examined crops and animals that were used in the ML models are discussed as a result of our searching within the four sub-categories of crop management similarly to [ 12 ]. These sub-categories refer to yield prediction, disease detection, crop recognition, and crop quality. Overall, approximately 80 different crop species were investigated. The 10 most utilized crops are summarized in Figure 9 . Specifically, the remarkable interest on maize (also known as corn) can be attributed to the fact that it is cultivated in many parts across the globe as well as its versatile usage (for example, direct consumption by humans, animal feed, producing ethanol, and other biofuels). Wheat and rice follow, which are two of the most widely consumed cereal grains. According to the Food and Agriculture Organization (FAO) [ 93 ], the trade in wheat worldwide is more than the summation of all other crops. Concerning rice, it is the cereal grain with the third-highest production and constitutes the most consumed staple food in Asia [ 94 ]. The large contribution of Asian countries presented in Figure 6 , like China and India, justifies the interest in this crop. In the same vein, soybeans, which are broadly distributed in East Asia, USA, Africa, and Australia [ 95 ], were presented in many studies. Finally, tomato, grape, canola/rapeseed (cultivated primarily for its oil-rich seed), potato, cotton, and barley complete the top 10 examined crops. All these species are widely cultivated all over the world. Some other indicative species, which were investigated at least five times in the present reviewed studies, were also alfalfa, citrus, sunflower, pepper, pea, apple, squash, sugarcane, and rye.

The 10 most investigated crops using machine learning models; the results refer to crop management.

As far as livestock management is concerned, the examined animal species can be classified, in descending order of frequency, into the categories of cattle (58.5%), sheep and goats (26.8%), swine (14.6%), poultry (4.9%), and sheepdog (2.4%). As can be depicted in Figure 10 , the last animal, which is historically utilized with regard to the raising of sheep, was investigated only in one study belonging to animal welfare, whereas all the other animals were examined in both categories of livestock management. In particular, the most investigated animal in both animal welfare and livestock production was cattle. Sheep and goats came next, which included nine studies for sheep and two studies for goats. Cattles are usually raised as livestock aimed at meat, milk, and hide used for leather. Similarly, sheep are raised for meat and milk as well as fleece. Finally, swine (often called domestic pigs) and poultry (for example, chicken, turkey, and duck), which are used mainly for their meat or eggs (poultry), had equal contribution from the two livestock sub-categories.

Frequency of animal species in studies concerning livestock management by using machine learning models.

4.2.3. Most Studied Features and Technologies

As mentioned in the beginning of this study, modern agriculture has to incorporate large amounts of heterogeneous data, which have originated from a variety of sensors over large areas at various spatial scale and resolution. Subsequently, such data are used as input into ML algorithms for their iterative learning up until modeling of the process in the most effective way possible. Figure 11 shows the features and technologies that were used in the reviewed studies, separately for each category, for the sake of better comprehending the results of the analysis.

Distribution of the most usual features implemented as input data in the machine learning algorithms for each category/sub-category.

Data coming from remote sensing were the most common in the yield prediction sub-category. Remote sensing, in turn, was primarily based on data derived from satellites (40.6% of the total studies published in this sub-category) and, secondarily, from UAVs (23.2% of the total studies published in this sub-category). A remarkable observation is the rapid increase of the usage of UAVs versus satellites from the year 2018 towards 2020, as UAVs seem to be a reliable alternative that can give faster and cheaper results, usually in higher resolution and independent of the weather conditions. Therefore, UAVs allow for discriminating details of localized circumscribed regions that the satellites’ lowest resolution may miss, especially under cloudy conditions. This explosion in the use of UAV systems in agriculture is a result of the developing market of drones and sensing solutions attached to them, rendering them economically affordable. In addition, the establishment of formal regulations for UAV operations and the simplification and automatization of the operational and analysis processes had a significant contribution on the increasing popularity of these systems. Data pertaining to the weather conditions of the investigated area were also of great importance as well as soil parameters of the farm at hand. An additional way of getting the data was via in situ manual measurements, involving measurements such as crop height, plant growth, and crop maturity. Finally, data concerning topographic, irrigation, and fertilization aspects were presented with approximately equal frequency.

As far as disease detection is concerned, Red-Green-Blue (RGB) images appear to be the most usual input data for the ML algorithms (in 62% of the publications). Normally, deep learning methods like CNNs are implemented with the intention of training a classifier to discriminate images depicting healthy leaves, for example, from infected ones. CNNs use some particular operations to transform the RGB images so that the desired features are enhanced. Subsequently, higher weights are given to the images having the most suitable features. This characteristic constitutes a significant advantage of CNNs as compared to other ML algorithms, when it comes to image classification [ 79 ]. The second most common input data came from either multispectral or hyperspectral measurements originated from spectroradiometers, UAVs, and satellites. Concerning the investigated diseases, fungal diseases were the most common ones with diseases from bacteria following, as is illustrated in Figure 12 a. This kind of disease can cause major problems in agriculture with detrimental economic consequences [ 96 ]. Other examined origins of crop diseases were, in descending order of frequency, pests, viruses, toxicity, and deficiencies.

Distribution of the most usual output features of the machine learning algorithms regarding: ( a ) Disease detection and ( b ) Crop quality.

Images were also the most used input data for weed detection purposes. These images were RGB images that originated mainly from in situ measurements as well as from UGVs and UAVs and, secondarily, multispectral images from the aforementioned sources. Finally, other parameters that were observed, although with lower frequency, were satellite multispectral images, mainly due to the considerably low resolution they provide, video recordings, and hyperspectral and greyscale images. Concerning crop recognition, the majority of the studies used data coming mostly from satellites and, secondarily, from in situ manual measurements. This is attributed to the fact that most of the studies in this category concern crop classification, a sector where satellite imaging is the most widely used data source owing to its potential for analysis of time series of extremely large surfaces of cultivated land. Laboratory measurements followed, while RGB and greyscale images as well as hyperspectral and multispectral measurements from UAVs were observed with lower incidence.

The input data pertaining to crop quality consisted mainly of RGB images, while X-ray images were also utilized (for seed germination monitoring). Additionally, quality parameters, such as color, mass, and flesh firmness, were used. There were also two studies using spectral data either from satellites or spectroradiometers. In general, the studies belonging in this sub-category dealt with either crop quality (80%) or seed germination potential (20%) ( Figure 12 b). The latter refers to the seed quality assessment that is essential for the seed production industry. Two studies were found about germination that both combined X-ray images analysis and ML.

Concerning soil management, various soil properties were taken into account in 65.7% of the studies. These properties included salinity, organic matter content, and electrical conductivity of soil and soil organic carbon. Usage of weather data was also very common (in 48.6% of the studies), while topographic and data pertaining to the soil moisture content (namely the ratio of the water mass over the dry soil) and crop properties were presented with lower frequency. Additionally, remote sensing, including satellite and UAV multispectral and hyperspectral data, as well as proximal sensing, to a lesser extent, were very frequent choices (in 40% of the studies). Finally, properties associated with soil temperature, land type, land cover, root microbial dynamics, and groundwater salinity make up the rest of data, which are labeled as “other” in the corresponding graph of Figure 11 .

In water management, weather data stood for the most common input data (appeared in the 75% of the studies), with ET being used in the vast majority of them. In many cases, accurate estimation of ET (the summation of the transpiration via the plant canopy and the evaporation from plant, soil, and open water surface) is among the most central elements of hydrologic cycle for optimal management of water resources [ 97 ]. Data from remote sensors and measurements of soil water content were also broadly used in this category. Soil water availability has a central impact on crops’ root growth by affecting soil aeration and nutrient availability [ 98 ]. Stem water potential, appearing in three studies, is actually a measure of water tension within the xylem of the plant, therefore functioning as an indicator of the crop’s water status. Furthermore, in situ measurements, soil, and other parameters related to cumulative water infiltration, soil and water quality, field topography, and crop yield were also used, as can be seen in Figure 11 .

Finally, in what concerns livestock management, motion capture sensors, including accelerometers, gyroscopes, and pedometers, were the most common devices giving information about the daily activities of animals. This kind of sensors was used solely in the studies investigating animal welfare. Images, audio, and video recordings came next, however, appearing in both animal welfare and livestock production sub-categories. Physical and growth characteristics followed, with slightly less incidence, by appearing mainly in livestock production sub-category. These characteristics included the animal’s weight, gender, age, metabolites, biometric traits, backfat and muscle thickness, and heat stress. The final characteristic may have detrimental consequences in livestock health and product quality [ 99 ], while through the measurement of backfat and muscle thickness, estimations of the carcass lean yield can be made [ 100 ].

5. Discussion and Main Conclusions

The present systematic review study deals with ML in agriculture, an ever-increasing topic worldwide. To that end, a comprehensive analysis of the present status was conducted concerning the four generic categories that had been identified in the previous review by Liakos et al. [ 12 ]. These categories pertain to crop, water, soil, and livestock management. Thus, by reviewing the relative literature of the last three years (2018–2020), several aspects were analyzed on the basis of an integrated approach. In summary, the following main conclusions can be drawn:

• The majority of the journal papers focused on crop management, whereas the other three generic categories contributed almost with equal percentage. Considering the review paper of [ 12 ] as a reference study, it can be deduced that the above picture remains, more or less, the same, with the only difference being the decrease of the percentage of the articles regarding livestock from 19% to 12% in favor of those referring to crop management. Nonetheless, this reveals just one side of the coin. Taking into account the tremendous increase in the number of relative papers published within the last three years (in particular, 40 articles were identified in [ 12 ] comparing to the 338 of the present literature survey), approximately 400% more publications were found on livestock management. Another important finding was the increasing research interest on crop recognition.
• Several ML algorithms have been developed for the purpose of handling the heterogeneous data coming from agricultural fields. These algorithms can be classified in families of ML models. Similar to [ 12 ], the most efficient ML models proved to be ANNs. Nevertheless, in contrast to [ 12 ], the interest also been shifted towards EL, which can combine the predictions that originated from more than one model. SVM completes the group with the three most accurate ML models in agriculture, due to some advantages, such as its high performance when it works with image data [ 101 ].
• As far as the most investigated crops are concerned, mainly maize and, secondarily, wheat, rice, and soybean were widely studied by using ML. In livestock management, cattle along with sheep and goats stood out constituting almost 85% of the studies. Comparing to [ 12 ], more species have been included, while wheat and rice as well as cattle, remain important specimens for ML applications.
• A very important result of the present review study was the demonstration of the input data used in the ML algorithms and the corresponding sensors. RGB images constituted the most common choice, thus, justifying the broad usage of CNNs due to their ability to handle this type of data more efficiently. Moreover, a wide range of parameters pertaining to weather as well as soil, water, and crop quality was used. The most common means of acquiring measurements for ML applications was remote sensing, including imaging from satellites, UAVs and UGVs, while in situ and laboratory measurements were also used. As highlighted above, UAVs are constantly gaining ground against satellites mainly because of their flexibility and ability to provide images with high resolution under any weather conditions. Satellites, on the other hand, can supply time-series over large areas [ 102 ]. Finally, animal welfare-related studies used mainly devices such as accelerometers for activity recognition, whereas those ones referring to livestock production utilized primary physical and growth characteristics of the animal.

As can be inferred from the geographical distribution (illustrated in Figure 6 ) in tandem with the broad spectrum of research fields, ML applications for facilitating various aspects of management in the agricultural sector is an important issue on an international scale. As a matter of fact, its versatile nature favors convergence research. Convergence research is a relatively recently introduced approach that is based on shared knowledge between different research fields and can have a positive impact on the society. This can refer to several aspects, including improvement of the environmental footprint and assuring human’s health. Towards this direction, ML in agriculture has a considerable potential to create value.

Another noteworthy finding of the present analysis is the capturing of the increasing interest on topics concerning ML analyses in agricultural applications. More specifically, as can be shown in Figure 13 , an approximately 26% increase was presented in the total number of the relevant studies, if a comparison is made between 2018 and 2019. The next year (i.e., 2020), the corresponding increase jumped to 109% against 2019 findings; thus, resulting in an overall 164% rise comparing with 2018. The accelerating rate of the research interest on ML in agriculture is a consequence of various factors, following the considerable advancements of ICT systems in agriculture. Moreover, there exists a vital need for increasing the efficiency of agricultural practices while reducing the environmental burden. This calls for both reliable measurements and handling of large volumes of data as a means of providing a wide overview of the processes taking place in agriculture. The currently observed technological outbreak has a great potential to strengthen agriculture in the direction of enhancing food security and responding to the rising consumers’ demands.

Temporal distribution of the reviewed studies focusing on machine learning in agriculture, which were published within 2018–2020.

In a nutshell, ICT in combination with ML, seem to constitute one of our best hopes to meet the emerging challenges. Taking into account the rate of today’s data accumulation along with the advancement of various technologies, farms will certainly need to advance their management practices by adopting Decision Support Systems (DSSs) tailored to the needs of each cultivation system. These DSSs use algorithms, which have the ability to work on a wider set of cases by considering a vast amount of data and parameters that the farmers would be impossible to handle. However, the majority of ICT necessitates upfront costs to be paid, namely the high infrastructure investment costs that frequently prevent farmers from adopting these technologies. This is going to be a pressing issue, mainly in developing economies, where agriculture is an essential economic factor. Nevertheless, having a tangible impact is a long-haul game. A different mentality is required by all stakeholders so as to learn new skills, be aware of the potential profits of handling big data, and assert sufficient funding. Overall, considering the constantly increasing recognition of the value of artificial intelligence in agriculture, ML will definitely become a behind-the-scenes enabler for the establishment of a sustainable and more productive agriculture. It is anticipated that the present systematic effort is going to constitute a beneficial guide to researchers, manufacturers, engineers, ICT system developers, policymakers, and farmers and, consequently, contribute towards a more systematic research on ML in agriculture.

In this section, the reviewed articles are summarized within the corresponding Tables as described in Table 2 .

Crop Management: Yield Prediction.

Acc: Accuracy: CA: Conservation Agriculture; CI: Crop Indices; CEC: Cation Exchange Capacity; CCC: Concordance Correlation Coefficient; DOY: Day Of Year; EC: Electrical Conductivity; HD: Heading Date; HDM: Heading Date to Maturity; K: Potassium; Mg: Magnesium; N: Nitrogen; OLI: Operational Land Imager; P: Phosphorus; RGB: Red-Green-Blue; S: Sulphur; SOM: Soil Organic Matter; SPAD: Soil and Plant Analyzer Development; STI: Soil Texture Information; STD: Standard Deviation; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.

Crop Management: Disease Detection.

Acc: Accuracy; AUC: Area Under Curve; CR: Cedar Rust; ExGR: Excess Green Minus Excess Red; FS: Frogeye Spot; H: Healthy; mAP: mean Average Precision; RGB: Red-Green-Blue; S: Scab; TYLC: Tomato Yellow Leaf Curl; UAV: Unmanned Aerial Vehicle; VddNet: Vine Disease Detection Network.

Crop Management: Weed Detection.

Acc: Accuracy; AUC: Area under Curve; IoU: Intersection over Union; mAP: mean Average Precision; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.

Crop Management: Crop Recognition.

Acc: Accuracy; IoU: Intersection over Union; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle.

Crop Management: Crop Quality.

Acc: Accuracy; DSM: Detection and Segmentation Module; EDG: Estimated Dimensions Geometry; IVTD: In Vitro True Digestibility; RGB; Red-Green-Blue; MMD: Manually Measured Dimensions; mAP: mean Average Precision; PSO: Particle Swarm Optimization; RGB; Red-Green-Blue; SAE: Stacked AutoEncoder; VI: Vegetation Indices; WF: Wavelet Features.

Water management.

Acc: Accuracy; CC: Coefficient of Correlation; ET: Evapotranspiration; ET o : reference EvapoTranspiration; ROC: Receiver Operating Characteristic; ME: Model Efficiency; NSE: Nash-Sutcliffe model efficiency Coefficient; POD: Probability Of Detection.

Soil management.

ACCA: Aminoyclopropane-1-carboxylate; AUC: Area Under Curve; BP: Bacterial Population; CC: Coefficient of Correlation; CCC: Concordance Correlation Coefficient; CCE: Calcium Carbonate Equivalent; ET: EvaporoTransporation; MIR: Mid InfraRed; NSE: Nash-Sutcliffe model efficiency Coefficient; NIR: Near-InfraRed; PS: Phosphate Solubilization; PWP: Permanent Wilting Point; RPIQ: Ratio of Performance to Interquartile Range; RPD: Relative Percent Deviation; SOC: Soil Organic Carbon; WI: Willmott’s Index.

Livestock Management: Animal Welfare.

AUC: Area Under Curve; Cont: Contagious; DE: Digestible Energy; ED: Energy Digestibility; ENV: Environmental; DWT: Discrete Wavelet Transform; MFCCs: Mel-Frequency Cepstral Coefficients; NIR: Near InfraRed; NPV: Negative Predictive Value; PTZ: Pan-Tilt-Zoom; PPV: Positive Predictive Value; RGB: Red-Green-Blue; RR: Respiration Rate; ST: Skin Temperature.

Livestock Management: Livestock Production.

ACFW: Adult Clean Fleece Weight; ADG: Average Daily Gain; AFD: Adult Fibre Diameter; AGFW: Adult Greasy Fleece Weight; ASL: Adult Staple Length; ASS: Adult Staple Strength; BBFT: Bacon/BackFat Thickness; BCS: Body Condition Score; CCW: Cold Carcass Weights; CTLEAN: Computed Tomography Lean Meat Yield; DBT: Deep Body Temperature; EMA: Eye Muscle Area; GWAS: Genome-Wide Association Studies; GRFAT: Greville Rule Fat Depth; HER: Human Error Range; IMF: IntraMuscular Fat; HCW: Hot Carcass Weight; LW: Loin Weight; MS: Marbling Score; MT: Muscle Thickness; REIMS: Rapid Evaporative Ionization Mass Spectrometry; RGB: Red-Green-Blue; SMY: Saleable Meat Yield.

Abbreviations for machine learning models.

Abbreviations for machine learning algorithms.

Author Contributions

Conceptualization, D.B.; methodology, L.B., G.D., R.B., D.K. and A.C.T.; investigation, L.B. and G.D.; writing—original draft preparation, L.B. and A.C.T.; writing—review and editing, L.B., G.D., D.K., A.C.T., R.B. and D.B.; visualization, L.B.; supervision, D.B. All authors have read and agreed to the published version of the manuscript.

This work has been partly supported by the Project “BioCircular: Bio-production System for Circular Precision Farming” (project code: T1EDK- 03987) co-financed by the European Union and the Greek national funds through the Operational Programme Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The Future of Farming: Artificial Intelligence and Agriculture

While artificial intelligence (AI) seemed until recently to be science fiction, countless corporations across the globe are now researching ways to implement this technology in everyday life. AI works by processing large quantities of data, interpreting patterns in that data, and then translating these interpretations into actions that resemble those of a human being. Scientists have used it to develop self-driving cars and chess-playing computers, but the technology has expanded into another domain: agriculture. AI has the potential to spur more efficient methods of farming in order to combat global warming, but only with expanded regulation of its development.

Global Warming and Agriculture: A Vicious Cycle

Global warming continues to threaten every aspect of our everyday lives, including crop production. It will reduce the soil moisture in areas close to the equator while leaving northern countries virtually unscathed, according to a study from Wageningen University. We are already seeing the impact of these modified growing conditions on our food production in the form of lower crop yields .

Reduced food production has an especially devastating impact on developing countries. Climate change causes the loss of 35 trillion consumable food calories per year and harms poorer countries who do not have the money to import food. The result is growing food insecurity. And rising sea levels only compound the problem. By the year 2100, sea levels are expected to rise by one meter, which will have a detrimental impact on growers on the coasts whose crops cannot survive in areas where the water is too salty.

However, agriculture is not just a victim of global warming, but also a cause. Agriculture is part of a vicious cycle in which farming leads to global warming, which in turn devastates agricultural production. The process of clearing land for agriculture results in widespread deforestation and contributes to 40 percent of global methane production. Therefore, to confront climate change, it is necessary to ensure reforestation—but how? What is the path to efficient, environmentally-conscious farming?

The Benefits of AI for Environmentally-Conscious Agriculture

This is where AI enters the scene. Farmers use AI for methods such as precision agriculture ; they can monitor crop moisture, soil composition, and temperature in growing areas, enabling farmers to increase their yields by learning how to take care of their crops and determine the ideal amount of water or fertilizer to use.

Furthermore, this technology may have the capacity to reduce deforestation by allowing humans to grow food in urban areas. One Israeli tech company used AI algorithms that create optimal light and water conditions to grow crops in a container small enough to be stored inside a  home. The technology could be especially beneficial for countries in Latin America and the Caribbean, where much of the population lives in cities. Furthermore, the ability to grow food in pre-existing urban areas suggests that humans could become less dependent on deforestation for food production.

Additionally, AI can help locate and therefore protect carbon sinks , forest areas that absorb carbon dioxide from the atmosphere. Otherwise, continued efforts to clear these forests will release more carbon dioxide into the atmosphere. Furthermore, some AI is being developed that can find and target weeds in a field with the appropriate amount of herbicide, eliminating the need for farmers to spread chemicals across entire fields and pollute the surrounding ecosystem. Some countries are already implementing AI into their agricultural  methods. Some farmers in Argentina are already using digital agriculture; there are already AI farms in China .

AI can also be used to curb global warming outside of agriculture. The technology can be used to monitor how efficiently buildings are using energy and monitor urban heat islands. Urban heat islands are first created when urban building materials like concrete and asphalt absorb heat, causing cities to grow hotter than the rest of their surroundings. People then rely more heavily on air conditioning throughout the day in order to stay cool, and the energy used for these services results in greater greenhouse gas emissions. Providing information about the location of these islands could help politicians determine what policies they should adopt to reduce emissions and encourage more efficient and environmentally-conscious city planning.

The Risks of AI

Nonetheless, AI is far from a silver bullet—it could actually contribute to global warming. Due to the large amount of data that AI needs to process, training a single AI releases five times the emissions that an average car would emit during its lifetime, thereby adding to the already substantial environmental impact of computing technology. Data storage and processing centers that deliver digital services like entertainment and cloud computing are already responsible for two percent of global greenhouse gas emissions, a number comparable to the overall percentage of pollution contributed by the aviation industry. Although this statistic may not seem overwhelming, it does suggest that the environmental costs of AI will need to be reduced before expanding the technology on a global scale. Some researchers are already working on developing a standard metric that researchers can use to compare how efficient their particular AI systems might be, ultimately encouraging innovators to create environmentally-friendly data-processing.

Further, securing access to AI on a global scale may pose some challenges. Countries will both need experts in the field who can successfully use the technology and internet connection, neither of which are always readily available. Therefore, in order for developing countries to take advantage of the benefits of AI and improve their food security, there will need to be a focus on developing the infrastructure necessary for internet access and teaching professionals how to use the technology. Additionally, AI can be expensive . Farmers might go into debt and will not be able to maintain the technology on their own as it suffers everyday wear-and-tear. Those unable to secure access to the technology will lose out to larger farms that can implement AI on a wide scale.

But farm owners themselves will not be the only ones faced with new pressures as a result of AI. New technologies will render many agricultural jobs obsolete as machines are able to accomplish the same tasks as humans. For example, China has created a seven-year pilot program that uses robots instead of humans to run farms. This program does not bode well for the future of jobs in agriculture: many of China’s 250 million farmers could lose their jobs due to increased automation.

Some may argue that the rise of automated jobs is not as threatening as it may seem, especially given the US agricultural labor shortage . However, the situation is not necessarily the same in other countries. Many countries in the Global South remain dependent on the agricultural sector because there are few job opportunities in urban areas. But if farmers can produce more food at a faster rate with machines, they will have an incentive to shift away from hiring humans, placing the livelihoods of many families at risk. Even if farmworkers do not lose their jobs, their wages could decline as they appear less efficient compared to their robot competitors. The result is chronic poverty and inequality.

Looking Forward: The Next Steps for AI in Agriculture

Given these concerns, AI cannot be the only response to climate change. These types of adaptive technologies can mitigate the consequences of climate change, but more sweeping measures are necessary to secure global access to food in the face of rising temperatures. If countries are to develop AI for use in agricultural sectors, global leaders must consider the potential costs, the role of legal institutions, and the environmental consequences of data processing before investing in the technology on a broader scale.

Sydney Young

Sydney is the former Director of Interviews and Perspectives at the HIR. She is interested in health, human rights, and social justice issues.

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