Table A1 Systematic analysis of the main...

Table A1

Systematic analysis of the main contributions

Authors	Topic	Methodology					Touchpoints				Cluster
		Methodology					AI technologies for external stakeholders	AI technologies for sustainability	AI for decision support systems and entrepreneurship	AI technologies for efficiency, productivity and profitability
		Mixed Method	Qualitative	Literature Review	Conceptual	Quantitative	AI technologies for external stakeholders	AI technologies for sustainability	AI for decision support systems and entrepreneurship
Abban and Abebe (2022)	The literature review investigates the importance of digitalization and sustainability in African agribusinesses and food supply chains and highlight their potential to overcome challenges in the sector. The authors point out the main technologies employed in agriculture and supply chain management and highlight several sustainable practices			X			X	X			A
Agyemang et al. (2022)	This study examines food system frameworks in relation to sustainability aspects, constructs future scenarios, and develops a decision support system that helps stakeholders make strategic decisions and take pre-emptive actions to address potential challenges and uncertainties in the food system	X						X	X		E
Ahmed and Hussain (2022)	The authors suggest a forecasting model to predict wheat production using machine learning. After analysis, the model proposed is proven to be effective and thus could be replicable on other crops and regions		X							X	B
Alam et al. (2020)	The authors discuss the integration of emerging technologies like artificial intelligence (AI) and blockchain in the development of an innovative, smart and sustainable farming environment. They focus on addressing the challenges faced by the agriculture industry and provide solutions through the use of these advanced technologies							X			B
Alfalih (2022)	This article aims to examine the impact of intelligent automation on customer engagement and retention in the food and restaurant industry, particularly in the context of the COVID-19 pandemic					X	X				A
Ali and Xia (2022)	This paper discusses the role of the digital agricultural market (DAM) in developing countries to mitigate uncertainty faced by stakeholders and to contribute to sustainability and social welfare, especially in the context of the COVID-19 pandemic				X			X			A
Anand et al. (2022)	The authors, based on a literature review, confer the use of artificial intelligence based technologies such as machine learning and Image processing in plant diseases detection and discuss their potential benefits			X						X	B
Basljan et al. (2021)	The authors discuss the challenges associated with delivering perishable goods and propose an approach for reliable planning and scheduling of perishable goods orders implementing artificial intelligence. This approach relies on gated recurrent unit neural networks to predict delivery routes and address the identified challenges					X	X				C
Bocewicz et al. (2017)	The authors examine the optimization of transportation logistics within the food supply chain, with a focus on developing a comprehensive model for efficient traffic flow management and planning. The purpose of this study is to provide valuable insights and actionable solutions to food supply chain management decision makers to build resilient food supply networks					X	X				C
Brunori et al. (2022)	This book chapter addresses the challenges of transitioning to sustainable food systems and explores the role of digital technologies, in this process. It identifies key digital technologies with disruptive potential, discusses the risks associated with their adoption, and provides recommendations for embedding socio-economic principles in the digitalization process		X					X			E
Camaréna (2020)	The authors focus on artificial intelligence adoption in implementing transitions to sustainable food systems. This study seeks to investigate how AI can contribute to improving food security, preserving environmental resources, promoting social and economic development, and ensuring overall sustainability in the food and agriculture sectors			X				X			E
Chu et al. (2019)	The authors introduce an AI-driven hybrid model aimed at improving the precision and dependability of grape price predictions. The study underscores the significance of precise forecasting, highlights the challenges it faces, specifically for grapes, and investigates the potential benefits of integrating advanced AI techniques, such as machine learning and optimization algorithms, to fulfil price forecasting and subsequently support more informed decision-making within the industry					X	X				F
Dadi et al. (2021)	The article discusses the challenges of agrifood supply chains and how digital technologies can refine them. The authors performing a systematic review, investigate the concept of digitalization in agrifood supply chains and highlight they key role it plays to develop intelligent, sensible and sustainable agrifood supply chain systems			X			X				E
Darapaneni et al. (2022)	The paper determines the challenges facing fishing farmers, highlighting the impact of diseases on fish productivity. The authors discuss AI-based fish disease detection systems that could be benefit micro and small fish farmers, and propose for the matter an underwater system based on image processing and AI technologies		X							X	A
Dellino et al. (2018)	The authors present the development of a decision support system (DSS) aimed at enhancing the management of fresh and highly perishable food supply chains. The primary objective is to improve efficiency, reduce waste, and maintain product quality throughout the supply chain, from production to consumption					X	X		X		D
Dora et al. (2022)	The article examines the critical factors influencing AI adoption in food supply chains. The authors identify the main limitations of food supply chains and analyse the role of AI to address these challenges and promote sustainability and food supply chains efficiency		X				X				D
Doshi and Varghese (2022)	The authors discuss the intertwinement of artificial intelligence and renewable energy to optimize farmers practices with the aim of addressing challenges facing agriculture in suburban and rural areas		X					X			B
El Hachimi et al. (2021)	The authors discuss multiple machine learning models for crop yield and air temperature prediction. They illustrate the successful development of predictive models and recommend their capability to improve agricultural efficiency and sustainability					X				X	B
Espolov et al. (2019)	This article examines the impact of digitalization in agriculture by shedding the light on how it can transform the agribusiness and highlighting the challenges that hindering successful transformation	X								X	B
Fenu and Malloci (2021)	This paper analyses recent research on plant and crop diseases prediction at early stages, examining methodologies, data pre-processing, performance metrics, and challenges faced				X					X	B
Fiore et al. (2017)	The article delves into the intersection of artificial intelligence techniques (machine learning) and consumer choices, assessing the role of these machine learning in predicting preferences for Type 1 wheat flour, and underscores the implications of these insights for promoting healthier and more sustainable food consumption patterns					X	X				D
Ganeshkumar et al. (2022)	The authors inspect agritech industry digital transformation focusing on artificial intelligence (AI) role in this process. They identify and examine both the benefits and issues related to AI-based products in the agriculture sector. They suggested that addressing these challenges is essential to explore full potential of AI in the agritech industry					X				X	A
Goel et al. (2022a)	The book chapter describes various solutions for crop monitoring and livestock monitoring systems. The authors highlight the potential and importance of machine learning based remote monitoring and predictive analysis for livestock monitoring as it could enhance productivity		X							X	B
Goel et al. (2022b)	The authors focus on the development and implementation of a machine learning-based system for remote monitoring and predictive analytics in agriculture. The primary goal of this system is to enhance the management of crop and livestock production by leveraging machine learning techniques for monitoring, data analysis, and prediction of various factors that influence agricultural productivity					X				X	B
Ikeda et al. (2021)	The authors discuss AI applications to plant growth management development to address the challenges associated to it. They present an agriculture support system for agricultural workers 'VegaCareAI'. They declare that it is a tool to boost crop productivity through vegetable classification, plant disease classification and insect pest classification					X					B
Issa et al. (2022)	The authors provide a comprehension of AI readiness and adoption identifying a set of strategic elements to help Agritech companies better control their “readiness process for AI adoption”	X								X	A
Jain and Choudhary (2022)	The authors examine the importance or crop yield in agriculture and provide an overview of information technology applications to crop management. They introduce “soil-based machine learning analytical framework” to forecast crop yield and perform a comparison among different machine learning techniques discussing their potential to level-up crop yield prediction					X				X	B
Khan et al. (2022)	The authors discuss machine learning techniques in agriculture and highlight several applications such as crop yield forecasting and precision agriculture. This book chapter identify potential benefits and limitations of machine learning for agriculture		X							X	B
Kim and Chung (2020)	The paper focuses on the development of a hybrid decision model that integrates expert knowledge with artificial neural networks (ANNs) to provide personalized nutrition management and dietary recommendations						X				D
Kim et al. (2021)	This paper studies an AI-based method to detect growth deviations in Aloe arborescence L. by utilizing the VGG-16 convolutional neural network, attaining high accuracy in identifying watering and lighting problems for better hydroponic system management					X				X	D
Kumar et al. (2015)	The authors present the Crop Selection Method (CSM), an innovative machine learning approach to enhance agricultural planning by addressing the complexity of crop. By evaluating factors such as production rate, market price, and government policies, CSM aims to push up crop yield rates					X				X	B
Lachman and López (2019)	This paper examines the barriers to innovation in precision farming technologies in Argentina, focusing on the factors hindering market expansion from the supply side					X				X	A
Lao et al. (2010a)	This paper addresses the challenges in food distribution centres’ inventory receiving activities due to the growing demand for customized services. It presents a Real-time Food Receiving operations management System (RFRS) to boost efficiency and organization in the food intake process				X					X	C
Lao et al. (2010b)	This article discusses the importance of inventory information management in the food industry and addresses the shortcomings of existing approaches. It presents the Integrative Food Handling System (IFHS), which aims to optimize inventory management in food warehouses through notification mechanisms and decision support systems				X				X		C
Lee et al. (2021)	This paper examines senior customers' intention to use artificial intelligence food service stores. It explores the effect of different types of Motivated Consumer Innovativeness (MCI) on perceived usefulness and enjoyment, and how these factors affect trust and usage intention					X	X				F
Li and Xiao (2021)	The authors examine the use of data mining technology and 6G IoT communication in agriculture e-commerce, they debate about their ability to improve and boost efficiency and competitiveness of agricultural e-commerce. The article evidences the potential advantages of 6G IoT communication to agriculture industry to collect and analyse data					X	X				C
Liundi et al. (2019)	The authors discuss the potential of artificial intelligence (AI) to enhance rice productivity in Indonesia by examining current cultivation technologies and systems. They propose a model to predict optimal planting dates, considering different factors aiming to minimize yield gaps and increase efficiency				X					X	B
Lugonja et al. (2022)	The authors study the concept of smart agriculture identifies its' correlation to sustainable digital transformation. They refer to several technologies used in smart technologies and highlight “the importance of data analytics and predictive modelling” in improving agriculture production		X					X		X	B
Maulana et al. (2022)	The authors accentuate the importance of AI and the challenges associated to it in the agriculture sector identifying the different trends and patterns of research. They explain the use of scientometric analysis and highlight its' potential to ameliorate the understanding of AI in agriculture			X						X	A
Morales and Elkader (2020)	The study explores the effects of Logistics 4.0 technologies on agriculture in developing nations, targeting three Sustainable Development Goals (SDGs): Zero Hunger, Clean Water and Sanitation, and Life on Land. The study highlights ten distinct technologies, and analyses their varying impacts on these SDGs. Ultimately, the study seeks to illustrate the potential of Logistics 4.0 technologies to promote sustainable agriculture in developing countries		X					X			A
Moreira et al. (2022)	The authors identify the benefits of using smart farm technologies (mobile applications, Convolutional Neural Network (CNN), edge-compute devices …) to enhance sustainability and efficiency in agricultural practices. The authors suggest that AgroLens could be further expanded and blended within smart farm systems to create a comprehensive and cost-effective leaf diseases diagnostics for farmers					X				X	B
Murugesan et al. (2019)	This paper delves into artificial intelligence and robotics application in Agriculture 5.0, in the context of crop yields prediction, utilizing machine learning and deep learning models across diverse crops and seasons. It highlights the possibility of enhanced accuracy					X				X	B
Nakandala et al. (2016)	The authors examine the important relationship between transportation costs and fresh food quality in the supply chain. This research focuses on developing an optimization model that balances the cost of transportation with the need to maintain the highest quality of perishable food in transit, so that consumers enjoy high quality food and businesses improve their efficiency and cost effectiveness					X	X				C
Nayal et al. (2023)	The authors examine the potential of artificial intelligence and machine learning to address the challenges that the COVID-19 pandemic has posed to the agricultural supply chain in the Indian context. The study highlights how artificial intelligence and machine learning could optimize different aspects of the agricultural supply chain. The study highlights the key role artificial intelligence and machine learning play in developing more resilient and adaptive agricultural supply chains to address challenges, and to manage uncertainties		X				X				E
Nayal et al. (2022)	The authors investigate which factors influence AI adoption and highlight its' effect on supply chain risk mitigation (SCRM) that could be caused by disruptions like COVID-19. They conclude that “process factors, information sharing and supply chain integration” are key determinants that influence AI adoption and the later positively affect SCRM					X	X				E
Olan et al. (2022)	The authors investigate the theory of artificial intelligence supply chain financing and networks for the food and beverages industry. They suggest a novel conceptual meta-framework and conclude that AI based supply chain networks establish a sustainable financing stream for supply chain and discuss the study implications fin the food and drink industry		X				X				D
Pantazi et al. (2020)	This book provides an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. It focuses on precision agriculture techniques	X								X	B
Priyadarshi et al. (2019)	This study aims to select the optimal demand forecasting model for selected vegetables at the retail stage based on performance analysis. The research compares various forecasting models, to help increase efficiency					X	X				C
Rahmouni et al. (2022)	The authors suggest an Artificial Intelligence of Things AIoT based framework for precision agriculture. They review existing frameworks and highlight the role of precision agriculture in improving efficiency and hence, productivity		X							X	B
Rahutomo et al. (2019)	This study discusses an AI-driven web application designed to boost pineapple counting efficiency in Indonesia's horticulture sector, leveraging Agricultural 4.0 technologies. This model streamlines resources utilization, including water, fertilizers, insecticides, and packaging materials, by minimizing human error and optimizing processes					X				X	B
Raith and Dustdar (2021)	This paper presents the use case of edge intelligence as a service platform from food computing to smart health				X					X	B
Richards et al. (2009)	This paper explores the implementation of plant genetics and farmer practices to enhance food security in Africa. The paper evaluates farmer seed systems, comparing supervised and unsupervised learning approaches, and proposes an unsupervised learning method				X			X		X	B
Rocha and Lagarteja (2020)	This article focuses on a smartphone application designed to assist mango farmers by identifying pests and suggesting adequate control measures, ultimately aiming to boost fruit production					X				X	B
Saranya et al. (2021)	The authors discuss the effects of climate changes on crop yield and propose the Bootstrap Aggregative MapReduce Rocchio Classification in order to ameliorate crop yield prediction accuracy					X				X	A
Secinaro et al. (2022)	The authors seek to understand the relationship between agriculture entrepreneurship and new technologies focusing on identifying the role emerging technologies (artificial intelligence, augmented reality and machine learning) could play to promote agri-businesses		X						X		A
Sitek et al. (2017)	The authors examine, introduce, the application of constraint-driven methods for optimizing food supply chain management and for addressing the challenges associated to it. They declare that a constraint-driven approach can be used to build a decision support system for food supply chain management					X	X				C
Skvortsov (2020)	This paper explores the potential of implementing artificial intelligence (AI) technologies in Sverdlovsk Oblast's agriculture and discusses the challenges and prospects for AI adoption					X				X	B
Sood et al. (2022)	This paper investigates the factors influencing the adoption of Artificial Intelligence in agriculture, aiming to contribute to sustainable agriculture practices and meet the growing food demands. The authors propose a framework in which they these factors into five groups, highlighting the importance building trust among farmers and customizing AI solutions for better technology adoption			X				X		X	A
Spanaki et al. (2021)	The authors discuss data sharing practices in agriculture and identify the main challenges facing the sector. They propose a data sharing agreement as a template of AI adoption to data management. They suggest that developing platforms, infrastructures, policies and legal frameworks for data usage remain critical for the farming sector	X								X	A
Spanaki et al. (2022)	The article explores the topic of artificial intelligence and food security. It identifies the potential benefits and challenges of AI-based agritech drones for smart agrifood operations and stress on the implication of the study for food security		X					X			A
Taghikhah et al. (2021)	The article discusses consumer behaviour shifts towards organic food products. Based on machine learning algorithms, the authors identify an attitude behavioural gap between intention and actual behaviour, focusing on cognitive factors. They conclude that affective factors and cues could result in spontaneous purchasing behaviour, causing them to behave against their intentions					X	X				F
Tassiello et al. (2021)	The article discusses consumer-voice assistant interaction in the purchasing process of food and beverages products and explore the psychological power it has on consumer decision making process					X	X				A
Ting et al. (2014)	The authors discuss the use of data mining techniques to analyse logistics data in the red wine industry in order to ensure quality and promote sustainability in the food supply chain					X	X				C
Valls et al. (2010)	The authors address the challenges associated with sewage sludge disposal management on agricultural soils. They propose a multi-attribute preference approach for evaluating and rating suitable soils					X				X	E
Vuppalapati (2022)	This chapter provides an introduction to AI techniques and heuristics, as well as methods for processing agricultural, climatology, and satellite radiometer datasets. It also examines association mining, clustering techniques, and concludes with a focus on wheat commodity risk modelling for Egypt		X							X	E
Yan et al. (2015)	The authors identify the most significant variables for accurate pest risk prediction. They explore the use of multiple regression analysis and artificial neural networks (ANNs) to predict crop pest risks, aiming to improve pest management strategies					X				X	C
Yang et al. (2020)	The article discusses AI implementation for dynamic pricing and information disclosure in the fresh produce market, identifying the main challenges facing the sector and argue about the potential benefits of employing AI to boost efficiency and sustainability. The authors find that quality-based pricing system is an efficient strategy for not only for producers and consumers, but also for the environment, as it reduces food waste					X		X		X	F
Yang et al. (2020)	This paper examines the role of artificial intelligence and Internet-of-Things in creating a collaborative business ecosystem for the Chinese fish farming industry, considering Celefish as a case study. The authors emphasize the importance of digital technologies in fostering value co-creation and sustainability		X					X			B
Zhu and Chang (2020)	The authors explore an interesting relationship between anthropomorphic AI-powered robotic chefs and consumer perceptions of food quality. By examining the impact of human-like characteristics of robot chefs on consumer expectations and satisfaction, this study provides valuable insight into the potential advantages and challenges of integrating AI-driven humanoid robots into the culinary industry. The results will not only help us understand consumer behaviour and psychology associated with advanced technology, but also guide the future development and deployment of robotic chefs in various foodservice environments					X	X				F

Authors	Topic	Methodology					Touchpoints				Cluster
		Methodology					AI technologies for external stakeholders	AI technologies for sustainability	AI for decision support systems and entrepreneurship	AI technologies for efficiency, productivity and profitability
		Mixed Method	Qualitative	Literature Review	Conceptual	Quantitative	AI technologies for external stakeholders	AI technologies for sustainability	AI for decision support systems and entrepreneurship
Abban and Abebe (2022)	The literature review investigates the importance of digitalization and sustainability in African agribusinesses and food supply chains and highlight their potential to overcome challenges in the sector. The authors point out the main technologies employed in agriculture and supply chain management and highlight several sustainable practices			X			X	X			A
Agyemang et al. (2022)	This study examines food system frameworks in relation to sustainability aspects, constructs future scenarios, and develops a decision support system that helps stakeholders make strategic decisions and take pre-emptive actions to address potential challenges and uncertainties in the food system	X						X	X		E
Ahmed and Hussain (2022)	The authors suggest a forecasting model to predict wheat production using machine learning. After analysis, the model proposed is proven to be effective and thus could be replicable on other crops and regions		X							X	B
Alam et al. (2020)	The authors discuss the integration of emerging technologies like artificial intelligence (AI) and blockchain in the development of an innovative, smart and sustainable farming environment. They focus on addressing the challenges faced by the agriculture industry and provide solutions through the use of these advanced technologies							X			B
Alfalih (2022)	This article aims to examine the impact of intelligent automation on customer engagement and retention in the food and restaurant industry, particularly in the context of the COVID-19 pandemic					X	X				A
Ali and Xia (2022)	This paper discusses the role of the digital agricultural market (DAM) in developing countries to mitigate uncertainty faced by stakeholders and to contribute to sustainability and social welfare, especially in the context of the COVID-19 pandemic				X			X			A
Anand et al. (2022)	The authors, based on a literature review, confer the use of artificial intelligence based technologies such as machine learning and Image processing in plant diseases detection and discuss their potential benefits			X						X	B
Basljan et al. (2021)	The authors discuss the challenges associated with delivering perishable goods and propose an approach for reliable planning and scheduling of perishable goods orders implementing artificial intelligence. This approach relies on gated recurrent unit neural networks to predict delivery routes and address the identified challenges					X	X				C
Bocewicz et al. (2017)	The authors examine the optimization of transportation logistics within the food supply chain, with a focus on developing a comprehensive model for efficient traffic flow management and planning. The purpose of this study is to provide valuable insights and actionable solutions to food supply chain management decision makers to build resilient food supply networks					X	X				C
Brunori et al. (2022)	This book chapter addresses the challenges of transitioning to sustainable food systems and explores the role of digital technologies, in this process. It identifies key digital technologies with disruptive potential, discusses the risks associated with their adoption, and provides recommendations for embedding socio-economic principles in the digitalization process		X					X			E
Camaréna (2020)	The authors focus on artificial intelligence adoption in implementing transitions to sustainable food systems. This study seeks to investigate how AI can contribute to improving food security, preserving environmental resources, promoting social and economic development, and ensuring overall sustainability in the food and agriculture sectors			X				X			E
Chu et al. (2019)	The authors introduce an AI-driven hybrid model aimed at improving the precision and dependability of grape price predictions. The study underscores the significance of precise forecasting, highlights the challenges it faces, specifically for grapes, and investigates the potential benefits of integrating advanced AI techniques, such as machine learning and optimization algorithms, to fulfil price forecasting and subsequently support more informed decision-making within the industry					X	X				F
Dadi et al. (2021)	The article discusses the challenges of agrifood supply chains and how digital technologies can refine them. The authors performing a systematic review, investigate the concept of digitalization in agrifood supply chains and highlight they key role it plays to develop intelligent, sensible and sustainable agrifood supply chain systems			X			X				E
Darapaneni et al. (2022)	The paper determines the challenges facing fishing farmers, highlighting the impact of diseases on fish productivity. The authors discuss AI-based fish disease detection systems that could be benefit micro and small fish farmers, and propose for the matter an underwater system based on image processing and AI technologies		X							X	A
Dellino et al. (2018)	The authors present the development of a decision support system (DSS) aimed at enhancing the management of fresh and highly perishable food supply chains. The primary objective is to improve efficiency, reduce waste, and maintain product quality throughout the supply chain, from production to consumption					X	X		X		D
Dora et al. (2022)	The article examines the critical factors influencing AI adoption in food supply chains. The authors identify the main limitations of food supply chains and analyse the role of AI to address these challenges and promote sustainability and food supply chains efficiency		X				X				D
Doshi and Varghese (2022)	The authors discuss the intertwinement of artificial intelligence and renewable energy to optimize farmers practices with the aim of addressing challenges facing agriculture in suburban and rural areas		X					X			B
El Hachimi et al. (2021)	The authors discuss multiple machine learning models for crop yield and air temperature prediction. They illustrate the successful development of predictive models and recommend their capability to improve agricultural efficiency and sustainability					X				X	B
Espolov et al. (2019)	This article examines the impact of digitalization in agriculture by shedding the light on how it can transform the agribusiness and highlighting the challenges that hindering successful transformation	X								X	B
Fenu and Malloci (2021)	This paper analyses recent research on plant and crop diseases prediction at early stages, examining methodologies, data pre-processing, performance metrics, and challenges faced				X					X	B
Fiore et al. (2017)	The article delves into the intersection of artificial intelligence techniques (machine learning) and consumer choices, assessing the role of these machine learning in predicting preferences for Type 1 wheat flour, and underscores the implications of these insights for promoting healthier and more sustainable food consumption patterns					X	X				D
Ganeshkumar et al. (2022)	The authors inspect agritech industry digital transformation focusing on artificial intelligence (AI) role in this process. They identify and examine both the benefits and issues related to AI-based products in the agriculture sector. They suggested that addressing these challenges is essential to explore full potential of AI in the agritech industry					X				X	A
Goel et al. (2022a)	The book chapter describes various solutions for crop monitoring and livestock monitoring systems. The authors highlight the potential and importance of machine learning based remote monitoring and predictive analysis for livestock monitoring as it could enhance productivity		X							X	B
Goel et al. (2022b)	The authors focus on the development and implementation of a machine learning-based system for remote monitoring and predictive analytics in agriculture. The primary goal of this system is to enhance the management of crop and livestock production by leveraging machine learning techniques for monitoring, data analysis, and prediction of various factors that influence agricultural productivity					X				X	B
Ikeda et al. (2021)	The authors discuss AI applications to plant growth management development to address the challenges associated to it. They present an agriculture support system for agricultural workers 'VegaCareAI'. They declare that it is a tool to boost crop productivity through vegetable classification, plant disease classification and insect pest classification					X					B
Issa et al. (2022)	The authors provide a comprehension of AI readiness and adoption identifying a set of strategic elements to help Agritech companies better control their “readiness process for AI adoption”	X								X	A
Jain and Choudhary (2022)	The authors examine the importance or crop yield in agriculture and provide an overview of information technology applications to crop management. They introduce “soil-based machine learning analytical framework” to forecast crop yield and perform a comparison among different machine learning techniques discussing their potential to level-up crop yield prediction					X				X	B
Khan et al. (2022)	The authors discuss machine learning techniques in agriculture and highlight several applications such as crop yield forecasting and precision agriculture. This book chapter identify potential benefits and limitations of machine learning for agriculture		X							X	B
Kim and Chung (2020)	The paper focuses on the development of a hybrid decision model that integrates expert knowledge with artificial neural networks (ANNs) to provide personalized nutrition management and dietary recommendations						X				D
Kim et al. (2021)	This paper studies an AI-based method to detect growth deviations in Aloe arborescence L. by utilizing the VGG-16 convolutional neural network, attaining high accuracy in identifying watering and lighting problems for better hydroponic system management					X				X	D
Kumar et al. (2015)	The authors present the Crop Selection Method (CSM), an innovative machine learning approach to enhance agricultural planning by addressing the complexity of crop. By evaluating factors such as production rate, market price, and government policies, CSM aims to push up crop yield rates					X				X	B
Lachman and López (2019)	This paper examines the barriers to innovation in precision farming technologies in Argentina, focusing on the factors hindering market expansion from the supply side					X				X	A
Lao et al. (2010a)	This paper addresses the challenges in food distribution centres’ inventory receiving activities due to the growing demand for customized services. It presents a Real-time Food Receiving operations management System (RFRS) to boost efficiency and organization in the food intake process				X					X	C
Lao et al. (2010b)	This article discusses the importance of inventory information management in the food industry and addresses the shortcomings of existing approaches. It presents the Integrative Food Handling System (IFHS), which aims to optimize inventory management in food warehouses through notification mechanisms and decision support systems				X				X		C
Lee et al. (2021)	This paper examines senior customers' intention to use artificial intelligence food service stores. It explores the effect of different types of Motivated Consumer Innovativeness (MCI) on perceived usefulness and enjoyment, and how these factors affect trust and usage intention					X	X				F
Li and Xiao (2021)	The authors examine the use of data mining technology and 6G IoT communication in agriculture e-commerce, they debate about their ability to improve and boost efficiency and competitiveness of agricultural e-commerce. The article evidences the potential advantages of 6G IoT communication to agriculture industry to collect and analyse data					X	X				C
Liundi et al. (2019)	The authors discuss the potential of artificial intelligence (AI) to enhance rice productivity in Indonesia by examining current cultivation technologies and systems. They propose a model to predict optimal planting dates, considering different factors aiming to minimize yield gaps and increase efficiency				X					X	B
Lugonja et al. (2022)	The authors study the concept of smart agriculture identifies its' correlation to sustainable digital transformation. They refer to several technologies used in smart technologies and highlight “the importance of data analytics and predictive modelling” in improving agriculture production		X					X		X	B
Maulana et al. (2022)	The authors accentuate the importance of AI and the challenges associated to it in the agriculture sector identifying the different trends and patterns of research. They explain the use of scientometric analysis and highlight its' potential to ameliorate the understanding of AI in agriculture			X						X	A
Morales and Elkader (2020)	The study explores the effects of Logistics 4.0 technologies on agriculture in developing nations, targeting three Sustainable Development Goals (SDGs): Zero Hunger, Clean Water and Sanitation, and Life on Land. The study highlights ten distinct technologies, and analyses their varying impacts on these SDGs. Ultimately, the study seeks to illustrate the potential of Logistics 4.0 technologies to promote sustainable agriculture in developing countries		X					X			A
Moreira et al. (2022)	The authors identify the benefits of using smart farm technologies (mobile applications, Convolutional Neural Network (CNN), edge-compute devices …) to enhance sustainability and efficiency in agricultural practices. The authors suggest that AgroLens could be further expanded and blended within smart farm systems to create a comprehensive and cost-effective leaf diseases diagnostics for farmers					X				X	B
Murugesan et al. (2019)	This paper delves into artificial intelligence and robotics application in Agriculture 5.0, in the context of crop yields prediction, utilizing machine learning and deep learning models across diverse crops and seasons. It highlights the possibility of enhanced accuracy					X				X	B
Nakandala et al. (2016)	The authors examine the important relationship between transportation costs and fresh food quality in the supply chain. This research focuses on developing an optimization model that balances the cost of transportation with the need to maintain the highest quality of perishable food in transit, so that consumers enjoy high quality food and businesses improve their efficiency and cost effectiveness					X	X				C
Nayal et al. (2023)	The authors examine the potential of artificial intelligence and machine learning to address the challenges that the COVID-19 pandemic has posed to the agricultural supply chain in the Indian context. The study highlights how artificial intelligence and machine learning could optimize different aspects of the agricultural supply chain. The study highlights the key role artificial intelligence and machine learning play in developing more resilient and adaptive agricultural supply chains to address challenges, and to manage uncertainties		X				X				E
Nayal et al. (2022)	The authors investigate which factors influence AI adoption and highlight its' effect on supply chain risk mitigation (SCRM) that could be caused by disruptions like COVID-19. They conclude that “process factors, information sharing and supply chain integration” are key determinants that influence AI adoption and the later positively affect SCRM					X	X				E
Olan et al. (2022)	The authors investigate the theory of artificial intelligence supply chain financing and networks for the food and beverages industry. They suggest a novel conceptual meta-framework and conclude that AI based supply chain networks establish a sustainable financing stream for supply chain and discuss the study implications fin the food and drink industry		X				X				D
Pantazi et al. (2020)	This book provides an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. It focuses on precision agriculture techniques	X								X	B
Priyadarshi et al. (2019)	This study aims to select the optimal demand forecasting model for selected vegetables at the retail stage based on performance analysis. The research compares various forecasting models, to help increase efficiency					X	X				C
Rahmouni et al. (2022)	The authors suggest an Artificial Intelligence of Things AIoT based framework for precision agriculture. They review existing frameworks and highlight the role of precision agriculture in improving efficiency and hence, productivity		X							X	B
Rahutomo et al. (2019)	This study discusses an AI-driven web application designed to boost pineapple counting efficiency in Indonesia's horticulture sector, leveraging Agricultural 4.0 technologies. This model streamlines resources utilization, including water, fertilizers, insecticides, and packaging materials, by minimizing human error and optimizing processes					X				X	B
Raith and Dustdar (2021)	This paper presents the use case of edge intelligence as a service platform from food computing to smart health				X					X	B
Richards et al. (2009)	This paper explores the implementation of plant genetics and farmer practices to enhance food security in Africa. The paper evaluates farmer seed systems, comparing supervised and unsupervised learning approaches, and proposes an unsupervised learning method				X			X		X	B
Rocha and Lagarteja (2020)	This article focuses on a smartphone application designed to assist mango farmers by identifying pests and suggesting adequate control measures, ultimately aiming to boost fruit production					X				X	B
Saranya et al. (2021)	The authors discuss the effects of climate changes on crop yield and propose the Bootstrap Aggregative MapReduce Rocchio Classification in order to ameliorate crop yield prediction accuracy					X				X	A
Secinaro et al. (2022)	The authors seek to understand the relationship between agriculture entrepreneurship and new technologies focusing on identifying the role emerging technologies (artificial intelligence, augmented reality and machine learning) could play to promote agri-businesses		X						X		A
Sitek et al. (2017)	The authors examine, introduce, the application of constraint-driven methods for optimizing food supply chain management and for addressing the challenges associated to it. They declare that a constraint-driven approach can be used to build a decision support system for food supply chain management					X	X				C
Skvortsov (2020)	This paper explores the potential of implementing artificial intelligence (AI) technologies in Sverdlovsk Oblast's agriculture and discusses the challenges and prospects for AI adoption					X				X	B
Sood et al. (2022)	This paper investigates the factors influencing the adoption of Artificial Intelligence in agriculture, aiming to contribute to sustainable agriculture practices and meet the growing food demands. The authors propose a framework in which they these factors into five groups, highlighting the importance building trust among farmers and customizing AI solutions for better technology adoption			X				X		X	A
Spanaki et al. (2021)	The authors discuss data sharing practices in agriculture and identify the main challenges facing the sector. They propose a data sharing agreement as a template of AI adoption to data management. They suggest that developing platforms, infrastructures, policies and legal frameworks for data usage remain critical for the farming sector	X								X	A
Spanaki et al. (2022)	The article explores the topic of artificial intelligence and food security. It identifies the potential benefits and challenges of AI-based agritech drones for smart agrifood operations and stress on the implication of the study for food security		X					X			A
Taghikhah et al. (2021)	The article discusses consumer behaviour shifts towards organic food products. Based on machine learning algorithms, the authors identify an attitude behavioural gap between intention and actual behaviour, focusing on cognitive factors. They conclude that affective factors and cues could result in spontaneous purchasing behaviour, causing them to behave against their intentions					X	X				F
Tassiello et al. (2021)	The article discusses consumer-voice assistant interaction in the purchasing process of food and beverages products and explore the psychological power it has on consumer decision making process					X	X				A
Ting et al. (2014)	The authors discuss the use of data mining techniques to analyse logistics data in the red wine industry in order to ensure quality and promote sustainability in the food supply chain					X	X				C
Valls et al. (2010)	The authors address the challenges associated with sewage sludge disposal management on agricultural soils. They propose a multi-attribute preference approach for evaluating and rating suitable soils					X				X	E
Vuppalapati (2022)	This chapter provides an introduction to AI techniques and heuristics, as well as methods for processing agricultural, climatology, and satellite radiometer datasets. It also examines association mining, clustering techniques, and concludes with a focus on wheat commodity risk modelling for Egypt		X							X	E
Yan et al. (2015)	The authors identify the most significant variables for accurate pest risk prediction. They explore the use of multiple regression analysis and artificial neural networks (ANNs) to predict crop pest risks, aiming to improve pest management strategies					X				X	C
Yang et al. (2020)	The article discusses AI implementation for dynamic pricing and information disclosure in the fresh produce market, identifying the main challenges facing the sector and argue about the potential benefits of employing AI to boost efficiency and sustainability. The authors find that quality-based pricing system is an efficient strategy for not only for producers and consumers, but also for the environment, as it reduces food waste					X		X		X	F
Yang et al. (2020)	This paper examines the role of artificial intelligence and Internet-of-Things in creating a collaborative business ecosystem for the Chinese fish farming industry, considering Celefish as a case study. The authors emphasize the importance of digital technologies in fostering value co-creation and sustainability		X					X			B
Zhu and Chang (2020)	The authors explore an interesting relationship between anthropomorphic AI-powered robotic chefs and consumer perceptions of food quality. By examining the impact of human-like characteristics of robot chefs on consumer expectations and satisfaction, this study provides valuable insight into the potential advantages and challenges of integrating AI-driven humanoid robots into the culinary industry. The results will not only help us understand consumer behaviour and psychology associated with advanced technology, but also guide the future development and deployment of robotic chefs in various foodservice environments					X	X				F

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