TY - JOUR
T1 - Application of machine learning and artificial intelligence on agriculture supply chain
T2 - a comprehensive review and future research directions
AU - Kumari, Sneha
AU - Venkatesh, V. G.
AU - Tan, Felix Ter Chian
AU - Bharathi, S. Vijayakumar
AU - Ramasubramanian, M.
AU - Shi, Yangyan
PY - 2023/9/5
Y1 - 2023/9/5
N2 - Agriculture has transitioned from traditional to contemporary practices because of technological transformation. Powered by digital technologies and analytics such as machine learning and artificial intelligence, the application of analytics has become an emerging topic in the agriculture supply chain. The study has used bibliometric and visualization tools followed by a taxonomy of the research manuscripts. The results confirm that the publication trend has increased as ASC has been demanding the application of AI and ML. The results of the geographical mapping, journal statistics, keyword analysis, network analysis, affiliation statistics, citation analysis, keywords map, co-occurrences and factor analysis reveal the transformation of ASC towards precision agriculture, deep learning, reinforcement learning, food safety and food supply chain. Based on the results and discussions, the work provided a roadmap for future studies on emerging research themes. It contributes to the literature by discussing the scope for machine learning in the coming years and, more importantly, identifying the research clusters and future research directions. The concept has been gaining momentum in recent years, and therefore, it has become necessary to categorize diverse types of research output and study the research trend in the agriculture supply chain.
AB - Agriculture has transitioned from traditional to contemporary practices because of technological transformation. Powered by digital technologies and analytics such as machine learning and artificial intelligence, the application of analytics has become an emerging topic in the agriculture supply chain. The study has used bibliometric and visualization tools followed by a taxonomy of the research manuscripts. The results confirm that the publication trend has increased as ASC has been demanding the application of AI and ML. The results of the geographical mapping, journal statistics, keyword analysis, network analysis, affiliation statistics, citation analysis, keywords map, co-occurrences and factor analysis reveal the transformation of ASC towards precision agriculture, deep learning, reinforcement learning, food safety and food supply chain. Based on the results and discussions, the work provided a roadmap for future studies on emerging research themes. It contributes to the literature by discussing the scope for machine learning in the coming years and, more importantly, identifying the research clusters and future research directions. The concept has been gaining momentum in recent years, and therefore, it has become necessary to categorize diverse types of research output and study the research trend in the agriculture supply chain.
KW - Agriculture
KW - Agriculture supply chain
KW - Artificial intelligence
KW - Bibliometric analysis
KW - Deep learning
KW - Machine learning
KW - Random forests
UR - http://www.scopus.com/inward/record.url?scp=85169803337&partnerID=8YFLogxK
U2 - 10.1007/s10479-023-05556-3
DO - 10.1007/s10479-023-05556-3
M3 - Article
AN - SCOPUS:85169803337
SN - 0254-5330
JO - Annals of Operations Research
JF - Annals of Operations Research
ER -