Abstract
The agri-food supply chain consists of activities in “farm-to-fork” order, including agriculture (i.e., land cultivation and crop production), production processes, packaging, warehousing systems, distribution, transportation, and marketing. Data analytics hold the key to ensuring future food security, food safety, and ecological sustainability. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing can change production management practices. The current study presents a systematic review of machine learning (ML) applications in the agri-food supply chain. This framework identifies the role of ML algorithms in providing real-time analytical insights to assist proactive data-driven decision-making processes in the agri-food supply chain. It also guides researchers, practitioners, and policymakers on successful management to increase the productivity and sustainability of agri-food.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Earth and Environmental Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
