Machine learning application for sustainable agri-food supply chain performance: a review
Imam Santoso, Mangku Purnomo, Akhmad Adi Sulianto, A Choirun
Abstract
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.