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Traditionally, the agricultural supply chains have dealt with a lot of flaws that affect the whole sector. The agricultural industry is undergoing a transformative shift with advanced technologies, particularly Machine Learning. This review depicts the bridging of the gap in the development of agricultural supply chains. ML and AI are found to be powerful tools for making informed decisions regarding challenges like post-harvest losses, price volatility, logistical difficulties, etc. In many review papers, the stated challenges are not addressed completely. The same can be addressed by handling and analyzing the data carefully and properly using ML algorithms to make the system more efficient than the present scenario. We believe these gaps can be bridged with techniques like demand forecasting, optimal resource utilization, supply chain visibility, etc.
Nimbokar et al. (Tue,) studied this question.