Smart Agriculture: Optimizing Multicropping with Hybrid Models for Secured Supply Chains

Abstract

Agriculture is the study and practice of growing and improving land. Agriculture is influenced by various factors, including disease, pests, climate change, natural disasters, and human activities. Farmers should plant multi-crops using rice, coconuts, bananas, and turmeric to boost yield. Several problems are associated with Multicropping, such as poor soil quality, declining biodiversity, the loss of water supplies, and a labor crisis. The report presents studies related to optimally growing more than one crop per acre on limited land. The use of AI for multi-crop analysis is covered in the paper. The goal of the investigation is to use blockchain technology and AI to facilitate safe transactions and forecast agricultural losses in Multicropping. Neutrosophic logic is utilized in multi-regression analysis to estimate crop loss prior to the experiment. In the experiment, transactions are secured by blockchain technology, and crop loss over multiple crops is reduced using multi-regression analysis that is optimized by Reinforcement Learning