ADAPTIVE LEARNING IN AGRICULTURE WITH THE HELP OF MACHINE LEARNING
Food is known to be a fundamental human need that can be met by farming, Agriculture plays an important role in sustaining human activities. Pre-harvesting, harvesting, and post-harvesting are the three main categories in which we can distinguish the agricultural practices. Major threats such as overpopulation and resource competition pose a threat to the planet's food security. Advancements in smart farming and precision agriculture provide valuable resources to solve agricultural productivity issues in order to address the ever-increasing dynamic problems in agricultural production systems. The secret to ensuring future food sustainability is data analytics. And on the other end, Machine Learning (ML) is the core of learning that works in a way like human brain does. ML is a current technology that is assisting farmers in reducing farming losses by making detailed crop advice and observations. The current study presents a systematic review of ML applications in supply units provided on the basis of farming. The study demonstrates how ML strategies can support these supply units and contribute to sustainability with the help of adaptive learning. The system recognizes the role of ML algorithms in delivering real time analytic comprehensions for proactive data driven decision making offers recommendations for effective management of Agro-Supply Chains(ASCs) for enhanced agricultural production and sustainability to researchers, practitioners, and policymakers and a quick snap about the conducts a comprehensive review of the most recent ML applications in agriculture to address issues in three areas: pre-harvesting, harvesting, and post-harvesting..
- 2021-07-13 (2)
- 2021-03-30 (1)