A Review: The Application of Machine Learning in the Agricultural Sector

This paper reviews some of the agricultural applications of machine learning. It describes how machine learning (ML) is changing the way we farm, driven by ever-increasing availability of data and computational power. The paper looks into the main application of ML in crop management, water management, soil management, and livestock management. It also points out several inherent difficulties associated with the adoption of ML in agriculture, including issues of data acquisition and quality, model interpretability needs, constraints in computational resources, and the necessities of building generalizable and adaptable models. In this regard, the paper highlights the importance of further research to configure resilient and robust ML-aided automated models, explainable AI, edge computing, data fusion, and human-centered AI to realize all the potential of ML for a stronger and better agricultural future. Farmers have had a tumultuous few years responding to resource scarcity, climate change, and feeding an increasing global population