Integrating Machine Learning and Remote Sensing for Sustainable Agricultural Decision-Making.pdf

Abstract Sustainable agriculture faces increasing challenges due to resource constraints and environmental pressures, prompting the adoption of advanced technologies for improved decision-making. This review synthesizes current progress in integrating machine learning (ML) with remote sensing to address key agricultural needs, including crop health monitoring, yield prediction, soil property estimation, and resource optimization. The paper details the technological foundations of ML and remote sensing, compares different algorithmic approaches, and highlights case studies from diverse regions and crops. Notable advancements include disease detection using multispectral UAV imagery, yield forecasting via satellite data, and precision irrigation through smart IoT systems. The review further discusses methodological innovations, such as ensemble learning and deep reinforcement learning, as well as persistent challenges like data quality, model scalability, and ethical considerations. Overall, the integration of ML and remote sensing holds significant promise for sustainable agriculture, but future efforts are needed to enhance interoperability, real-time decision-making, and stakeholder engagement across policy and practice.