A Review on Crop Yield Prediction using Machine Learning and Remote Sensing Data

As of 2025, Agriculture is still considered the backbone of the Indian economy as it significantly contributes to the gross domestic product and provides millions of livelihoods. The major challenge for us is the unpredictability of crop yields caused by various climatic, soil, and environmental factors. However, it has been interesting to see how recent advancements in remote sensing and machine learning have paved the way for better crop yield prediction. In this review, the authors inspect various machine-learning approaches for crop yield forecasting, satellite imagery, and environmental data to get hold of complex patterns that could benefit farmers and policymakers. A comparative analysis of different learning models, regression-based techniques and ensemble methods is performed to highlight their effectiveness in yield prediction. Moreover, the authors illustrate how remote sensing capabilities can improve our assessments of agriculture and our predictive capabilities. This review highlights the role of a data-driven approach in modern agriculture, aiding food security and sustainable development.