Rice Yield Prediction and Crop Health Assessment in Bangladesh Using Satellite Imagery and Machine Learning

This study proposes an integrated machine learning framework for rice yield prediction and crop health assessment in Bangladesh using satellite imagery and environmental data. The study combines NDVI derived from Sentinel-2 satellite imagery with rainfall and temperature data to characterize rice-growing conditions. Machine learning models are employed to predict rice yield and assess crop health across different rice seasons in Mymensingh and Khulna districts. The proposed approach demonstrates the potential of integrating remote sensing and machine learning for data-driven rice monitoring and yield estimation in Bangladesh.