Rice Yield

Predicting rice yield accurately is crucial for food security and agricultural planning. Current research focuses on developing sophisticated predictive models using machine learning algorithms, such as those based on deep learning, boosted trees, and sparse regression, integrating climate data (temperature, precipitation), remotely sensed imagery (NDVI, leaf area index), and soil information to improve forecast precision. These models aim to identify causal relationships between environmental factors and yield, enhancing the reliability of yield forecasts at various scales (district to national levels). Improved yield prediction enables more effective resource allocation, risk management, and ultimately, more sustainable rice production.