Predicting Crop Yields Based on Climate Conditions

Agricultural productivity is vital for food security, yet it's increasingly vulnerable to changing climate conditions. Variations in temperature, rainfall, soil moisture, and extreme weather events directly affect crop yields. Traditional agricultural planning relies heavily on intuition and seasonal predictions. Machine learning models can analyze historical crop, soil, and climate data to provide accurate forecasts, empowering farmers, governments, and agricultural organizations to make informed planning decisions.

Historical yield datasets combined with weather, soil, and satellite data are used to build machine learning models that predict the expected yield of different crops. Regression models, Random Forests, XGBoost, and deep learning architectures like LSTM can capture temporal and seasonal trends. These predictions help farmers optimize planting schedules, resource allocation, and food supply planning, especially under changing climate scenarios.

Climate data (temperature, rainfall, humidity, soil moisture) and historical crop production data are collected for specific regions. Feature engineering includes lagged climate variables, seasonal indicators, and soil properties. Machine learning models predict crop yield outputs, often as a regression problem. Insights are visualized through dashboards showing yield forecasts, risk zones, and optimal planting recommendations for different climate conditions.

• Collect datasets covering historical crop yields, soil quality data, rainfall, temperature, humidity, and satellite vegetation indexes.
• Preprocess and engineer features like seasonal trends, lag variables (e.g., rainfall in previous months), and soil health metrics.
• Train regression models such as Random Forest Regressor, XGBoost Regressor, or LSTM time-series models for yield forecasting.
• Evaluate using MAE, RMSE, R² metrics to ensure accurate and robust predictive performance across seasons and regions.
• Deploy results into a dynamic dashboard or mobile app for farmers, policy-makers, and agricultural advisory services.

• FAO Agricultural Data Portal
• NASA POWER Climate Data Access
• Crop Yield Prediction Using ML – Kaggle