Integrated Approach for Crop Yield Prediction in Telangana Region Using Ensemble Techniques and ARIMA Model
Keywords:
ARIMA, Crop yield, Ensemble, Machine learning, Metrics, Random Forest, Telangana.Abstract
This paper presents an integrated methodology for accurate and comprehensive crop yield prediction in the Telangana region, spanning the years 1966 to 2030. Leveraging an ensemble approach, our model combines the strengths of Random Forest Regressor at both the state and district levels, providing granular predictions for each administrative unit. Additionally, we employ an ARIMA model to forecast key meteorological and soil parameters from 2021 to 2030. The ensemble predictions are then integrated with historical data, resulting in a holistic forecast for crop yield. The methodology addresses data sparsity by replacing zeros with mean values, enhancing the reliability of predictions. The proposed approach is validated using robust metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, demonstrating the robustness and accuracy of the model. The study contributes to the field of precision agriculture, offering insights into the complex dynamics influencing crop yield and providing a valuable tool for sustainable planning in the Telangana region.
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