Comparative Analysis of Machine Learning Models for Crop Yield Prediction in the Telangana Region

P. Sowmya, Dr. A. V. Krishna Prasad

Abstract: The paper presents a comprehensive comparison of various predictive models employed for agricultural yield forecasting in the Telangana region. Leveraging ensemble methods, including Random Forest Regressor, ARIMA, and others, the study evaluates the performance of each model based on key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared. The analysis encompasses state-level and district-level forecasting, providing insights into the strengths and limitations of each model. Furthermore, the study investigates the impact of data sparsity on prediction accuracy, offering a nuanced understanding of model reliability in the context of agricultural yield forecasting.

Keywords: Comparison, Crop yield, Metrics, Forecasting, Telangana