Vol. 14, Issue 5 (2025)

Author(s):
P Sravan Kumar, Pooja, Sweta Ramole, Lakshmi Narasimhaiah and Ravi R Saxena

Abstract:
This study evaluates the analysis of machine learning (ML) and regression models for maize yield prediction across three districts in India: Raipur, Bilaspur, and Durg, using data from 1997 to 2022. Key climatic parameters, including rainfall (RF), relative humidity (RH), wind speed (WS), maximum temperature (Tmax), and minimum temperature (Tmin), were utilized to train and test four models: Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Regression (LR). The performance of the models was evaluated using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE)., and R-squared (R²). Results reveal that Random Forest consistently outperformed other models across all districts, achieving the highest R² values (0.87 in Raipur, 0.90 in Bilaspur, and 0.95 in Durg) and the lowest error rates. In contrast, Linear Regression exhibited the poorest performance, highlighting its limitations in handling complex agricultural datasets. The study demonstrates the superiority of ML techniques, particularly Random Forest, in accurately predicting maize yields, offering valuable insights for farmers and policymakers to optimize crop planning and resource allocation under varying climatic conditions. These findings underscore the potential of advanced ML models in enhancing agricultural productivity and decision-making in the face of climate variability. The analysis was conducted using R-software, ensuring robust and reproducible results.