2025, Vol. 10, Special Issue 9
A machine learning models for crop yield prediction of Chhattisgarh state, India based on weather parameters
Author(s): Yamuna Kashyap, MK Pradhan, Anjali Gilhare, Sapna Bhardwaj and Monika Paikra
Abstract: This study focuses on chickpea yield prediction in the Bastar Plateau region of Chhattisgarh State using machine learning models. Weather parameters such as rainfall, maximum temperature, minimum temperature, relative humidity-I, and relative humidity-II were considered along with yield data for the period 1983-2023. Support Vector Machine (SVM) models with Radial Basis Function, were compared with K-Nearest Neighbors (KNN), Linear Regression, and Decision Tree. Among these, the SVM consistently outperformed other models, achieving the highest R² and the lowest error values. The findings highlight the efficiency of kernel-based approaches in capturing complex non-linear relationships between weather variables and yield, demonstrating the potential of machine learning for accurate yield forecasting and agricultural planning.
Pages: 07-11 | Views: 350 | Downloads: 7Download Full Article: Click HereHow to cite this article:
Yamuna Kashyap, MK Pradhan, Anjali Gilhare, Sapna Bhardwaj, Monika Paikra. A machine learning models for crop yield prediction of Chhattisgarh state, India based on weather parameters. Int J Stat Appl Math 2025;10(9S):07-11.
