Optimizing Rice Production Forecasting Through Integrating Multiple Linear Regression with Recursive Feature Elimination

Abstract

Rice is a staple food for most Nigerians, making accurate yield prediction is crucial for food security. This study addresses the limitations of traditional forecasting methods by employing Multiple Linear Regression (MLR) coupled with Recursive Feature Elimination (RFE) to predict rice yield in Adamawa and Cross River states, characterized by distinct agroclimatic conditions. Utilizing climatic data and historical yield records from 1990 to 2022, we trained and evaluated MLR and compared the MLR results with two other machine learning models (XGBoost, and K Nearest Neighbours). RFE-optimized feature selection identified All-sky Photosynthetically Active Radiation (PAR) as a key factor. MLR demonstrated a very stable prediction performance with R² values of 0.90 and 0.92 for Adamawa and Cross River, respectively, after RFE. This research contributes to developing advanced Agro-information systems, supporting informed agricultural decision-making, and enhancing Nigeria's food security.