AI-Driven Approach to Crop Recommendation: Tackling Class Imbalance and Feature Selection in Precision Agriculture

Abstract

Agricultural yields have significantly decreased over the years due to soil degradation and weather variability. Inefficient utilization of these farming resources has worsened the situation. To address this challenge, this study developed an AI-driven crop recommendation framework based on structured soil and climate data. The dataset comprises key features such as temperature, humidity, and rainfall, as well as soil pH, nitrogen, phosphorus, and potassium, collected from the Wannune axis of Tarka Local Government Area in Benue State, Nigeria. To handle the data imbalance in the dataset, the Synthetic Minority Over-Sampling Technique (SMOTE) was applied. Recursive Feature Elimination (RFE) was employed to optimize feature selection. Five tree-based models, including Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, and Extreme Gradient Boost (XGBoost), were evaluated. The XGBoost model yielded the best performance, with an accuracy of 99.65%. The use of indigenous data bridges the gap between reliability and the relevance of localization in precision agriculture. Future work includes integrating dynamic environmental variables and conducting field validation to improve scalability and adoption