An Efficient Crop Yield Prediction Framework Using Hybrid Machine Learning Model

Abstract

Given India's vast expanse and dense population, the prediction of agricultural yields is crucial for ensuring food security. The task, however, is complex due to the influence of a multitude of factors, such as agricultural practices, environmental conditions, and technological advancements. Existing machine learning (ML) models face difficulties due to the quality and variability of data, model overfitting, intricate model structures, insufficient feature engineering, and temporal dependencies. Therefore, a robust and efficient model that addresses these challenges is imperative. In this study, an investigation was conducted using five prevalent ML algorithms — Random Forest (RF), XGBoost, Decision Tree (DT), Support Vector Machine (SVM), and Linear Regression (LR) — on a crop prediction dataset sourced from Kaggle. Algorithms that exhibited the highest coefficient of determination (R2) were selected to construct a hybrid model for aggregate prediction. Results demonstrated that the proposed hybrid model, encompassing DT, XGBoost, and RF, surpassed individual classifiers in terms of R2score and outperformed the existing models, achieving an accuracy of 98.6%. This provides a robust and efficient framework for crop yield predictions. Consequently, a user-friendly tool, 'Crop Yield Predictor', was developed, rendering the model accessible and practical for on-ground applications in agriculture. This tool effectively translates complex data and algorithms into actionable insights, bridging the gap between advanced machine learning techniques and practical agricultural applications.