Comparative Analysis of Machine Learning Models for Crop Yield Prediction Across Multiple Crop Types

In this study, we delve into the predictive capabilities of machine learning (ML) regression models across various crops, aiming to revolutionize crop yield forecasting through technological advancement. Our research meticulously assesses the performance of several prominent ML algorithms, including Gaussian Process, Random Forest, K-Nearest Neighbors (KNN), XGBoost, and AdaBoost, applied to six critical crop datasets: wheat, chickpea, pearl millets, Rabi Sorghum, sugarcane, and maize. Our methodology hinges on the innovative manipulation of dataset features, exploring the effects of both feature reduction and expansion on model accuracy. Our findings reveal a nuanced landscape where no single model universally excels; instead, specific models demonstrate superior performance for particular crops. For instance, Gaussian Process models exhibit remarkable predictive accuracy for wheat and pearl millets with fewer features, highlighting their efficacy in simpler scenarios. Conversely, the Random Forest model emerges as the frontrunner for chickpea yield prediction, showcasing its robustness amidst varying data dimensions. Notably, the KNN model underperforms in predicting Rabi Sorghum yields, whereas AdaBoost struggles with maize, underscoring the critical need for model-specific optimizations based on crop characteristics. This research not only benchmarks the effectiveness of various ML models in the agricultural domain but also sheds light on the strategic importance of feature selection in enhancing predictive accuracy. By presenting a detailed analysis of model performances across multiple crops, we provide invaluable insights for researchers and practitioners aiming to employ ML techniques for agricultural yield prediction. Our study paves the way for more informed decision-making in agriculture, promoting the adoption of ML technologies to ensure food security and sustainable farming practices in the face of global challenges.