Hybrid and Classical Machine Learning Models for Mixed-crop Recommendation in Precision Agriculture: An Empirical Performance Analysis

Precise mixed-crop recommendation is essential for increasing agricultural productivity, improving land utilization, and supporting sustainable farming. Effective crop combinations depend on several environmental and soil factors, making accurate recommendations a challenging task. This study compared classical and hybrid machine learning approaches for mixed-crop recommendation using an indigenous dataset from Andhra Pradesh, India. The dataset consisted of seven key features: nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall. The study evaluated Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Extra Trees (ET), alongside a proposed hybrid Random Forest-Extra Trees (RF-ET) model. Performance was measured using accuracy, precision, recall, F1-score, k-fold cross-validation, and confusion matrix analysis. The results show that the proposed hybrid RF-ET model achieved the best performance, obtaining 95.91% accuracy, 95.98% precision, 95.91% recall, and 95.94% F1-score. The model also demonstrated strong robustness through cross-validation. These findings indicate that hybrid machine learning can significantly improve the accuracy and reliability of mixed-crop recommendation systems, providing farmers with better decision-support tools for sustainable and efficient agricultural production.