Hybrid PSO-ACO Optimization for Rice Leaf Disease Classification Using Random Forest and Support Vector Machines

Abstract

This study proposes a hybrid machine learning framework for rice leaf disease detection by combining handcrafted feature extraction with metaheuristic optimization and classical classifiers. Using a dataset of 6,000 rice leaf images across seven classes, features including color, texture, shape, and edge were extracted and optimized using Spider Monkey Optimization (SMO), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Classification was conducted using Random Forest Classifier (RFC) and Support Vector Classifier (SVC), both with and without hyperparameter tuning. Experimental results revealed that PSO consistently outperformed other optimizers, achieving 91.00% accuracy with RFC and 94.64% with SVC when all features and optimal parameters were used. While SMO also showed strong performance, ACO yielded less consistent results. These findings highlight the importance of combining comprehensive feature engineering with adaptive optimization strategies to improve classification accuracy. Compared to previous SMO-based approaches, the proposed PSO-ACO framework demonstrated improved stability and scalability. The proposed framework is interpretable, efficient, and scalable, making it suitable for practical deployment in precision agriculture. Future research directions include integrating deep learning with handcrafted features, developing adaptive metaheuristics, and implementing real-time mobile detection systems.