Revolutionizing Rice Leaf Disease Detection: Next-Generation SMOREF-SVM Integrating Spider Monkey Optimization and Advanced Machine Learning Techniques

Abstract

Leaf diseases pose a significant challenge to rice productivity, which is critical as rice is a staple food for over half of the world's population and a major agricultural commodity. These diseases can lead to severe economic losses and jeopardize food security, particularly in regions heavily reliant on rice farming. Traditional detection methods, such as visual inspection and microscopy, are often inadequate for early disease identification, which is crucial for effective management and minimizing yield loss. This presentation introduces SMOREF-SVM, a novel approach that combines Spider Monkey Optimization (SMO) with Random Forest (RF) and Support Vector Machine (SVM) to improve the classification of rice leaf diseases. The innovation of SMOREF-SVM lies in its use of SMO for effective feature optimization, which selects the most relevant features from complex disease patterns, and its dual-classification framework using RF and SVM. Results demonstrate that SMOREF-SVM achieves an average accuracy of 98%, significantly outperforming standard SVM methods, which achieve around 90%. SMOREF-SVM also improves key metrics, including Precision, Recall, and F1 Score, by 5-10% for diseases with fewer samples, reaching Precision of 94%, Recall of 92%, and F1 Score of 93%. Additionally, ROC curve analysis shows an enhanced Area Under the Curve (AUC), approaching 0.98 for more disease classes, compared to 0.85 with traditional methods. This makes SMOREF-SVM a valuable tool for early and accurate disease detection, offering the potential to improve crop productivity and sustainability, addressing the critical challenges of disease management in agriculture.