Comparative Analysis of Deep Learning and Optimization Techniques for Sugarcane Disease Classification

Sugarcane is a major agricultural crop in Tamil Nadu. In agriculture, there is considerable interest in applying digital image processing for crop protection and disease detection. The timely detection of sugarcane leaf diseases plays a crucial role in improving crop yield and protecting the livelihood of farmers who depend on healthy harvests. This study presents a novel hybrid deep learning approach that combines Convolutional Neural Network (CNN) features with Gray-Level Co-occurrence Matrix (GLCM) texture analysis to accurately classify sugarcane leaf diseases. A detailed dataset comprising 2,521 images of sugarcane leaves, encompassing seven major diseases, including Leaf Scald, Smut, Rust, Wilt, Red Root, Ratoon Stunting Disease, Sett Rot, and Grassy Shoot disease, was used for evaluation. The proposed CNN-Hybrid + GLCM model achieved an outstanding accuracy of 98.99%, surpassing models such as Baseline CNN (84.3%), VGG16 (89.5%), ResNet50 (90.2%), and Random Forest with CNN features (89.05%). With an average testing time of just 1.08 seconds per image, the model proves efficient for real-time applications. This solution offers a practical tool for farmers, facilitating early disease diagnosis, reducing crop loss, and easing the burden of manual monitoring. The integration of deep learning and texture-based features provides a powerful, farmer-friendly framework for smart agriculture and sustainable sugarcane cultivation.