Enhanced Tomato Leaf Disease Classification and Localization using Advanced Feature Extraction and Transfer Learning

Tomato plants are susceptible to various diseases that significantly impact crop yield and quality. Accurate and timely identification of these diseases is crucial for effective management and mitigation. This study presents a deep learning-based methodology for enhancing disease prediction, classification, and precise localization of affected areas within tomato leaves. The proposed approach leverages a combination of statistical, texture (Tamura and GLCM), geometry, and color features extracted from leaf images. To further enrich feature representation, wavelet analysis is employed. The model not only classifies ten prevalent tomato diseases but also estimates the proportion of affected leaf area, providing valuable insights for disease severity assessment. Evaluated on a dataset comprising 10,000 images, our model achieves remarkable accuracy of 99.50%. This robust performance underscores the efficacy of our approach in accurate disease diagnosis, benefitting farmers and researchers by enabling prompt intervention and efficient disease management strategies.