Hybrid Deep Learning and Machine Learning Framework for Automated Tomato Leaf Disease Classifcation

Abstract

Tomato leaf diseases significantly impact crop productivity, necessitating accurate and effcient diagnostic tools. This study proposes a hybrid framework that integrates deep learning-based localization and segmentation with handcrafted feature extraction and classical machine learning for tomato leaf disease classification. Specifically, YOLOv8 is used for object detection and SAM for segmenting diseased regions. Features are then extracted using HSV color space, GLCM, and LBP descriptors. To address class imbalance, the SMOTE technique was applied, expanding the original 48,243-image dataset to 102,465 balanced samples across 11 disease categories. Multiple classifiers were evaluated, with Random Forest achieving the highest performance-over 90% accuracy and a macro F1-score of 0.90. Importantly, recall for minority classes improved markedly after balancing. The proposed system demonstrates strong potential for deployment in real-world agricultural environments due to its low computational cost and robustness under varying conditions. Future work will explore multi-crop generalization, real-time inference, and field validation under challenging conditions such as lighting variation and occlusion.