Comparison of ResNet50 and ResNet101 Feature Extraction for Tea Leaf Disease Classification Using Support Vector Machine

Abstract

Introduction: Tea leaf diseases can substantially reduce crop quality and productivity, making early and accurate diagnosis important for effective disease management. This study compares ResNet50 and ResNet101 as pretrained deep feature extractors combined with Support Vector Machine (SVM) to determine whether a deeper residual architecture can improve discrimination among visually similar tea leaf disease classes. Method: Images were obtained from the Kaggle “Identifying Disease in Tea Leaves” dataset comprising eight classes. Data augmentation increased each class to 800 images, yielding 6,400 images that were divided into training and testing sets using an 80:20 ratio. ResNet50 and ResNet101 pretrained on ImageNet were used as fixed feature extractors, and the resulting feature vectors were standardized and classified using an RBF-kernel SVM. Results and Discussion: ResNet101–SVM achieved the best performance with 97.97% accuracy and precision, recall, and F1-score of 98%, substantially outperforming ResNet50–SVM, which achieved 89.15% accuracy, 90% precision, 89% recall, and 89% F1-score. The deeper ResNet101 architecture provided more discriminative representations for visually similar disease patterns, although a small number of misclassifications remained. Conclusion: ResNet101 combined with SVM provides a more accurate and reliable framework than ResNet50–SVM for multi-class tea leaf disease classification and offers a promising foundation for automated disease diagnosis systems.