A COMPARATIVE STUDY OF CNN AND TRANSFORMER-BASED DEEP LEARNING MODELS FOR TEA LEAF DISEASE RECOGNITION

Busra Er¹ , VOLKAN KAYA²

This study presents a comparative analysis of six deep learning models for the automatic classification of eight different disease categories found in tea leaves. The dataset used in the study was divided into three parts with 70% training, 15% validation, and 15% testing ratios. As part of the experimental evaluation, five convolutional neural network (CNN) based architectures (ResNet50, DenseNet121, EfficientNet-B0, MobileNetV3-Large, and ConvNeXt-Tiny) and one Transformer-based model (Vision Transformer, ViT-Small) were tested using the same training strategies. The models were trained using a transfer learning and fine-tuning approach; performance metrics were reported based on accuracy, precision, recall, and F1-score values. In addition, the number of parameters and the prediction time per image were calculated for each model. Experimental results show that the DenseNet121 model achieved the highest success rate in the validation dataset, while the ConvNeXt-Tiny architecture achieved the highest accuracy and F1-score values in the standalone test dataset. The findings indicate that modern CNN-based architectures offer high generalization capabilities in the classification of tea leaf diseases. The results obtained serve as a comparative reference for future studies in the field of agricultural image analysis.

Keywords
Tea Leaf Disease Classification, Deep Learning, Convolutional Neural Networks, Vision Transformer, Plant Disease Detection,