Aug 2026· International Journal of Electrical and Computer Engineering (IJECE)· 0 citations
TL;DR
A comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification demonstrates that transfer learning can effectively improve classification performance in plant disease recognition tasks.
Abstract
Image-based techniques are widely used in plant disease classification to support agricultural productivity and facilitate early detection. This study presents a comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification. A publicly available dataset containing five categories—tomato bacterial spot, tomato late blight, tomato septoria leaf spot, tomato yellow leaf curl virus, and healthy leaves—was used in the experiments. Model performance was evaluated using several standard classification metrics, with emphasis on overall accuracy. The baseline CNN achieved an accuracy of 97.0%, whereas the ResNet50 model reached 99.6%. The results demonstrate that the ResNet50 model produces more stable and reliable predictions, particularly when distinguishing between visually similar disease classes. These findings confirm that transfer learning can effectively improve classification performance in plant disease recognition tasks.
Overall accuracy alone is insufficient for judging the reliability of multi-class plant disease detection systems, and practical deployment of CNN-based tomato disease detection therefore requires improved class balance, stronger validation, and attention to computational performance.
T. Adebiyi, A. Esan, A. Sobowale et al.· 0 citations
A hybrid framework integrating a Convolutional Neural Network with a Large Language Model to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations demonstrated that combining high-performance visual recognition with language-based reasoning enhanced both diagnostic reliability and interpretability in digital agriculture applications.
Frenky Riski Gilang Pratama, S. Surono, A. Thobirin· International Journal of Adv...· 0 citations
The FRCNN-FCMWeight model provides strong support for agricultural use cases and improves overall model generalization and enhances training robustness and identifies diseased regions effectively under highly variable field conditions.
Shashank Yadav, A. S. Tewari· Journal of Crop Health· 0 citations
The proposed CNN framework provides a scalable, computationally efficient, and intelligent solution for automated cotton leaf disease classification, contributing to the advancement of AI-driven precision agriculture and sustainable crop management.
Sonali Kamra, Vijay Laxmi· International Journal of Res...· 0 citations
The study demonstrates that transfer learning effectively enhances plant disease classification, enabling faster, more reliable and resource efficient detection for precision agriculture.
Chika K. Gangadharan, P. M. Jasmine, Roshni Alex et al.· Indian Journal of Agricultur...· 0 citations
Crop leaf diseases often differ by small lesion regions and similar color-texture patterns, which can limit the feature selectivity of compact CNN backbones when annotated images are insufficient. To address this problem, this study develops an improved ResNet18 for PlantVillage-based leaf disease recognition. The model combines three changes: an SE channel-recalibration unit for emphasizing lesion-related channels, Leaky ReLU for preserving gradient flow when activations are negative, and ImageNet-based transfer learning for faster adaptation to the target classes. Under the same training and evaluation protocol, the proposed network achieved 97.86% accuracy, with precision, recall, and F1-score of 97.63%, 97.41%, and 97.52%, respectively. These values exceeded the original ResNet18 and several commonly used CNN baselines. Ablation experiments showed stepwise gains from the three modifications, and Grad-CAM visualization indicated more concentrated responses over lesion regions. The results suggest that the modified ResNet18 is an effective compact baseline for crop leaf disease recognition, although validation under complex field conditions remains necessary.
