Results indicate that the proposed model provides an effective method for tomato leaf disease recognition in intelligent agricultural monitoring scenarios and demonstrates that the proposed attention mechanism can enhance lesion localization and suppress irrelevant background responses.
Abstract
Tomato leaf diseases are important factors affecting tomato yield and quality. Accurate and efficient disease recognition is therefore essential for intelligent crop protection and agricultural monitoring. Although convolutional neural networks have achieved promising performance in plant disease recognition, complex field environments still present challenges such as background interference, illumination variation, and subtle lesion differences. To improve the discriminative capability of tomato leaf disease recognition, a ResNet34-based dual-attention network is proposed in this study. ResNet34 is adopted as the backbone network for hierarchical feature extraction, and the original classification structure is replaced by an attention-enhanced classification framework. A dual channel-spatial fusion attention module is introduced to enhance lesion-related feature responses by jointly modeling channel dependency and spatial saliency. In addition, depthwise convolutional operations are incorporated to improve feature adjustment and computational feasibility. Experiments are conducted on a seven-class tomato leaf dataset containing six disease categories and healthy leaves. The proposed method achieves an accuracy of 98.12%, precision of 98.82%, recall of 98.10%, and F1-score of 98.22%, outperforming ResNet34, ResNet50, DenseNet121, and ShuffleNetV2 under the same experimental conditions. The ablation results and Grad-CAM visualization further demonstrate that the proposed attention mechanism can enhance lesion localization and suppress irrelevant background responses. These results indicate that the proposed model provides an effective method for tomato leaf disease recognition in intelligent agricultural monitoring scenarios.
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.
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.
Sumana Budsabok, Wachiraporn Polpanumas, Piyanan Khongphai· International Journal of Ele...· 0 citations
The proposed MultiCotNet framework provides a scalable and reliable solution for early cotton disease detection and can support intelligent agricultural monitoring systems for timely disease management and improved crop productivity.
Sabari Nathan, S. A., K. S et al.· Journal of Cotton Research· 0 citations
Plant diseases are crucial for improving crop yield and ensuring sustainable agricultural practices, particularly for staple crops such as groundnut and paddy leaf. However, existing methods often suffer from limited feature discrimination, inadequate attention to disease-affected regions, and reduced performance under real-world conditions. To address these limitations, this research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification. Initially, the input leaf images are enhanced using Bilateral Filtering (BF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise and improve contrast. Subsequently, HSV color space segmentation is employed to precisely isolate disease-affected regions. The proposed Dual Attention Network (DuAtNet) integrates channel and spatial attention mechanisms within a ConvNeXt backbone to capture discriminative disease-specific features. An efficient Fuzzy Extreme Learning Machine (FELM) classifier is then utilized for final categorization into Healthy, Leaf Spot, Bacterial Wilt, and Leaf Blight classes. The effectiveness of the GOPI-NET is evaluated using precision, recall, specificity, accuracy, and F1-score. The experimental results demonstrate that GOPI-NET achieves an overall accuracy of 98.32%. The GOPI-NET improves classification accuracy by 1.29%, 1.40%, and 2.23% compared to GLDICCNN, DNN-CSA, and LeafNet respectively.
S. Sharmila, V. Jeyalakshmi· Scientific Reports· 0 citations
A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.
A. Elkholy, N. Elshennawy, Ahmed M. Gab Allah· Journal of King Saud Univers...· 0 citations
A modified AlexNet architecture for classifying field-captured tomato leaf images into seven disease categories was developed and generally focused on symptom-bearing leaf regions, whereas target spot was the most difficult category to classify.
Debabrat Bharali, Kanak C. Bora, Rashel Sarkar et al.· Journal of Scientific Resear...· 0 citations