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.
Early diagnosis of tree leaf diseases is crucial for ensuring ecological stability, conserving biodiversity and sustainable agriculture productivity. Manual inspection and traditional image processing methods are often subjective, time-consuming, and susceptible to environmental changes like illumination variations, complex background and orientation of leaves in the images, which makes them difficult to work with. In response to the above drawbacks, the present work aims at proposing a novel, intelligent deep learning-based framework for automated detection of tree leaf diseases and supporting tree Agri-knowledge, namely PHYTOASSIST. The proposed approach is based on YOLO (You Only Look Once) convolutional neural network (CNN) for real-time object recognition of disease region on diseased leaf using high resolution image. To ensure robustness and generalisation capabilities to field scenarios, image pre-processing is used such as resize, normalisation and augmentation. The framework also includes a fertilizer and treatment recommendation module as well as an interactive agricultural chatbot that offers contextually relevant information for agricultural practice and disease prevention. Experimental results have proven the robustness of the accuracy (96.2%), precision (95.4%), recall (94.8%), and F1 score (95.1%) with an inference speed of less than 10 frames per second. The outcomes demonstrate the effectiveness of the design of the framework supporting the scalable agricultural disease monitoring and intelligent decision support in sustainable agricultural environments.
G. S, R. S, Sanjay A K et al.· 2026 4th International Confe...· 0 citations
Cotton is one of the most economically significant cash crops worldwide, and its productivity is severely affected by various leaf diseases that reduce crop quality and yield. Early and accurate disease identification is therefore essential for effective crop management and sustainable agricultural practices. Conventional disease diagnosis methods largely depend on manual inspection by experts, making them time-consuming, subjective, and less suitable for large-scale real-time monitoring under field conditions. To address these limitations, this study proposes MultiCotNet, a novel multispatial attention-based deep learning framework for robust classification of cotton leaf disease. The proposed architecture integrates multilevel attention mechanisms to enhance feature representation by capturing both local and global contextual information, thereby improving classification performance in challenging real-world scenarios involving illumination variations, occlusions, and complex backgrounds. Experimental evaluation conducted on a comprehensive real-world cotton leaf dataset demonstrates the effectiveness of the proposed model, achieving an accuracy of 96.6%, precision of 96.84%, recall of 97.21%, and F1-score of 96.96%. Comparative analysis further shows that MultiCotNet outperforms several existing baseline models in terms of classification accuracy and robustness. The proposed 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
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.
Jinqiao Nong, Yushan Lin· Advances in Engineering Tech...· 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
Maize leaf blight is a disastrous foliar disease in the world production of maize that causes significant losses in terms of yield annually. The classical machine learning (ML) and convolutional neural network (CNN) models often exhibit poor generalization under diverse field conditions due to variations in illumination, background complexity, and leaf morphology. To address these challenges, this study proposes a hybrid CNN–Transformer architecture optimized using Adaptive Genetic Optimization (AGO) for accurate maize leaf disease classification. The hybrid model utilizes CNN-based spatial feature extraction and global self-attention mechanism of the Vision Transformer (ViT) to capture both local and contextual patterns of diseases. The AGO algorithm dynamically optimizes key hyperparameters, including learning rate, batch size, embedding dimension, and attention heads, according to the population diversity and fitness evaluation, thereby improving convergence speed and classification performance. Experimental analysis conducted on an augmented maize leaf disease dataset demonstrated that the proposed model achieved an overall classification accuracy of 95.7%, outperforming conventional architectures including VGG16, ResNet50, DenseNet201, MobileNetV3, and a baseline ViT. Ablation studies, statistical stability analysis, and robustness evaluation further confirmed the effectiveness, reliability, and generalization capability of the proposed framework under varying field conditions. The proposed AGO-CNN–Transformer framework provides an effective and computationally feasible solution for intelligent maize disease diagnosis and precision agriculture applications.
Akhilesh Kumar, Ashish Kumar Pandey, L. S. Umrao· Journal of Crop Health· 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