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.
Abstract
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.
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.
Jinqiao Nong, Yushan Lin· Advances in Engineering Tech...· 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
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.
Background: Cowpea is a drought-resilient legume that is important in India, Africa and parts of Asia. In India, it is widely cultivated in arid and semi-arid regions, supporting rural nutrition and income. Leaf diseases, however, significantly reduce yields, making AI-based detection systems essential for timely and accurate diagnosis. Methods: This study presents a deep learning framework using DenseNet121 for multi-class classification of cowpea leaf conditions: Bacterial wilt, septoria leaf spot, mosaic virus and fresh leaves. A total of 2,273 annotated images were curated, preprocessed and divided into training, validation and test sets. Real-time data augmentation and transfer learning techniques were employed to improve model generalization. The model was trained using categorical cross-entropy loss and evaluated with various metrics. Result: The model achieved a test accuracy of 93.87%, with strong F1-scores and precision across all four classes. The matthews correlation coefficient (MCC) was 0.9184, indicating high reliability, while the multi-class ROC-AUC score reached 0.9931, showing excellent class separability. Confusion matrix and precision–recall analysis further confirmed robust performance, especially for bacterial wilt and Septoria leaf spot. These results support the model’s potential for integration into smart agricultural systems for early disease detection in cowpea cultivation.
Seng-phil Hong· Legume Research An Internati...· 0 citations
The escalating global food crisis, exacerbated by climate change-induced yield losses and the increasing impact of pests and plant diseases, has reached a critical level. The early and accurate detection of plant diseases is of strategic importance not only for ensuring sustainable agricultural production but also for reducing economic dependency and safeguarding food security. Although deep learning-based approaches proposed in the existing literature often achieve high classification accuracy, their decision-making processes largely remain opaque, thereby limiting model reliability and practical adoption. In this study, a MobileNetV2-based deep learning architecture was employed for plant leaf disease classification, and the Convolutional Block Attention Module (CBAM) was integrated to enhance model performance. By emphasizing salient regions within leaf images, CBAM improved classification accuracy while simultaneously reducing computational overhead associated with processing irrelevant features. Furthermore, to enhance model interpretability, the Grad-CAM technique was applied to visualize the specific features and image regions that influenced the model's predictions. The experimental results not only demonstrate the contribution of the attention mechanism to classification performance but also address a significant gap in transparency and reliability within deep learning-based agricultural decision support systems.
Ömer Furkan Kaplan, Dilber Çetintaş· Signal Processing and Commun...· 0 citations
A scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics and enables deployment on smartphones, drones, and edge devices for smart agriculture applications.
B Venkata Ramulu, Dr. Geeta Tripathi· International Journal of Eng...· 0 citations