Aug 2026· Journal of Crop Health· Vol 78· 0 citations· 30 references
TL;DR
The proposed AGO-CNN–Transformer framework provides an effective and computationally feasible solution for intelligent maize disease diagnosis and precision agriculture applications.
Abstract
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.
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
Findings confirm that the hybrid CNN–Transformer architecture effectively enhances classification performance, robustness, and generalization in coffee leaf disease classification, with potential applications in precision agriculture and data-driven crop management.
This paper, proposes a Dual-Optimization Convolutional Neural Network to improve the accuracy and generalization
of maize leaf disease classification. To address the constraints of predefined CNN architectures and single-algorithm hyperparameter consistence optimization, we combine two meta-heuristics optimizers with an architectural attention layer. In the proposed dual-optimization framework, PSO determines the number of convolutional filters in a dynamic manner, and BO finetunes the size of dense layers and global learning rate. The intermediate hybrid CNN structure is then instantiated with the best
found FCSs and two other models are inspected: the hyper parameter hybrid model and the improved ancestral squeeze and
excitation-based hybrid model that introduces modified SE attention blocks for concentrating on more discriminative, lesionrelated zones. All models were trained with focal loss, dynamic L2 regularization, and Learning Rate Scheduler to obtain solid
and stable class imbalance handling. Competitive experiments over the test set show that the hybrid model leads to higher classification accuracy and generalization, which confirms that combined optimization on hyperparameters and attention-driven
learning directly supports establishing a cutting-edge diagnostic model in our proposed co-design strategy. The proposed model
achieved an accuracy of approximately 89%, outperforming the baseline CNN, PSO, and BO models, thus validating the effectiveness of the dual optimization framework
Rohit Maheshwari, Awamit Kumar, Amit Sharma· International Journal for Re...· 0 citations
Plant diseases are one of the many factors which reduce agricultural productivity and global food security. Accurate and early diagnosis of plant diseases helps to reduce significant losses to crops and aid in sustainable agriculture. In recent years, deep learning methods for plant disease diagnosis have been of great interest in the field of agriculture. This study proposes a hybrid model, Hybrid plant disease classification using deep learning models, which uses a Convolutional Neural Network (CNN) to obtain local features of the affected plant leaves and a pre-trained Vision Transformer (ViT) to get global features. In this work, the proposed hybrid model is validated using 15 plant disease classes of which different data augmentation techniques such as changing the light exposure and object orientation are employed. Experimental results revealed the convergence stability of the model, the strong generalization ability, and the better accuracy as compared with each of the models used independently.
Vallem Ranadheer Reddy· Dandao Xuebao/Journal of Bal...· 0 citations
A novel hybrid deep learning framework integrating Convolutional Neural Networks, Transformer-based attention mechanisms, and Long Short-Term Memory networks for spatio-temporal cotton leaf disease detection and classification is proposed, suitable for intelligent precision agriculture systems and real-time disease monitoring applications.
Prajakta Sunil Gupta, A. V. Zade· International journal of com...· 0 citations
A deep hybrid Convolutional Neural Network –Transformer architecture is introduced by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer (as local feature extractor) and Swin Transformer (as global context encoder) to predict tomato leaf diseases.
