Jul 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 33 references
TL;DR
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.
Abstract
The early and effective diagnosis of tomato leaf diseases is very important to enhance crop yield and reduce economic loss in precision agriculture. The conventional image-based methods are typically based on single architecture model, which cannot capture fine-grained lesion details and global contextual patterns simultaneously in the real-field. To this end, we introduce a deep hybrid Convolutional Neural Network (CNN) –Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) (as local feature extractor) and Swin Transformer (as global context encoder). The concatenated features vector is then fed to a shallow classifier to predict the disease. The model was tested on two datasets, namely a field dataset in agriculture areas from Madhya Pradesh (India) and a benchmark tomato leaf dataset. Experimental results revealed that the proposed scheme achieved accuracy of 92.83% on a primary dataset, and performance was significantly high with an accuracy of up to 95.65% in terms of generalization rate for computing technique models from various environmental conditions.
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
Introduction Maize is one of the most important food crops in the world, and foliar diseases can lead to significant yield losses if identification is not performed on time. Experts conducting manual inspections find it less effective and more subjective. Deep learning-based approaches utilizing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been demonstrated as a viable approach to automate disease diagnosis. Thus, while CNNs fail to capture wider context due to their local feature focus and ViTs need larger datasets and tend to miss finer-grained details. To overcome these limitations, we present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning. Methods The EDISP framework brings together the strengths of CNNs and ViTs to overcome their individual weaknesses. It is trained on a dataset that includes both controlled-environment and real-field maize leaf images, which helps it handle different environmental conditions. The data undergoes thorough preprocessing, including normalization, augmentation, and stratified splitting into training, validation, and test sets to support generalization. The CNN focuses on detailed local disease features, while the ViT captures broader contextual information across the maize leaf surfaces. Results The proposed EDISP model significantly outperforms standalone CNN and ViT Models in multiple performance metrics, achieving an overall classification accuracy of 99.40%, precision of 99.43%, recall of 99.38%, and an F1-score of 99.40%. Experimental results demonstrate that EDISP excels in identifying maize leaf diseases, including Common Rust, Gray Leaf Spot, Northern Leaf Blight, and Healthy leaves, with minimal false positives and negatives. External validation with an independent dataset further highlights the model’s robustness and ability to generalize to real-world conditions. Discussion The EDISP hybrid architecture, integrating CNNs and ViTs, provides a stronger method for accurate, automated maize leaf disease detection. Its robust performance, consistent results on controlled and field datasets shows robustness in diverse environments. However, EDISP’s effectiveness may be limited by image quality, lighting, or disease types not seen in training. These results highlight the promise of hybrid deep learning in precision agriculture and offer a scalable solution for disease detection, supporting farmers without expert diagnostic resources.
Aziz Ullah, Shah Hussain, Qi Hou et al.· Frontiers in Plant Science· 0 citations
Accurate and quick detection of plant leaf diseases is essential for precision agriculture to intervene promptly and boost crop yields. A new deep learning model called ResVNet has been introduced in this study. It combines the powerful local feature detection of ResNet152 with the global attention capabilities of Vision Transformer (ViT) and utilises Low-Rank Adaptation (LoRA) to accelerate fine-tuning. The PlantVillage dataset, which contains both healthy and diseased tomato samples, was used to train and test ResVNet. Experimental evaluation on the PlantVillage tomato dataset using stratified 5-fold cross-validation demonstrates that the proposed ResVNet model achieves a mean classification accuracy of 97.45%, along with superior macro-precision, macro-recall, and macro-F1 scores compared to existing deep learning architectures. The results of the confusion matrix and the ROC analysis validate its discriminatory power. The results highlight the potential of architectures strengthened with transformers in agricultural diagnostics. For real-time disease detection in the field, ResVNet is perfect for edge device deployment on drones and smartphones thanks to its high accuracy and adaptability. The application of Explainable AI (XAI) technologies for interpretability, integration with the Internet of Things (IoT), and multi-crop classification will all be explored in future studies. We will also look into model compression approaches so we can deploy efficiently in low-resource settings without sacrificing performance.
Abhishek Mathur, Shailendra Kumar Shrivastava· Tarım Bilimleri Dergisi· 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 study proposes a hybrid technique that integrates attention-weighted exponential pooling (AWEP) with CNN and Vision Transformer (ViT) to enhance feature representation and significantly improve classification performance and highlights that ViT improves embedding separability through t‑distributed stochastic neighbor embedding (t-SNE), thereby reducing overfitting and producing fewer misclassifications in visually similar classes.
Maddassar Jalal, Amandeep Kaur· Applied Fruit Science· 0 citations
An extensive set of experiments was conducted to evaluate the performance of the proposed model for plant disease detection, and it is demonstrated that the model achieves highly reliable results, with an accuracy of 97.13%.
Hayat Meddeber, M. Meddeber· ITEGAM- Journal of Engineeri...· 0 citations