ArticleHeliyon2024
A novel hierarchical framework for plant leaf disease detection using residual vision transformer.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Explainable transformer framework for fast cotton leaf diagnostics and fabric defect detection.iScience · 2026Article
- High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.Scientific reports · 2025Article
- Ensemble-based feature fusion for accurate plant disease classification using pre-trained models.Scientific reports · 2025Article
- Lightweight grape leaf disease recognition method based on transformer framework.Scientific reports · 2025Article
- Plant leaf disease detection using vision transformers for precision agriculture.Scientific reports · 2025Article
- ADQ-YOLOv8m: a precise detection model of sugarcane disease in complex environment.Frontiers in plant science · 2025Article
- Enhancing multiclass plant disease classification using GAN-boosted vision transformer with XAI insights.Frontiers in plant science · 2025Article
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Authors and funding
3 authors.
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Abstract
Early detection of plant leaf diseases accurately and promptly is very crucial for safeguarding agricultural crop productivity and ensuring food security. During their life cycle, plant leaves get diseased because of multiple factors like bacteria, fungi, weather conditions, etc. In this work, the authors propose a model that aids in the early detection of leaf diseases using a novel hierarchical residual vision transformer using improved Vision Transformer and ResNet9 models. The proposed model can extract more meaningful and discriminating details by reducing the number of trainable parameters with a smaller number of computations. The proposed method is evaluated on the Local Crop dataset, Plant Village dataset, and Extended Plant Village Dataset with 13, 38, and 51 different leaf disease classes. The proposed model is trained using the best trail parameters of Improved Vision Transformer and classified the features using ResNet 9. Performance evaluation is carried out on a wide aspects over the aforementioned datasets and results revealed that the proposed model outperforms other models such as InceptionV3, MobileNetV2, and ResNet50.
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