Lightweight grape leaf disease recognition method based on transformer framework
Abstract Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addresses the problems in grape leaf disease re...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13689-7 |
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| author | Ning Zhang Enxu Zhang Guowei Qi Fei Li Cheng Lv |
| author_facet | Ning Zhang Enxu Zhang Guowei Qi Fei Li Cheng Lv |
| author_sort | Ning Zhang |
| collection | DOAJ |
| description | Abstract Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addresses the problems in grape leaf disease recognition under small-sample conditions, such as the difficulty in capturing multi-scale features, the minuteness of features, and the weak adaptability of traditional data augmentation methods. It proposes a solution that combines a multi-scale feature hybrid fusion architecture with data augmentation. The innovation of this study lies in the following four dimensions: (1) Utilize generative models to enhance the cross-category data balancing ability under small-sample conditions and enrich the sample information in the dataset. (2) Innovatively propose the LVT Block, a multi-scale information perception hybrid module based on the Ghost and Transformer structures. This module can effectively acquire and fuse multi-scale information and global information in the feature map. (3) Use the dense connection method to combine the LVT Block and the MARI Block to propose a new architecture, the DLVT Block. By fusing multi-scale information and global information, it improves the richness of feature information. It also uses the MARI to enhance the model’s perception of disease areas and constructs an end-to-end lightweight model, DLVTNet, using the DLVT Block. Experiments show that this method achieves an average recognition rate of 98.48% on the New Plant Diseases Dataset. The number of parameters is reduced to 42.7% of that of MobileNetV4, and it maintains an accuracy of 96.12% in the tomato leaf disease test. This paper embeds pathological features into the generative adversarial process, which can effectively alleviate the problem of insufficient samples in intelligent agricultural detection. It provides a new method system with strong interpretability and excellent generalization performance for disease detection. |
| format | Article |
| id | doaj-art-89fa619e566044beb56b330a3d3e9dda |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-89fa619e566044beb56b330a3d3e9dda2025-08-20T03:05:17ZengNature PortfolioScientific Reports2045-23222025-08-0115112510.1038/s41598-025-13689-7Lightweight grape leaf disease recognition method based on transformer frameworkNing Zhang0Enxu Zhang1Guowei Qi2Fei Li3Cheng Lv4Engineering Research Center of Hydrogen Energy Equipment & Safety Detection, Universities of Shaanxi Province, Xijing UniversityEngineering Research Center of Hydrogen Energy Equipment & Safety Detection, Universities of Shaanxi Province, Xijing UniversityEngineering Research Center of Hydrogen Energy Equipment & Safety Detection, Universities of Shaanxi Province, Xijing UniversityEngineering Research Center of Hydrogen Energy Equipment & Safety Detection, Universities of Shaanxi Province, Xijing UniversityEngineering Research Center of Hydrogen Energy Equipment & Safety Detection, Universities of Shaanxi Province, Xijing UniversityAbstract Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addresses the problems in grape leaf disease recognition under small-sample conditions, such as the difficulty in capturing multi-scale features, the minuteness of features, and the weak adaptability of traditional data augmentation methods. It proposes a solution that combines a multi-scale feature hybrid fusion architecture with data augmentation. The innovation of this study lies in the following four dimensions: (1) Utilize generative models to enhance the cross-category data balancing ability under small-sample conditions and enrich the sample information in the dataset. (2) Innovatively propose the LVT Block, a multi-scale information perception hybrid module based on the Ghost and Transformer structures. This module can effectively acquire and fuse multi-scale information and global information in the feature map. (3) Use the dense connection method to combine the LVT Block and the MARI Block to propose a new architecture, the DLVT Block. By fusing multi-scale information and global information, it improves the richness of feature information. It also uses the MARI to enhance the model’s perception of disease areas and constructs an end-to-end lightweight model, DLVTNet, using the DLVT Block. Experiments show that this method achieves an average recognition rate of 98.48% on the New Plant Diseases Dataset. The number of parameters is reduced to 42.7% of that of MobileNetV4, and it maintains an accuracy of 96.12% in the tomato leaf disease test. This paper embeds pathological features into the generative adversarial process, which can effectively alleviate the problem of insufficient samples in intelligent agricultural detection. It provides a new method system with strong interpretability and excellent generalization performance for disease detection.https://doi.org/10.1038/s41598-025-13689-7Grape leaf diseaseDeep learningTransformerLightweight modelAttention mechanisms |
| spellingShingle | Ning Zhang Enxu Zhang Guowei Qi Fei Li Cheng Lv Lightweight grape leaf disease recognition method based on transformer framework Scientific Reports Grape leaf disease Deep learning Transformer Lightweight model Attention mechanisms |
| title | Lightweight grape leaf disease recognition method based on transformer framework |
| title_full | Lightweight grape leaf disease recognition method based on transformer framework |
| title_fullStr | Lightweight grape leaf disease recognition method based on transformer framework |
| title_full_unstemmed | Lightweight grape leaf disease recognition method based on transformer framework |
| title_short | Lightweight grape leaf disease recognition method based on transformer framework |
| title_sort | lightweight grape leaf disease recognition method based on transformer framework |
| topic | Grape leaf disease Deep learning Transformer Lightweight model Attention mechanisms |
| url | https://doi.org/10.1038/s41598-025-13689-7 |
| work_keys_str_mv | AT ningzhang lightweightgrapeleafdiseaserecognitionmethodbasedontransformerframework AT enxuzhang lightweightgrapeleafdiseaserecognitionmethodbasedontransformerframework AT guoweiqi lightweightgrapeleafdiseaserecognitionmethodbasedontransformerframework AT feili lightweightgrapeleafdiseaserecognitionmethodbasedontransformerframework AT chenglv lightweightgrapeleafdiseaserecognitionmethodbasedontransformerframework |