ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images
The outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers adva...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2432 |
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| author | Jing Zhang Hao Zhou Kunyu Liu Yuguang Xu |
| author_facet | Jing Zhang Hao Zhou Kunyu Liu Yuguang Xu |
| author_sort | Jing Zhang |
| collection | DOAJ |
| description | The outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers advantages such as high resolution and strong timeliness, it faces dual challenges in the field of disease identification, such as complex background interference and irregular disease morphology. To address these issues, this study proposes an intelligent classification method for cassava diseases based on drone imagery and an ED-Swin Transformer. Firstly, we introduced the EMAGE (Efficient Multi-Scale Attention with Grouping and Expansion) module, which integrates the global distribution features and local texture details of diseased leaves in drone imagery through a multi-scale grouped attention mechanism, effectively mitigating the interference of complex background noise on feature extraction. Secondly, the DASPP (Deformable Atrous Spatial Pyramid Pooling) module was designed to use deformable atrous convolution to adaptively match the irregular boundaries of diseased areas, enhancing the model’s robustness to morphological variations caused by angles and occlusions in low-altitude drone photography. The results show that the ED-Swin Transformer model achieved excellent performance across five evaluation metrics, with scores of 94.32%, 94.56%, 98.56%, 89.22%, and 96.52%, representing improvements of 1.28%, 2.32%, 0.38%, 3.12%, and 1.4%, respectively. These experiments demonstrate the superior performance of the ED-Swin Transformer model in cassava classification networks. |
| format | Article |
| id | doaj-art-aaf3d5ab09e648168c2017e01934f9ee |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-aaf3d5ab09e648168c2017e01934f9ee2025-08-20T02:25:04ZengMDPI AGSensors1424-82202025-04-01258243210.3390/s25082432ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV ImagesJing Zhang0Hao Zhou1Kunyu Liu2Yuguang Xu3College of Artificial Intelligence & Computer Science, Xi’an University of Science and Technology, Xi’an 710600, ChinaCollege of Artificial Intelligence & Computer Science, Xi’an University of Science and Technology, Xi’an 710600, ChinaSchool of Economics and Management, Xidian University, Xi’an 710126, ChinaCollege of Artificial Intelligence & Computer Science, Xi’an University of Science and Technology, Xi’an 710600, ChinaThe outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers advantages such as high resolution and strong timeliness, it faces dual challenges in the field of disease identification, such as complex background interference and irregular disease morphology. To address these issues, this study proposes an intelligent classification method for cassava diseases based on drone imagery and an ED-Swin Transformer. Firstly, we introduced the EMAGE (Efficient Multi-Scale Attention with Grouping and Expansion) module, which integrates the global distribution features and local texture details of diseased leaves in drone imagery through a multi-scale grouped attention mechanism, effectively mitigating the interference of complex background noise on feature extraction. Secondly, the DASPP (Deformable Atrous Spatial Pyramid Pooling) module was designed to use deformable atrous convolution to adaptively match the irregular boundaries of diseased areas, enhancing the model’s robustness to morphological variations caused by angles and occlusions in low-altitude drone photography. The results show that the ED-Swin Transformer model achieved excellent performance across five evaluation metrics, with scores of 94.32%, 94.56%, 98.56%, 89.22%, and 96.52%, representing improvements of 1.28%, 2.32%, 0.38%, 3.12%, and 1.4%, respectively. These experiments demonstrate the superior performance of the ED-Swin Transformer model in cassava classification networks.https://www.mdpi.com/1424-8220/25/8/2432UAVimage processingplant diseaseswin transformer |
| spellingShingle | Jing Zhang Hao Zhou Kunyu Liu Yuguang Xu ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images Sensors UAV image processing plant disease swin transformer |
| title | ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images |
| title_full | ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images |
| title_fullStr | ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images |
| title_full_unstemmed | ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images |
| title_short | ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images |
| title_sort | ed swin transformer a cassava disease classification model integrated with uav images |
| topic | UAV image processing plant disease swin transformer |
| url | https://www.mdpi.com/1424-8220/25/8/2432 |
| work_keys_str_mv | AT jingzhang edswintransformeracassavadiseaseclassificationmodelintegratedwithuavimages AT haozhou edswintransformeracassavadiseaseclassificationmodelintegratedwithuavimages AT kunyuliu edswintransformeracassavadiseaseclassificationmodelintegratedwithuavimages AT yuguangxu edswintransformeracassavadiseaseclassificationmodelintegratedwithuavimages |