Deep learning method for cucumber disease detection in complex environments for new agricultural productivity
Abstract Cucumber disease detection under complex agricultural conditions faces significant challenges due to multi-scale variation, background clutter, and hardware limitations. This study proposes YOLO-Cucumber, an improved lightweight detection algorithm based on YOLOv11n, incorporating four key...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Plant Biology |
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| Online Access: | https://doi.org/10.1186/s12870-025-06841-y |
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| author | Jun Liu Xuewei Wang Qian Chen Peng Yan Xin Liu |
| author_facet | Jun Liu Xuewei Wang Qian Chen Peng Yan Xin Liu |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | Abstract Cucumber disease detection under complex agricultural conditions faces significant challenges due to multi-scale variation, background clutter, and hardware limitations. This study proposes YOLO-Cucumber, an improved lightweight detection algorithm based on YOLOv11n, incorporating four key innovations: (1) Deformable Convolutional Networks (DCN) for enhanced feature extraction of irregular targets, (2) a P2 prediction layer for fine-grained detection of early-stage lesions, (3) a Target-aware Loss (TAL) function addressing class imbalance, and (4) Channel Pruning via Batch Normalization (CPBN) for model compression. Experiments on our cucumber disease dataset demonstrate that YOLO-Cucumber achieves a 6.5% improvement in mAP@50 (93.8%), while reducing model size by 3.87 MB and increasing inference speed to 218 FPS. The model effectively handles symptom variability and complex detection scenarios, outperforming mainstream detection algorithms in accuracy, speed, and compactness, making it ideal for embedded agricultural applications. |
| format | Article |
| id | doaj-art-df026757f87f446b887339fbc9b2d413 |
| institution | Kabale University |
| issn | 1471-2229 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Plant Biology |
| spelling | doaj-art-df026757f87f446b887339fbc9b2d4132025-08-20T03:42:40ZengBMCBMC Plant Biology1471-22292025-07-0125111810.1186/s12870-025-06841-yDeep learning method for cucumber disease detection in complex environments for new agricultural productivityJun Liu0Xuewei Wang1Qian Chen2Peng Yan3Xin Liu4Shandong Engineering Research Center of Green and High-value Marine Fine Chemical, Weifang University of Science and TechnologyShandong Engineering Research Center of Green and High-value Marine Fine Chemical, Weifang University of Science and TechnologySchool of Computer, Sichuan Technology and Business UniversityThe Industry-Education Integration Office, Sichuan Technology and Business UniversityShandong Engineering Research Center of Green and High-value Marine Fine Chemical, Weifang University of Science and TechnologyAbstract Cucumber disease detection under complex agricultural conditions faces significant challenges due to multi-scale variation, background clutter, and hardware limitations. This study proposes YOLO-Cucumber, an improved lightweight detection algorithm based on YOLOv11n, incorporating four key innovations: (1) Deformable Convolutional Networks (DCN) for enhanced feature extraction of irregular targets, (2) a P2 prediction layer for fine-grained detection of early-stage lesions, (3) a Target-aware Loss (TAL) function addressing class imbalance, and (4) Channel Pruning via Batch Normalization (CPBN) for model compression. Experiments on our cucumber disease dataset demonstrate that YOLO-Cucumber achieves a 6.5% improvement in mAP@50 (93.8%), while reducing model size by 3.87 MB and increasing inference speed to 218 FPS. The model effectively handles symptom variability and complex detection scenarios, outperforming mainstream detection algorithms in accuracy, speed, and compactness, making it ideal for embedded agricultural applications.https://doi.org/10.1186/s12870-025-06841-yCucumber disease detectionYOLOv11nDeformable Convolution Networks (DCN)Target-aware LossChannel pruningEmbedded deployment |
| spellingShingle | Jun Liu Xuewei Wang Qian Chen Peng Yan Xin Liu Deep learning method for cucumber disease detection in complex environments for new agricultural productivity BMC Plant Biology Cucumber disease detection YOLOv11n Deformable Convolution Networks (DCN) Target-aware Loss Channel pruning Embedded deployment |
| title | Deep learning method for cucumber disease detection in complex environments for new agricultural productivity |
| title_full | Deep learning method for cucumber disease detection in complex environments for new agricultural productivity |
| title_fullStr | Deep learning method for cucumber disease detection in complex environments for new agricultural productivity |
| title_full_unstemmed | Deep learning method for cucumber disease detection in complex environments for new agricultural productivity |
| title_short | Deep learning method for cucumber disease detection in complex environments for new agricultural productivity |
| title_sort | deep learning method for cucumber disease detection in complex environments for new agricultural productivity |
| topic | Cucumber disease detection YOLOv11n Deformable Convolution Networks (DCN) Target-aware Loss Channel pruning Embedded deployment |
| url | https://doi.org/10.1186/s12870-025-06841-y |
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