An advanced deep learning method for pepper diseases and pests detection
Abstract Despite the significant progress in deep learning-based object detection, existing models struggle to perform optimally in complex agricultural environments. To address these challenges, this study introduces YOLO-Pepper, an enhanced model designed specifically for greenhouse pepper disease...
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| Format: | Article |
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BMC
2025-05-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01387-4 |
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| author | Xuewei Wang Jun Liu Qian Chen |
| author_facet | Xuewei Wang Jun Liu Qian Chen |
| author_sort | Xuewei Wang |
| collection | DOAJ |
| description | Abstract Despite the significant progress in deep learning-based object detection, existing models struggle to perform optimally in complex agricultural environments. To address these challenges, this study introduces YOLO-Pepper, an enhanced model designed specifically for greenhouse pepper disease and pest detection, overcoming three key obstacles: small target recognition, multi-scale feature extraction under occlusion, and real-time processing demands. Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. Evaluated on a diverse dataset of 8046 annotated images, YOLO-Pepper achieves state-of-the-art performance, with 94.26% mAP@0.5 at 115.26 FPS, marking an 11.88 percentage point improvement over YOLOv10n (82.38% mAP@0.5) while maintaining a lightweight structure (2.51 M parameters, 5.15 MB model size) optimized for edge deployment. Comparative experiments highlight YOLO-Pepper’s superiority over nine benchmark models, particularly in detecting small and occluded targets. By addressing computational inefficiencies and refining small object detection capabilities, YOLO-Pepper provides robust technical support for intelligent agricultural monitoring systems, making it a highly effective tool for early disease detection and integrated pest management in commercial greenhouse operations. |
| format | Article |
| id | doaj-art-75b6418df1464a90bf3579a0b30cd8da |
| institution | DOAJ |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-75b6418df1464a90bf3579a0b30cd8da2025-08-20T03:22:03ZengBMCPlant Methods1746-48112025-05-0121111810.1186/s13007-025-01387-4An advanced deep learning method for pepper diseases and pests detectionXuewei Wang0Jun Liu1Qian Chen2Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and TechnologyShandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and TechnologySchool of Computer, Sichuan Technology and Business UniversityAbstract Despite the significant progress in deep learning-based object detection, existing models struggle to perform optimally in complex agricultural environments. To address these challenges, this study introduces YOLO-Pepper, an enhanced model designed specifically for greenhouse pepper disease and pest detection, overcoming three key obstacles: small target recognition, multi-scale feature extraction under occlusion, and real-time processing demands. Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. Evaluated on a diverse dataset of 8046 annotated images, YOLO-Pepper achieves state-of-the-art performance, with 94.26% mAP@0.5 at 115.26 FPS, marking an 11.88 percentage point improvement over YOLOv10n (82.38% mAP@0.5) while maintaining a lightweight structure (2.51 M parameters, 5.15 MB model size) optimized for edge deployment. Comparative experiments highlight YOLO-Pepper’s superiority over nine benchmark models, particularly in detecting small and occluded targets. By addressing computational inefficiencies and refining small object detection capabilities, YOLO-Pepper provides robust technical support for intelligent agricultural monitoring systems, making it a highly effective tool for early disease detection and integrated pest management in commercial greenhouse operations.https://doi.org/10.1186/s13007-025-01387-4Pepper diseases and pests detectionDeep learningYOLOv10Small object detectionAgricultural scenarios |
| spellingShingle | Xuewei Wang Jun Liu Qian Chen An advanced deep learning method for pepper diseases and pests detection Plant Methods Pepper diseases and pests detection Deep learning YOLOv10 Small object detection Agricultural scenarios |
| title | An advanced deep learning method for pepper diseases and pests detection |
| title_full | An advanced deep learning method for pepper diseases and pests detection |
| title_fullStr | An advanced deep learning method for pepper diseases and pests detection |
| title_full_unstemmed | An advanced deep learning method for pepper diseases and pests detection |
| title_short | An advanced deep learning method for pepper diseases and pests detection |
| title_sort | advanced deep learning method for pepper diseases and pests detection |
| topic | Pepper diseases and pests detection Deep learning YOLOv10 Small object detection Agricultural scenarios |
| url | https://doi.org/10.1186/s13007-025-01387-4 |
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