MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8
In response to the challenges of low accuracy in traditional pepper blight identification under natural complex conditions, particularly in detecting subtle infections on early-stage leaves, stems, and fruits. This study proposes a multi-site pepper blight disease image recognition algorithm based o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/4/839 |
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| author | Xiaodong Zheng Zichun Shao Yile Chen Hui Zeng Junming Chen |
| author_facet | Xiaodong Zheng Zichun Shao Yile Chen Hui Zeng Junming Chen |
| author_sort | Xiaodong Zheng |
| collection | DOAJ |
| description | In response to the challenges of low accuracy in traditional pepper blight identification under natural complex conditions, particularly in detecting subtle infections on early-stage leaves, stems, and fruits. This study proposes a multi-site pepper blight disease image recognition algorithm based on YOLOv8, named MSPB-YOLO. This algorithm effectively locates different infection sites on peppers. By incorporating the RVB-EMA module into the model, we can significantly reduce interference from shallow noise in high-resolution depth layers. Additionally, the introduction of the RepGFPN network structure enhances the model’s capability for multi-scale feature fusion, resulting in a marked improvement in multi-target detection accuracy. Furthermore, we optimized CIOU to DIOU by integrating the center distance of bounding boxes into the loss function; as a result, the model achieved an impressive mAP@0.5 score of 96.4%. This represents an enhancement of 2.2% over the original algorithm’s mAP@0.5. Overall, this model provides effective technical support for promoting intelligent management and disease prevention strategies for peppers. |
| format | Article |
| id | doaj-art-41bb57b31cd649938ceb2d6562f74688 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-41bb57b31cd649938ceb2d6562f746882025-08-20T02:17:13ZengMDPI AGAgronomy2073-43952025-03-0115483910.3390/agronomy15040839MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8Xiaodong Zheng0Zichun Shao1Yile Chen2Hui Zeng3Junming Chen4Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaSchool of Design, Jiangnan University, Wuxi 214122, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaIn response to the challenges of low accuracy in traditional pepper blight identification under natural complex conditions, particularly in detecting subtle infections on early-stage leaves, stems, and fruits. This study proposes a multi-site pepper blight disease image recognition algorithm based on YOLOv8, named MSPB-YOLO. This algorithm effectively locates different infection sites on peppers. By incorporating the RVB-EMA module into the model, we can significantly reduce interference from shallow noise in high-resolution depth layers. Additionally, the introduction of the RepGFPN network structure enhances the model’s capability for multi-scale feature fusion, resulting in a marked improvement in multi-target detection accuracy. Furthermore, we optimized CIOU to DIOU by integrating the center distance of bounding boxes into the loss function; as a result, the model achieved an impressive mAP@0.5 score of 96.4%. This represents an enhancement of 2.2% over the original algorithm’s mAP@0.5. Overall, this model provides effective technical support for promoting intelligent management and disease prevention strategies for peppers.https://www.mdpi.com/2073-4395/15/4/839pepper blightYOLOmulti-site detectionRepGFPNdisease prevention strategies |
| spellingShingle | Xiaodong Zheng Zichun Shao Yile Chen Hui Zeng Junming Chen MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8 Agronomy pepper blight YOLO multi-site detection RepGFPN disease prevention strategies |
| title | MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8 |
| title_full | MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8 |
| title_fullStr | MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8 |
| title_full_unstemmed | MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8 |
| title_short | MSPB-YOLO: High-Precision Detection Algorithm of Multi-Site Pepper Blight Disease Based on Improved YOLOv8 |
| title_sort | mspb yolo high precision detection algorithm of multi site pepper blight disease based on improved yolov8 |
| topic | pepper blight YOLO multi-site detection RepGFPN disease prevention strategies |
| url | https://www.mdpi.com/2073-4395/15/4/839 |
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