SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection
Plant disease detection remains a significant challenge, necessitating innovative approaches to enhance detection efficiency and accuracy. This study proposes an improved YOLOv8 model, SerpensGate-YOLOv8, specifically designed for plant disease detection tasks. Key enhancements include the incorpora...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1514832/full |
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| author | Yongzheng Miao Yongzheng Miao Wei Meng Wei Meng Xiaoyu Zhou Xiaoyu Zhou |
| author_facet | Yongzheng Miao Yongzheng Miao Wei Meng Wei Meng Xiaoyu Zhou Xiaoyu Zhou |
| author_sort | Yongzheng Miao |
| collection | DOAJ |
| description | Plant disease detection remains a significant challenge, necessitating innovative approaches to enhance detection efficiency and accuracy. This study proposes an improved YOLOv8 model, SerpensGate-YOLOv8, specifically designed for plant disease detection tasks. Key enhancements include the incorporation of Dynamic Snake Convolution (DySnakeConv) into the C2F module, which improves the detection of intricate features in complex structures, and the integration of the SPPELAN module, combining Spatial Pyramid Pooling (SPP) and Efficient Local Aggregation Network (ELAN) for superior feature extraction and fusion. Additionally, an innovative Super Token Attention (STA) mechanism was introduced to strengthen global feature modeling during the early stages of the network. The model leverages the PlantDoc dataset, a highly generalizable dataset containing 2,598 images across 13 plant species and 27 classes (17 diseases and 10 healthy categories). With these improvements, the model achieved a Precision of 0.719. Compared to the original YOLOv8, the mean Average Precision (mAP@0.5) improved by 3.3%, demonstrating significant performance gains. The results indicate that SerpensGate-YOLOv8 is a reliable and efficient solution for plant disease detection in real-world agricultural environments. |
| format | Article |
| id | doaj-art-a7d9d5e7c7504d1285f12bcfe273c820 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-a7d9d5e7c7504d1285f12bcfe273c8202025-01-20T07:20:14ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15148321514832SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detectionYongzheng Miao0Yongzheng Miao1Wei Meng2Wei Meng3Xiaoyu Zhou4Xiaoyu Zhou5School of Information Science and Technology, Beijing Forestry University, Beijing, ChinaEngineering Research Center for Forestry-Oriented Intelligent Information Processing, Beijing, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing, ChinaEngineering Research Center for Forestry-Oriented Intelligent Information Processing, Beijing, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing, ChinaEngineering Research Center for Forestry-Oriented Intelligent Information Processing, Beijing, ChinaPlant disease detection remains a significant challenge, necessitating innovative approaches to enhance detection efficiency and accuracy. This study proposes an improved YOLOv8 model, SerpensGate-YOLOv8, specifically designed for plant disease detection tasks. Key enhancements include the incorporation of Dynamic Snake Convolution (DySnakeConv) into the C2F module, which improves the detection of intricate features in complex structures, and the integration of the SPPELAN module, combining Spatial Pyramid Pooling (SPP) and Efficient Local Aggregation Network (ELAN) for superior feature extraction and fusion. Additionally, an innovative Super Token Attention (STA) mechanism was introduced to strengthen global feature modeling during the early stages of the network. The model leverages the PlantDoc dataset, a highly generalizable dataset containing 2,598 images across 13 plant species and 27 classes (17 diseases and 10 healthy categories). With these improvements, the model achieved a Precision of 0.719. Compared to the original YOLOv8, the mean Average Precision (mAP@0.5) improved by 3.3%, demonstrating significant performance gains. The results indicate that SerpensGate-YOLOv8 is a reliable and efficient solution for plant disease detection in real-world agricultural environments.https://www.frontiersin.org/articles/10.3389/fpls.2024.1514832/fullplant disease detectionYOLOv8complex environmentdeep learning in agricultureagricultural productivity |
| spellingShingle | Yongzheng Miao Yongzheng Miao Wei Meng Wei Meng Xiaoyu Zhou Xiaoyu Zhou SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection Frontiers in Plant Science plant disease detection YOLOv8 complex environment deep learning in agriculture agricultural productivity |
| title | SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection |
| title_full | SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection |
| title_fullStr | SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection |
| title_full_unstemmed | SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection |
| title_short | SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection |
| title_sort | serpensgate yolov8 an enhanced yolov8 model for accurate plant disease detection |
| topic | plant disease detection YOLOv8 complex environment deep learning in agriculture agricultural productivity |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1514832/full |
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