Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring
IntroductionPotatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning...
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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.2024.1485903/full |
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| author | Ruiqian Qin Yiming Wang Xiaoyan Liu Xiaoyan Liu Helong Yu |
| author_facet | Ruiqian Qin Yiming Wang Xiaoyan Liu Xiaoyan Liu Helong Yu |
| author_sort | Ruiqian Qin |
| collection | DOAJ |
| description | IntroductionPotatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification. However, complex climatic conditions in real agricultural environments challenge model robustness, and current mainstream models struggle with accurate recognition of the same diseases across different plant species.MethodsThis paper proposes the SIS-YOLOv8 model, which enhances adaptability to complex agricultural climates by improving the YOLOv8 network structure. The research introduces three key modules: 1) a Fusion-Inception Conv module to improve feature extraction against complex backgrounds like rain and haze; 2) a C2f-SIS module incorporating Style Randomization to enhance generalization ability for different crop diseases and extract more detailed disease features; and 3) an SPPF-IS module to boost model robustness through feature fusion. To reduce the model’s parameter size, this study employs the Dep Graph pruning method, significantly decreasing parameter volume by 19.9% and computational load while maintaining accuracy.ResultsExperimental results show that the SIS-YOLOv8 model outperforms the original YOLOv8n model in disease detection tasks for potatoes and tomatoes, with improvements of 8.2% in accuracy, 4% in recall rate, 5.9% in mAP50, and 6.3% in mAP50-95.DiscussionThrough these network structure optimizations, the SIS-YOLOv8 model demonstrates enhanced adaptability to complex agricultural environments, offering an effective solution for automatic crop disease detection. By improving model efficiency and robustness, our approach not only advances agricultural disease monitoring but also contributes to the broader adoption of AI-driven solutions for sustainable crop management in diverse climates. |
| format | Article |
| id | doaj-art-dd492b6f38814a85bb67a337fb4cb85a |
| 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-dd492b6f38814a85bb67a337fb4cb85a2025-01-09T10:45:35ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.14859031485903Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoringRuiqian Qin0Yiming Wang1Xiaoyan Liu2Xiaoyan Liu3Helong Yu4College of Information Technology, Jilin Agricultural University, Changchun, ChinaTeaching Resource Information Service Center, Changchun Institute of Education, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaChangchun Sci-Tech University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaIntroductionPotatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification. However, complex climatic conditions in real agricultural environments challenge model robustness, and current mainstream models struggle with accurate recognition of the same diseases across different plant species.MethodsThis paper proposes the SIS-YOLOv8 model, which enhances adaptability to complex agricultural climates by improving the YOLOv8 network structure. The research introduces three key modules: 1) a Fusion-Inception Conv module to improve feature extraction against complex backgrounds like rain and haze; 2) a C2f-SIS module incorporating Style Randomization to enhance generalization ability for different crop diseases and extract more detailed disease features; and 3) an SPPF-IS module to boost model robustness through feature fusion. To reduce the model’s parameter size, this study employs the Dep Graph pruning method, significantly decreasing parameter volume by 19.9% and computational load while maintaining accuracy.ResultsExperimental results show that the SIS-YOLOv8 model outperforms the original YOLOv8n model in disease detection tasks for potatoes and tomatoes, with improvements of 8.2% in accuracy, 4% in recall rate, 5.9% in mAP50, and 6.3% in mAP50-95.DiscussionThrough these network structure optimizations, the SIS-YOLOv8 model demonstrates enhanced adaptability to complex agricultural environments, offering an effective solution for automatic crop disease detection. By improving model efficiency and robustness, our approach not only advances agricultural disease monitoring but also contributes to the broader adoption of AI-driven solutions for sustainable crop management in diverse climates.https://www.frontiersin.org/articles/10.3389/fpls.2024.1485903/fulldeep learningdetection of diseasesobject detectionYOLOv8digital agriculture |
| spellingShingle | Ruiqian Qin Yiming Wang Xiaoyan Liu Xiaoyan Liu Helong Yu Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring Frontiers in Plant Science deep learning detection of diseases object detection YOLOv8 digital agriculture |
| title | Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring |
| title_full | Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring |
| title_fullStr | Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring |
| title_full_unstemmed | Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring |
| title_short | Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring |
| title_sort | advancing precision agriculture with deep learning enhanced sis yolov8 for solanaceae crop monitoring |
| topic | deep learning detection of diseases object detection YOLOv8 digital agriculture |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1485903/full |
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