Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8
The growth of strawberries is influenced by environmental diversity and spatial dispersion, which present significant challenges for accurate identification and real-time image processing in complex environments. This paper addresses these challenges by proposing an advanced recognition model based...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1541365/full |
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| author | Liping Bai Chenglei Xia Fei Liu Xing Yang Tai Zhang |
| author_facet | Liping Bai Chenglei Xia Fei Liu Xing Yang Tai Zhang |
| author_sort | Liping Bai |
| collection | DOAJ |
| description | The growth of strawberries is influenced by environmental diversity and spatial dispersion, which present significant challenges for accurate identification and real-time image processing in complex environments. This paper addresses these challenges by proposing an advanced recognition model based on YOLOv8, tailored for strawberry identification. In this study, we enhanced the YOLOv8 architecture by replacing the traditional backbone with an EfficientNetV2 feature extraction network and using ODConv instead of the standard convolution. The loss function was modified with a dynamic nonmonotonic focusing mechanism, and WiseIoU was introduced to replace the traditional CIoU. The experimental results showed that the proposed model outperformed the original YOLOv8 regarding mAP50, precision, and recall, with improvements of 16.91%, 14.92%, and 8.4%, respectively. Additionally, the model's lightness increased by 15.67%. The proposed model demonstrated superior accuracy in identifying strawberries of different ripeness levels. The improvements in the proposed model indicate its effectiveness in strawberry recognition tasks, providing more accurate results in varying environmental conditions. The lightweight nature of the model makes it suitable for deployment on picking robots, enhancing its practical applicability for real-time processing in agricultural settings. |
| format | Article |
| id | doaj-art-dfc4fcfda9ad4bb5babe876ceea4c23c |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-dfc4fcfda9ad4bb5babe876ceea4c23c2025-08-20T02:10:53ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-03-011610.3389/fpls.2025.15413651541365Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8Liping BaiChenglei XiaFei LiuXing YangTai ZhangThe growth of strawberries is influenced by environmental diversity and spatial dispersion, which present significant challenges for accurate identification and real-time image processing in complex environments. This paper addresses these challenges by proposing an advanced recognition model based on YOLOv8, tailored for strawberry identification. In this study, we enhanced the YOLOv8 architecture by replacing the traditional backbone with an EfficientNetV2 feature extraction network and using ODConv instead of the standard convolution. The loss function was modified with a dynamic nonmonotonic focusing mechanism, and WiseIoU was introduced to replace the traditional CIoU. The experimental results showed that the proposed model outperformed the original YOLOv8 regarding mAP50, precision, and recall, with improvements of 16.91%, 14.92%, and 8.4%, respectively. Additionally, the model's lightness increased by 15.67%. The proposed model demonstrated superior accuracy in identifying strawberries of different ripeness levels. The improvements in the proposed model indicate its effectiveness in strawberry recognition tasks, providing more accurate results in varying environmental conditions. The lightweight nature of the model makes it suitable for deployment on picking robots, enhancing its practical applicability for real-time processing in agricultural settings.https://www.frontiersin.org/articles/10.3389/fpls.2025.1541365/fullstrawberries recognitiontarget detectionimproved YOLOv8EfficientNetv2ODConvWise-IoU |
| spellingShingle | Liping Bai Chenglei Xia Fei Liu Xing Yang Tai Zhang Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 Frontiers in Plant Science strawberries recognition target detection improved YOLOv8 EfficientNetv2 ODConv Wise-IoU |
| title | Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 |
| title_full | Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 |
| title_fullStr | Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 |
| title_full_unstemmed | Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 |
| title_short | Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 |
| title_sort | full dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on yolov8 |
| topic | strawberries recognition target detection improved YOLOv8 EfficientNetv2 ODConv Wise-IoU |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1541365/full |
| work_keys_str_mv | AT lipingbai fulldimensionaldynamicconvolutionandprogressivelearningstrategyforstrawberryrecognitionbasedonyolov8 AT chengleixia fulldimensionaldynamicconvolutionandprogressivelearningstrategyforstrawberryrecognitionbasedonyolov8 AT feiliu fulldimensionaldynamicconvolutionandprogressivelearningstrategyforstrawberryrecognitionbasedonyolov8 AT xingyang fulldimensionaldynamicconvolutionandprogressivelearningstrategyforstrawberryrecognitionbasedonyolov8 AT taizhang fulldimensionaldynamicconvolutionandprogressivelearningstrategyforstrawberryrecognitionbasedonyolov8 |