Research on detection of wheat tillers in natural environment based on YOLOv8-MRF
To bolster agricultural efficiency and precision, this study introduces the YOLOv8-MRF model (multi-path coordinate attention, receptive field attention convolution, and Focaler-CIoU-optimized YOLOv8), a groundbreaking advancement in automated detection of wheat tillers. This model transcends tradit...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003241 |
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| author | Min Liang Yuchen Zhang Jian Zhou Fengcheng Shi Zhiqiang Wang Yu Lin Liang Zhang Yaxi Liu |
| author_facet | Min Liang Yuchen Zhang Jian Zhou Fengcheng Shi Zhiqiang Wang Yu Lin Liang Zhang Yaxi Liu |
| author_sort | Min Liang |
| collection | DOAJ |
| description | To bolster agricultural efficiency and precision, this study introduces the YOLOv8-MRF model (multi-path coordinate attention, receptive field attention convolution, and Focaler-CIoU-optimized YOLOv8), a groundbreaking advancement in automated detection of wheat tillers. This model transcends traditional manual methods prone to subjectivity and inefficiency. This approach integrates an enhanced multi-path coordinate attention (MPCA) mechanism within the backbone network, capturing multi-scale features and significantly elevating tillers recognition. The innovative replacement of the CSPDarknet53 to 2-Stage FPN (C2F) module with receptive field attention convolution (RFCAConv) addresses parameter-sharing limitations, accentuating feature significance, and amplifying network performance. Coupled with the Focaler-CIoU loss for superior detection accuracy, YOLOv8-MRF outperforms RTDETR, YOLOv5, YOLOv7, and YOLOv8 by impressive margins in mAP50, while operating with merely 11 % of the parameters of YOLOv7, achieving a detection precision of 91.7 %, and with enhancements of 2.5 % in precision, 5.5 % in recall, and 4.1 % in mAP50 over the original model. The experimental results demonstrate that this method can realize tillering detection under complex backgrounds, contributing to advancing intelligent farming practices for wheat. Importantly, the YOLOv8-MRF model not only achieves significant technological advancements but also shows strong potential in practical applications, providing an effective tool for agricultural automation and intelligence, which could become pivotal in the development of future precision agriculture technologies. |
| format | Article |
| id | doaj-art-e2bc8da4f2224b50964006c0b99e059d |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-e2bc8da4f2224b50964006c0b99e059d2025-08-20T02:52:20ZengElsevierSmart Agricultural Technology2772-37552025-03-011010072010.1016/j.atech.2024.100720Research on detection of wheat tillers in natural environment based on YOLOv8-MRFMin Liang0Yuchen Zhang1Jian Zhou2Fengcheng Shi3Zhiqiang Wang4Yu Lin5Liang Zhang6Yaxi Liu7Triticeae Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, China; College of Information Engineering, Sichuan Agricultural University, Ya'an, ChinaTriticeae Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, ChinaChina Tobacco Sichuan Industrial Co., Ltd., Harmful Components and Tar Reduction in Cigarette Key Laboratory of Sichuan Province, Chengdu, Sichuan, ChinaChina Tobacco Sichuan Industrial Co., Ltd., Harmful Components and Tar Reduction in Cigarette Key Laboratory of Sichuan Province, Chengdu, Sichuan, ChinaCoarse Cereal Research Institute, Agronomy and Horticulture Institute, Chengdu Agricultural College, Chengdu, Sichuan, ChinaTriticeae Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China; Corresponding authors.Triticeae Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, China; Corresponding authors.To bolster agricultural efficiency and precision, this study introduces the YOLOv8-MRF model (multi-path coordinate attention, receptive field attention convolution, and Focaler-CIoU-optimized YOLOv8), a groundbreaking advancement in automated detection of wheat tillers. This model transcends traditional manual methods prone to subjectivity and inefficiency. This approach integrates an enhanced multi-path coordinate attention (MPCA) mechanism within the backbone network, capturing multi-scale features and significantly elevating tillers recognition. The innovative replacement of the CSPDarknet53 to 2-Stage FPN (C2F) module with receptive field attention convolution (RFCAConv) addresses parameter-sharing limitations, accentuating feature significance, and amplifying network performance. Coupled with the Focaler-CIoU loss for superior detection accuracy, YOLOv8-MRF outperforms RTDETR, YOLOv5, YOLOv7, and YOLOv8 by impressive margins in mAP50, while operating with merely 11 % of the parameters of YOLOv7, achieving a detection precision of 91.7 %, and with enhancements of 2.5 % in precision, 5.5 % in recall, and 4.1 % in mAP50 over the original model. The experimental results demonstrate that this method can realize tillering detection under complex backgrounds, contributing to advancing intelligent farming practices for wheat. Importantly, the YOLOv8-MRF model not only achieves significant technological advancements but also shows strong potential in practical applications, providing an effective tool for agricultural automation and intelligence, which could become pivotal in the development of future precision agriculture technologies.http://www.sciencedirect.com/science/article/pii/S2772375524003241Detection of wheat tillersYOLOv8MPCARFCAConvFocaler-CIoU |
| spellingShingle | Min Liang Yuchen Zhang Jian Zhou Fengcheng Shi Zhiqiang Wang Yu Lin Liang Zhang Yaxi Liu Research on detection of wheat tillers in natural environment based on YOLOv8-MRF Smart Agricultural Technology Detection of wheat tillers YOLOv8 MPCA RFCAConv Focaler-CIoU |
| title | Research on detection of wheat tillers in natural environment based on YOLOv8-MRF |
| title_full | Research on detection of wheat tillers in natural environment based on YOLOv8-MRF |
| title_fullStr | Research on detection of wheat tillers in natural environment based on YOLOv8-MRF |
| title_full_unstemmed | Research on detection of wheat tillers in natural environment based on YOLOv8-MRF |
| title_short | Research on detection of wheat tillers in natural environment based on YOLOv8-MRF |
| title_sort | research on detection of wheat tillers in natural environment based on yolov8 mrf |
| topic | Detection of wheat tillers YOLOv8 MPCA RFCAConv Focaler-CIoU |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003241 |
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