Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration
The precise determination of tobacco leaf maturity is pivotal for safeguarding the taste and quality of tobacco products, augmenting the financial gains of tobacco growers, and propelling the industry’s sustainable progression. This research addresses the inherent subjectivity and variability in con...
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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.2024.1474207/full |
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| author | Yi Shi Hong Wang Fei Wang Yingkuan Wang Jianjun Liu Long Zhao Hui Wang Feng Zhang Qiongmin Cheng Shunhao Qing |
| author_facet | Yi Shi Hong Wang Fei Wang Yingkuan Wang Jianjun Liu Long Zhao Hui Wang Feng Zhang Qiongmin Cheng Shunhao Qing |
| author_sort | Yi Shi |
| collection | DOAJ |
| description | The precise determination of tobacco leaf maturity is pivotal for safeguarding the taste and quality of tobacco products, augmenting the financial gains of tobacco growers, and propelling the industry’s sustainable progression. This research addresses the inherent subjectivity and variability in conventional maturity evaluation techniques reliant on human expertise by introducing an innovative YOLOv10-based method for tobacco leaf maturity detection. This technique facilitates a rapid and non-invasive assessment of leaf maturity, significantly elevating the accuracy and efficiency of tobacco leaf quality evaluation. In our study, we have advanced the YOLOv10 framework by integrating DCNv3 with C2f to construct an enhanced neck network, designated as C2f-DCNv3. This integration is designed to augment the model’s capability for feature integration, particularly concerning the morphological and edge characteristics of tobacco leaves. Furthermore, the incorporation of the Efficient Local Attention (ELA) mechanism at multiple stages of the model has substantially enhanced the efficiency and fidelity of feature extraction. The empirical results underscore the model’s pronounced enhancement in performance across all maturity classifications. Notably, the overall precision (P) has been elevated from 0.939 to 0.973, the recall rate (R) has improved from 0.968 to 0.984, the mean average precision at 50% intersection over union (mAP50) has advanced from 0.984 to 0.994, and the mean average precision across the 50% to 95% intersection over union range (mAP50-95) has risen from 0.962 to 0.973. This research presents the tobacco industry with a novel rapid detection instrument for tobacco leaf maturity, endowed with substantial practical utility and broad prospects for application. Future research endeavors will be directed towards further optimization of the model’s architecture to bolster its generalizability and to explore its implementation within the realm of actual tobacco cultivation and processing. |
| format | Article |
| id | doaj-art-56752beebc0f4b87b51c4cf2ef740a04 |
| 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-56752beebc0f4b87b51c4cf2ef740a042025-01-03T06:47:19ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.14742071474207Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integrationYi Shi0Hong Wang1Fei Wang2Yingkuan Wang3Jianjun Liu4Long Zhao5Hui Wang6Feng Zhang7Qiongmin Cheng8Shunhao Qing9College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, ChinaHenan Province Tobacco Company, Luoyang Company, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, ChinaAcademy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing, ChinaHenan Province Tobacco Company, Zhengzhou, ChinaCollege of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, ChinaHenan Province Tobacco Company, Luoyang Company, Luoyang, ChinaHenan Province Tobacco Company, Luoyang Company, Luoyang, ChinaHenan Province Tobacco Company, Luoyang Company, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, ChinaThe precise determination of tobacco leaf maturity is pivotal for safeguarding the taste and quality of tobacco products, augmenting the financial gains of tobacco growers, and propelling the industry’s sustainable progression. This research addresses the inherent subjectivity and variability in conventional maturity evaluation techniques reliant on human expertise by introducing an innovative YOLOv10-based method for tobacco leaf maturity detection. This technique facilitates a rapid and non-invasive assessment of leaf maturity, significantly elevating the accuracy and efficiency of tobacco leaf quality evaluation. In our study, we have advanced the YOLOv10 framework by integrating DCNv3 with C2f to construct an enhanced neck network, designated as C2f-DCNv3. This integration is designed to augment the model’s capability for feature integration, particularly concerning the morphological and edge characteristics of tobacco leaves. Furthermore, the incorporation of the Efficient Local Attention (ELA) mechanism at multiple stages of the model has substantially enhanced the efficiency and fidelity of feature extraction. The empirical results underscore the model’s pronounced enhancement in performance across all maturity classifications. Notably, the overall precision (P) has been elevated from 0.939 to 0.973, the recall rate (R) has improved from 0.968 to 0.984, the mean average precision at 50% intersection over union (mAP50) has advanced from 0.984 to 0.994, and the mean average precision across the 50% to 95% intersection over union range (mAP50-95) has risen from 0.962 to 0.973. This research presents the tobacco industry with a novel rapid detection instrument for tobacco leaf maturity, endowed with substantial practical utility and broad prospects for application. Future research endeavors will be directed towards further optimization of the model’s architecture to bolster its generalizability and to explore its implementation within the realm of actual tobacco cultivation and processing.https://www.frontiersin.org/articles/10.3389/fpls.2024.1474207/fulltobacco leaf maturityYOLOv10DCNv3efficient local attentiontargeted detection |
| spellingShingle | Yi Shi Hong Wang Fei Wang Yingkuan Wang Jianjun Liu Long Zhao Hui Wang Feng Zhang Qiongmin Cheng Shunhao Qing Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration Frontiers in Plant Science tobacco leaf maturity YOLOv10 DCNv3 efficient local attention targeted detection |
| title | Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration |
| title_full | Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration |
| title_fullStr | Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration |
| title_full_unstemmed | Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration |
| title_short | Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration |
| title_sort | efficient and accurate tobacco leaf maturity detection an improved yolov10 model with dcnv3 and efficient local attention integration |
| topic | tobacco leaf maturity YOLOv10 DCNv3 efficient local attention targeted detection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1474207/full |
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