A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8
Current object detection algorithms lack accuracy in detecting citrus maturity color, and feature extraction needs improvement. In automated harvesting, accurate maturity detection reduces waste caused by incorrect evaluations. To address this issue, this study proposes an improved YOLOv8-based meth...
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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.2025.1472230/full |
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| author | Fuqin Deng Zhenghong He Lanhui Fu Jianle Chen Nannan Li Weibiao Chen Jialong Luo Weilai Qiao Jianfeng Hou Yongkang Lu |
| author_facet | Fuqin Deng Zhenghong He Lanhui Fu Jianle Chen Nannan Li Weibiao Chen Jialong Luo Weilai Qiao Jianfeng Hou Yongkang Lu |
| author_sort | Fuqin Deng |
| collection | DOAJ |
| description | Current object detection algorithms lack accuracy in detecting citrus maturity color, and feature extraction needs improvement. In automated harvesting, accurate maturity detection reduces waste caused by incorrect evaluations. To address this issue, this study proposes an improved YOLOv8-based method for detecting Xinhui citrus maturity. GhostConv was introduced to replace the ordinary convolution in the Head of YOLOv8, reducing the number of parameters in the model and enhancing detection accuracy. The CARAFE (Content-Aware Reassembly of Features) upsampling operator was used to replace the conventional upsampling operation, retaining more details through feature reorganization and expansion. Additionally, the MCA (Multidimensional Collaborative Attention) mechanism was introduced to focus on capturing the local feature interactions between feature mapping channels, enabling the model to more accurately extract detailed features, thus further improving the accuracy of citrus color identification. Experimental results show that the precision, recall, and average precision of the improved YOLOv8 on the test set are 88.6%, 93.1%, and 93.4%, respectively. Compared to the original model, the improved YOLOv8 achieved increases of 16.5%, 20.2%, and 14.7%, respectively, and the parameter volume was reduced by 0.57%. This paper aims to improve the model for detecting Xinhui citrus maturity in complex orchards, supporting automated fruit-picking systems. |
| format | Article |
| id | doaj-art-d89ad18249894069960bfbfad701f5a7 |
| 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-d89ad18249894069960bfbfad701f5a72025-01-29T06:46:14ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011610.3389/fpls.2025.14722301472230A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8Fuqin Deng0Zhenghong He1Lanhui Fu2Jianle Chen3Nannan Li4Weibiao Chen5Jialong Luo6Weilai Qiao7Jianfeng Hou8Yongkang Lu9School of Electronic and Information Engineering, the Wuyi University, Jiangmen, ChinaSchool of Electronic and Information Engineering, the Wuyi University, Jiangmen, ChinaSchool of Electronic and Information Engineering, the Wuyi University, Jiangmen, ChinaSchool of Electronic and Information Engineering, the Wuyi University, Jiangmen, ChinaSchool of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao, Macao SAR, ChinaSchool of Electronic and Information Engineering, the Wuyi University, Jiangmen, ChinaSchool of Mechanical and Automation Engineering, The Wuyi University, Jiangmen, ChinaSchool of Electronic and Information Engineering, the Wuyi University, Jiangmen, ChinaSchool of Electronic and Information Engineering, the Wuyi University, Jiangmen, ChinaCollege of Advanced Engineering, Great Bay University, Dongguan, ChinaCurrent object detection algorithms lack accuracy in detecting citrus maturity color, and feature extraction needs improvement. In automated harvesting, accurate maturity detection reduces waste caused by incorrect evaluations. To address this issue, this study proposes an improved YOLOv8-based method for detecting Xinhui citrus maturity. GhostConv was introduced to replace the ordinary convolution in the Head of YOLOv8, reducing the number of parameters in the model and enhancing detection accuracy. The CARAFE (Content-Aware Reassembly of Features) upsampling operator was used to replace the conventional upsampling operation, retaining more details through feature reorganization and expansion. Additionally, the MCA (Multidimensional Collaborative Attention) mechanism was introduced to focus on capturing the local feature interactions between feature mapping channels, enabling the model to more accurately extract detailed features, thus further improving the accuracy of citrus color identification. Experimental results show that the precision, recall, and average precision of the improved YOLOv8 on the test set are 88.6%, 93.1%, and 93.4%, respectively. Compared to the original model, the improved YOLOv8 achieved increases of 16.5%, 20.2%, and 14.7%, respectively, and the parameter volume was reduced by 0.57%. This paper aims to improve the model for detecting Xinhui citrus maturity in complex orchards, supporting automated fruit-picking systems.https://www.frontiersin.org/articles/10.3389/fpls.2025.1472230/fullobject detectionmaturity detectionXinHui citrusYOLOv8CARAFE lightweight operatormulti-dimensional collaborative attention mechanism (MCA) |
| spellingShingle | Fuqin Deng Zhenghong He Lanhui Fu Jianle Chen Nannan Li Weibiao Chen Jialong Luo Weilai Qiao Jianfeng Hou Yongkang Lu A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8 Frontiers in Plant Science object detection maturity detection XinHui citrus YOLOv8 CARAFE lightweight operator multi-dimensional collaborative attention mechanism (MCA) |
| title | A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8 |
| title_full | A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8 |
| title_fullStr | A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8 |
| title_full_unstemmed | A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8 |
| title_short | A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8 |
| title_sort | new maturity recognition algorithm for xinhui citrus based on improved yolov8 |
| topic | object detection maturity detection XinHui citrus YOLOv8 CARAFE lightweight operator multi-dimensional collaborative attention mechanism (MCA) |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1472230/full |
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