YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes
Citrus ranks among the most widely cultivated and economically vital fruit crops globally, with southern China being a major production area. In recent years, global warming has intensified extreme weather events, such as prolonged high temperature and strong solar radiation, posing increasing risks...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1591989/full |
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| _version_ | 1849320133148803072 |
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| author | Guoxun Cong Guoxun Cong Xinghong Chen Xinghong Chen Zongyu Bing Zongyu Bing Wenhuan Liu Wenhuan Liu Xiangling Chen Qun Wu Zheng Guo Zheng Guo Yongqiang Zheng Yongqiang Zheng |
| author_facet | Guoxun Cong Guoxun Cong Xinghong Chen Xinghong Chen Zongyu Bing Zongyu Bing Wenhuan Liu Wenhuan Liu Xiangling Chen Qun Wu Zheng Guo Zheng Guo Yongqiang Zheng Yongqiang Zheng |
| author_sort | Guoxun Cong |
| collection | DOAJ |
| description | Citrus ranks among the most widely cultivated and economically vital fruit crops globally, with southern China being a major production area. In recent years, global warming has intensified extreme weather events, such as prolonged high temperature and strong solar radiation, posing increasing risks to citrus production,leading to significant economic losses. Existing identification methods struggle with accuracy and generalization in complex environments, limiting their real-time application. This study presents an improved, lightweight citrus sunburn recognition model, YOLOv8-Scm, based on the YOLOv8n architecture. Three key enhancements are introduced: (1) DSConv module replaces the standard convolution for a more efficient and lightweight design, (2) Global Attention Mechanism (GAM) improves feature extraction for multi-scale and occluded targets, and (3) EIoU loss function enhances detection precision and generalization. The YOLOv8-Scm model achieves improvements of 2.0% in mAP50 and 1.5% in Precision over the original YOLOv8n, with only a slight increase in computational parameters (0.182M). The model’s Recall rate decreases minimally by 0.01%. Compared to other models like SSD, Faster R-CNN, YOLOv5n, YOLOv7-tiny, YOLOv8n, and YOLOv10n, YOLOv8-Scm outperforms in mAP50, Precision, and Recall, and is significantly more efficient in terms of computational parameters. Specifically, the model achieves a mAP50 of 92.7%, a Precision of 86.6%, and a Recall of 87.2%. These results validate the model’s superior capability in accurately detecting citrus sunburn across diverse and challenging natural scenarios. YOLOv8-Scm enables accurate, real-time citrus sunburn monitoring, providing strong technical support for smart orchard management and practical deployment. |
| format | Article |
| id | doaj-art-6a7c14c679764b4e8385145bb8435162 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-6a7c14c679764b4e8385145bb84351622025-08-20T03:50:12ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.15919891591989YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenesGuoxun Cong0Guoxun Cong1Xinghong Chen2Xinghong Chen3Zongyu Bing4Zongyu Bing5Wenhuan Liu6Wenhuan Liu7Xiangling Chen8Qun Wu9Zheng Guo10Zheng Guo11Yongqiang Zheng12Yongqiang Zheng13National Digital Planting (Citrus) Innovation Sub-Center, National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing, ChinaCitrus Research Institute, Southwest University, Chongqing, ChinaNational Digital Planting (Citrus) Innovation Sub-Center, National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing, ChinaCitrus Research Institute, Southwest University, Chongqing, ChinaNational Digital Planting (Citrus) Innovation Sub-Center, National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing, ChinaCitrus Research Institute, Southwest University, Chongqing, ChinaNational Digital Planting (Citrus) Innovation Sub-Center, National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing, ChinaCitrus Research Institute, Southwest University, Chongqing, ChinaHorticultural Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, Guangxi, ChinaQuzhou Academy of Agricultural and Forestry Sciences, Quzhou, Zhejiang, ChinaNational Digital Planting (Citrus) Innovation Sub-Center, National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing, ChinaCitrus Research Institute, Southwest University, Chongqing, ChinaNational Digital Planting (Citrus) Innovation Sub-Center, National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing, ChinaCitrus Research Institute, Southwest University, Chongqing, ChinaCitrus ranks among the most widely cultivated and economically vital fruit crops globally, with southern China being a major production area. In recent years, global warming has intensified extreme weather events, such as prolonged high temperature and strong solar radiation, posing increasing risks to citrus production,leading to significant economic losses. Existing identification methods struggle with accuracy and generalization in complex environments, limiting their real-time application. This study presents an improved, lightweight citrus sunburn recognition model, YOLOv8-Scm, based on the YOLOv8n architecture. Three key enhancements are introduced: (1) DSConv module replaces the standard convolution for a more efficient and lightweight design, (2) Global Attention Mechanism (GAM) improves feature extraction for multi-scale and occluded targets, and (3) EIoU loss function enhances detection precision and generalization. The YOLOv8-Scm model achieves improvements of 2.0% in mAP50 and 1.5% in Precision over the original YOLOv8n, with only a slight increase in computational parameters (0.182M). The model’s Recall rate decreases minimally by 0.01%. Compared to other models like SSD, Faster R-CNN, YOLOv5n, YOLOv7-tiny, YOLOv8n, and YOLOv10n, YOLOv8-Scm outperforms in mAP50, Precision, and Recall, and is significantly more efficient in terms of computational parameters. Specifically, the model achieves a mAP50 of 92.7%, a Precision of 86.6%, and a Recall of 87.2%. These results validate the model’s superior capability in accurately detecting citrus sunburn across diverse and challenging natural scenarios. YOLOv8-Scm enables accurate, real-time citrus sunburn monitoring, providing strong technical support for smart orchard management and practical deployment.https://www.frontiersin.org/articles/10.3389/fpls.2025.1591989/fullYOLO v8nYOLOv8-Scmcitrus sunburnsmart orchard monitoringobject detection |
| spellingShingle | Guoxun Cong Guoxun Cong Xinghong Chen Xinghong Chen Zongyu Bing Zongyu Bing Wenhuan Liu Wenhuan Liu Xiangling Chen Qun Wu Zheng Guo Zheng Guo Yongqiang Zheng Yongqiang Zheng YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes Frontiers in Plant Science YOLO v8n YOLOv8-Scm citrus sunburn smart orchard monitoring object detection |
| title | YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes |
| title_full | YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes |
| title_fullStr | YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes |
| title_full_unstemmed | YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes |
| title_short | YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes |
| title_sort | yolov8 scm an improved model for citrus fruit sunburn identification and classification in complex natural scenes |
| topic | YOLO v8n YOLOv8-Scm citrus sunburn smart orchard monitoring object detection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1591989/full |
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