YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions
Accurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model buil...
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MDPI AG
2025-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1581 |
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| author | Yi Liu Xiang Han Hongjian Zhang Shuangxi Liu Wei Ma Yinfa Yan Linlin Sun Linlong Jing Yongxian Wang Jinxing Wang |
| author_facet | Yi Liu Xiang Han Hongjian Zhang Shuangxi Liu Wei Ma Yinfa Yan Linlin Sun Linlong Jing Yongxian Wang Jinxing Wang |
| author_sort | Yi Liu |
| collection | DOAJ |
| description | Accurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model built on an enhanced YOLOv8 architecture. We replace the backbone with MobileNetV4, incorporating unified inverted bottleneck (UIB) modules and depth-wise separable convolutions for efficient feature extraction. We introduce a spatial pyramid pooling fast cross-stage partial connections (SPPFCSPC) module for multi-scale feature fusion and a modified proportional distance IoU (MPD-IoU) loss to optimize bounding-box regression. Finally, layer-adaptive magnitude pruning (LAMP) combined with knowledge distillation compresses the model while retaining performance. On our custom Jinxiu Malus dataset, YOLOv8-MSP-PD achieves a mean average precision (mAP) of 92.2% (1.6% gain over baseline), reduces floating-point operations (FLOPs) by 59.9%, and shrinks to 2.2 MB. Five-fold cross-validation confirms stability, and comparisons with Faster R-CNN and SSD demonstrate superior accuracy and efficiency. This work offers a practical vision solution for agricultural robots and guidance for lightweight detection in precision agriculture. |
| format | Article |
| id | doaj-art-b53d9f99ec4e48cb8ca63b7a02fde652 |
| institution | Matheson Library |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-b53d9f99ec4e48cb8ca63b7a02fde6522025-07-25T13:09:50ZengMDPI AGAgronomy2073-43952025-06-01157158110.3390/agronomy15071581YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field ConditionsYi Liu0Xiang Han1Hongjian Zhang2Shuangxi Liu3Wei Ma4Yinfa Yan5Linlin Sun6Linlong Jing7Yongxian Wang8Jinxing Wang9College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaAccurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model built on an enhanced YOLOv8 architecture. We replace the backbone with MobileNetV4, incorporating unified inverted bottleneck (UIB) modules and depth-wise separable convolutions for efficient feature extraction. We introduce a spatial pyramid pooling fast cross-stage partial connections (SPPFCSPC) module for multi-scale feature fusion and a modified proportional distance IoU (MPD-IoU) loss to optimize bounding-box regression. Finally, layer-adaptive magnitude pruning (LAMP) combined with knowledge distillation compresses the model while retaining performance. On our custom Jinxiu Malus dataset, YOLOv8-MSP-PD achieves a mean average precision (mAP) of 92.2% (1.6% gain over baseline), reduces floating-point operations (FLOPs) by 59.9%, and shrinks to 2.2 MB. Five-fold cross-validation confirms stability, and comparisons with Faster R-CNN and SSD demonstrate superior accuracy and efficiency. This work offers a practical vision solution for agricultural robots and guidance for lightweight detection in precision agriculture.https://www.mdpi.com/2073-4395/15/7/1581Jinxiu Malus fruitYOLOv8lightweightmulti-scale feature fusionobject detection |
| spellingShingle | Yi Liu Xiang Han Hongjian Zhang Shuangxi Liu Wei Ma Yinfa Yan Linlin Sun Linlong Jing Yongxian Wang Jinxing Wang YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions Agronomy Jinxiu Malus fruit YOLOv8 lightweight multi-scale feature fusion object detection |
| title | YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions |
| title_full | YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions |
| title_fullStr | YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions |
| title_full_unstemmed | YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions |
| title_short | YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions |
| title_sort | yolov8 msp pd a lightweight yolov8 based detection method for jinxiu malus fruit in field conditions |
| topic | Jinxiu Malus fruit YOLOv8 lightweight multi-scale feature fusion object detection |
| url | https://www.mdpi.com/2073-4395/15/7/1581 |
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