VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning
Apple-picking robots are increasingly applied in smart agriculture, but their performance is limited by complex orchard conditions such as unstable lighting, occlusion, and weather variations. This study proposes an optimized lightweight detection model, VBP-YOLO-prune, based on YOLOv8n, to enhance...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825008828 |
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| author | Haohai You Hao Wang Zhanchen Wei Chunguang Bi Lijuan Zhang Xuefang Li Yingying Yin |
| author_facet | Haohai You Hao Wang Zhanchen Wei Chunguang Bi Lijuan Zhang Xuefang Li Yingying Yin |
| author_sort | Haohai You |
| collection | DOAJ |
| description | Apple-picking robots are increasingly applied in smart agriculture, but their performance is limited by complex orchard conditions such as unstable lighting, occlusion, and weather variations. This study proposes an optimized lightweight detection model, VBP-YOLO-prune, based on YOLOv8n, to enhance detection accuracy and deployment efficiency on edge devices. The model incorporates a V7 downsampling module, BiFPN feature fusion, and an improved PIOUv2 loss function, aiming to improve multi-scale representation and bounding box regression. A custom apple dataset was augmented with diverse lighting and weather conditions to improve generalization. Experimental results using 10-fold cross-validation show that VBP-YOLO-prune achieves 89.0 % mAP50 and 66.26 % mAP50–95, with Precision of 84.01 % and Recall of 80.52 %. Additionally, it reduces parameters by 79.7 %, FLOPs by 60.9 %, and increases FPS by 29.2 % compared to YOLOv8n.The final model contains only 0.61 M parameters, 3.2 GFLOPs, and runs at 102.6 FPS on NVIDIA Jetson Orin Nano. These results demonstrate that VBP-YOLO-prune provides a practical and efficient solution for real-time fruit detection in complex environments. Future research may extend this approach to other crop types and explore full integration into autonomous harvesting systems. |
| format | Article |
| id | doaj-art-1cb60bed39c84d33a74fa5c267521bfe |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-1cb60bed39c84d33a74fa5c267521bfe2025-08-20T04:01:57ZengElsevierAlexandria Engineering Journal1110-01682025-09-01128992101410.1016/j.aej.2025.08.013VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruningHaohai You0Hao Wang1Zhanchen Wei2Chunguang Bi3Lijuan Zhang4Xuefang Li5Yingying Yin6College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaFaculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, China; College of Internet of Things Engineering, Wuxi University, Wuxi 214105, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, China; Corresponding author.Apple-picking robots are increasingly applied in smart agriculture, but their performance is limited by complex orchard conditions such as unstable lighting, occlusion, and weather variations. This study proposes an optimized lightweight detection model, VBP-YOLO-prune, based on YOLOv8n, to enhance detection accuracy and deployment efficiency on edge devices. The model incorporates a V7 downsampling module, BiFPN feature fusion, and an improved PIOUv2 loss function, aiming to improve multi-scale representation and bounding box regression. A custom apple dataset was augmented with diverse lighting and weather conditions to improve generalization. Experimental results using 10-fold cross-validation show that VBP-YOLO-prune achieves 89.0 % mAP50 and 66.26 % mAP50–95, with Precision of 84.01 % and Recall of 80.52 %. Additionally, it reduces parameters by 79.7 %, FLOPs by 60.9 %, and increases FPS by 29.2 % compared to YOLOv8n.The final model contains only 0.61 M parameters, 3.2 GFLOPs, and runs at 102.6 FPS on NVIDIA Jetson Orin Nano. These results demonstrate that VBP-YOLO-prune provides a practical and efficient solution for real-time fruit detection in complex environments. Future research may extend this approach to other crop types and explore full integration into autonomous harvesting systems.http://www.sciencedirect.com/science/article/pii/S1110016825008828apple detectionYOLOv8deep learninglightweight deployment |
| spellingShingle | Haohai You Hao Wang Zhanchen Wei Chunguang Bi Lijuan Zhang Xuefang Li Yingying Yin VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning Alexandria Engineering Journal apple detection YOLOv8 deep learning lightweight deployment |
| title | VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning |
| title_full | VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning |
| title_fullStr | VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning |
| title_full_unstemmed | VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning |
| title_short | VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning |
| title_sort | vbp yolo prune robust apple detection under variable weather via feature adaptive fusion and efficient yolo pruning |
| topic | apple detection YOLOv8 deep learning lightweight deployment |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825008828 |
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