YOLOv8n-Pose-DSW: A Precision Picking Point Localization Model for Zucchini in Complex Greenhouse Environments

Hongxiong Su, Sa Wang, Honglin Su, Fumin Ma, Yanwen Li and Juxia Li ()
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Hongxiong Su: College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
Sa Wang: College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
Honglin Su: College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
Fumin Ma: College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Yanwen Li: College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
Juxia Li: College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China

Agriculture, 2025, vol. 15, issue 18, 1-23

Abstract: Zucchini growth in greenhouse environments presents significant challenges for fruit recognition and picking point localization due to characteristics such as foliage occlusion, high density, structural complexity, and diverse fruit morphologies. Current recognition and localization algorithms exhibit limitations including low accuracy, restricted applicability, and procedural complexity, falling short of the requirements for precise and robust intelligent harvesting. To address these issues, this study constructs a zucchini dataset of 942 images using an Intel RealSense D455 depth camera and a smartphone, and proposes a novel keypoint detection model named YOLOv8n-Pose-DSW. The model introduces three key enhancements compared with YOLOv8n-Pose. First, the conventional upsample operator is replaced with an adaptive point sampling operator called Dysample, improving detection accuracy while reducing GPU memory consumption. Second, a Slim-Neck structure is designed to decrease computational overhead through lightweight bottleneck architecture, while preserving robust feature representation. Third, the WIoU-v3 loss is adopted to optimize bounding box regression for object detection, thereby enhancing localization accuracy. Experimental results demonstrate that YOLOv8n-Pose-DSW achieves a zucchini detection P, R, mAP@50, and mAP@50–95 of 92.1%, 90.7%, 94.0%, and 71.4%, respectively. These metrics represent improvements of 3.3%, 11.7%, 7.4%, and 15.4%, respectively, over the original model. For picking point localization, the improved model attains a P of 93.1%, R of 89.5%, mAP@50 of 95.6%, and mAP@50–95 of 95.2%, corresponding to gains of 8.8%, 11.0%, 11.3%, and 27.9% over the original model. Further error analysis shows that picking point localization errors are concentrated within the 0–4-pixel range, demonstrating enhanced localization precision critical for practical harvesting applications. The proposed algorithm effectively addresses greenhouse environmental challenges and provides essential technical support for intelligent zucchini harvesting systems.

Keywords: deep learning; YOLOv8n-Pose; lightweight model; zucchini; picking point localization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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