Work

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

Year: 2025

Type: article

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.

Source: Agriculture

DOI: 10.3390/agriculture15181954

Authors: Hongxiong Su, Sa Wang, Honglin Su, Fumin Ma, Yanwen Li, Juxia Li

Institutions: Shanxi Agricultural University, Lanzhou University of Technology

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137