Estimation of asparagus stem height and diameter in complex environments by integrating improved YOLOv5 with point cloud - Hong, Weiwei; Ma, Zenghong; Ye, Bingliang; Yu, Gaohong; Tang, Tao; Zheng, Mingfeng (International Journal of Agricultural and Biological Engineering, 2025)
Identifying the maturity of asparagus is a crucial step for machine-assisted harvesting of asparagus in complex environments. This study proposes an innovative method to evaluate the height and diameter of asparagus stems, combining an enhanced YOLOv5 detection algorithm with point cloud data. In this method, first, the YOLOv5 model was improved, enabling efficient recognition and detection of asparagus in complex environments. Subsequently, a RealSense L515 radar camera was deployed to capture both the original RGB images and the point cloud information. The improved YOLOv5 algorithm was then employed to detect asparagus instances within the RGB images, with the pixel positions of the detection frames mapped onto the point cloud dataset to extract comprehensive 3D point cloud details of the asparagus. Finally, noise was reduced through statistical filtering and Euclidean clustering, and asparagus height was determined using the oriented bounding box methodology. Slices, each with a thickness of 10 mm, were extracted at designated measurement points, and the asparagus diameter was calculated by assessing the disparity between the maximum and minimum coordinates perpendicular to the growth direction of the asparagus. Experimental results showed that the mean average precision, precision, and recall of the improved YOLOv5 model increased by 4.85%, 5.09%, and 3.4%, reaching 98.21%, 97.11%, and 95.33%, respectively, which are higher than those of the YOLOv5 prototype network. Therefore, the proposed method could effectively detect asparagus. The algorithm exhibited a mean absolute error of 1.08 cm, a mean absolute percentage error of 4.06%, and a root mean square error of 1.60 cm in its estimation of asparagus height. For asparagus diameter estimation, the algorithm achieved a mean absolute error of 0.86 mm, a mean absolute percentage error of 7.98%, and a root mean square error of 1.23 mm. These results confirm that the proposed method can estimate the height and diameter of asparagus stems accurately, thereby providing invaluable technical support for machine harvesting of asparagus. Keywords: asparagus, height, diameter, improved YOLOv5, point cloud DOI: 10.25165/j.ijabe.20251805.9262 Citation: Hong W W, Ma Z H, Ye B L, Yu G H, Tang T, Zheng M F. Estimation of asparagus stem height and diameter in complex environments by integrating improved YOLOv5 with point cloud. Int J Agric & Biol Eng, 2025; 18(5): 268–277.