Stereo vision based broccoli recognition and attitude estimation method for field harvesting

IF 12.4 Q1 AGRICULTURE, MULTIDISCIPLINARY

Artificial Intelligence in Agriculture Pub Date : 2025-02-13 DOI:10.1016/j.aiia.2025.02.002

Zhenni He , Fahui Yuan , Yansuo Zhou , Bingbo Cui , Yong He , Yufei Liu

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Abstract

At present, automatic broccoli harvest in field still faces some issues. It is difficult to segment broccoli in real time under complex field background, and hard to pick tilt-growing broccoli for the end-effector of robot. In this research, an improved YOLOv8n-seg model, named YOLO-Broccoli-Seg was proposed for broccoli recognition. Through adding a triplet attention module to YOLOv8-Seg model, the feature fusion capability of the algorithm is improved significantly. The mean average precision mAP50 (Mask), mAP95 (Mask), mAP50 (Bounding Box, Bbox) and mAP95 (Bbox) of YOLO-Broccoli-Seg are 0.973, 0.683, 0.973 and 0.748 respectively. Precision P-value was improved the most, with an increment of 8.7 %. In addition, an attitude estimation method based on three-dimensional point cloud is proposed. When the tilt angle of broccoli is between −30°and 30°, the R² between the estimated value and the true value is 0.934. It indicated that this method can well represent the growth attitude of broccoli. This research can provide the rich broccoli information and technical basis for the automated broccoli picking.

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基于立体视觉的西兰花田间收获识别与姿态估计方法

目前，西兰花田间自动收获还面临着一些问题。在复杂的田间背景下，对西兰花进行实时分割是困难的，对机器人末端执行器来说，对倾斜生长的西兰花进行选择也是困难的。本研究提出了一种改进的YOLOv8n-seg模型，命名为YOLOv8n-seg。通过在YOLOv8-Seg模型中加入三重关注模块，显著提高了算法的特征融合能力。YOLO-Broccoli-Seg的平均精度mAP50 （Mask）、mAP95 （Mask）、mAP50 （Bounding Box, Bbox）和mAP95 （Bbox）分别为0.973、0.683、0.973和0.748。精度p值提高幅度最大，达到8.7%。此外，提出了一种基于三维点云的姿态估计方法。西兰花倾斜角度在−30°~ 30°之间时，估计值与真实值的R2为0.934。结果表明，该方法能较好地反映西兰花的生长态度。本研究可为花椰菜自动化采摘提供丰富的花椰菜信息和技术依据。

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