Three-Dimensional Spatial Perception of Blueberry Fruits Based on Improved YOLOv11 Network
Year: 2025
Type: article
Abstract: Automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance of blueberry fruits, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper presents a method for blueberry detection, 3D spatial localization, and pose estimation using visual perception, which can be deployed on an OAK depth camera. Firstly, a blueberry and calyx scar detection dataset is constructed to train the detection network and evaluate its performance. Secondly, the blueberry and calyx scar detection model based on a lightweight yolov11 (the eleventh version of the You Only Look Once) network with an improved depth-wise separable convolution (DSC) module is designed, and a 3D coordinate system relative to the camera is established to calculate the 3D pose of the blueberry fruits. Finally, the above detection model is deployed onto the OAK depth camera, leveraging its depth estimation module and three-axis gyroscope to obtain the 3D coordinates of the blueberry fruits. Experimental results demonstrate that the method proposed in this paper can accurately perceive blueberry fruits at various maturity levels, achieving a detection accuracy of 95.8% mAP50-95, a positioning error of less than 1 centimeter within 0.5 meters, and an average 3D pose error of 19.2 degrees while maintaining the detection frame rate of 13.4 FPS (Frames Per Second) on the OAK depth camera, which can provide effective picking guidance for the mechanical arm of picking robot.
Source: Agronomy
Authors: Kun Zhao, Yuhuan Li, Zunmin Liu
Institution: Qingdao University of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,137
Award: G3487742445
OpenAlex ID: W4407903919