For automating the harvesting of bunches of tomatoes in a greenhouse, the end-effector needs to reach the exact cutting point and adaptively adjust the pose of peduncles. In this paper by Rong, Dai, and Wang, a method is proposed for peduncle cutting point localization and pose estimation. The images are captured in real-time at a fixed long-distance are detected using the YOLOv4.
Tiny detectors with a precision of 92.7% and a detection speed of 0.0091 s/frame, then the YOLACT + + Network with mAP of 73.1 and a time speed of 0.109 s/frame is used to segment the close-up distance.
The segmented peduncle mask is fitted to the curve using least squares and three key points on the curve are found. Finally, a geometric model is established to estimate the pose of the peduncle with an average error of 4.98° in yaw angle and 4.75° in pitch angle over the 30 sets of tests.
Read the complete research at www.researchgate.net.
Rong, Jiacheng & Dai, Guanglin & Wang, Pengbo. (2021). A peduncle detection method of tomato for autonomous harvesting. Complex & Intelligent Systems. 7. 10.1007/s40747-021-00522-7.
