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Peach tree detection for weeding robot based on Faster-RCNN

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2020 ASABE Annual International Virtual Meeting  2000708.(doi:10.13031/aim.202000708)
Authors:   Jun Luo, Yong You, Decheng Wang, Xianshun Sun, Jie Lv, Wenpeng Ma, Xuening Zhang
Keywords:   deep learning, Faster-RCNN, peach tree, Machine vision

Abstract: Deep learning and Machine Vision has demonstrated excellent capabilities for learning image features and is widely used for image target detection. In order to improve the performance of machine vision in peach tree detection for weeding robot, Faster Region Convolutional Neural Network (Faster-RCNN) was introduced. Resnet100 was adopted as backbone network, combined with the Feature Pyramid Network (FPN) architecture for feature extraction. The Region Proposal Network (RPN) was trained and region proposals for each feature map were created. Peach tree detection results of 1000 test images showed that the average detection precision rate was 86.41%, the recall rate was 92.15% and the mean intersection over union (MIoU) rate for instance segmentation was 85.45%. The performance of the Weeding robot tests show that the rate of missed cuts is greatly reduced, compared to traditional weeding methods.

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