Robot-supervised Learning of Crop Row Segmentation

• Published in: 2021 IEEE International Conference on Robotics and Automation (ICRA) pp. 2185 - 2191
• Main Authors: Bakken, Marianne, Ponnambalam, Vignesh Raja, Moore, Richard J. D., Omholt Gjevestad, Jon Glenn, Johan From, Pal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30-05-2021
• Subjects: Crops; Global navigation satellite system; Image segmentation; Neural networks; Noise measurement; Semantics; Training
• ISSN: 2577-087X
• DOI: 10.1109/ICRA48506.2021.9560815

We propose an approach for robot-supervised learning that automates label generation for semantic segmentation with Convolutional Neural Networks (CNNs) for crop row detection in a field. Using a training robot equipped with RTK GNSS and RGB camera, we train a neural network that can later be used for pure vision-based navigation. We test our approach on an agri-robot in a strawberry field and successfully train crop row segmentation without any hand-drawn image labels. Our main finding is that the resulting segmentation output of the CNN shows better performance than the noisy labels it was trained on. Finally, we conduct open-loop field trials with our agri-robot and show that row-following based on the segmentation result is accurate enough for closed-loop guidance. We conclude that training with noisy segmentation labels is a promising approach for learning vision-based crop row following.