Robot-supervised Learning of Crop Row Segmentation

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.

Automated summary

This study introduces a robot-supervised learning approach that automates label generation for semantic segmentation in crop row detection, showing promising potential for future development in field testing.