Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming. (arXiv:2210.11545v1 [cs.CV])

Weed and crop segmentation is becoming an increasingly integral part of
precision farming that leverages the current computer vision and deep learning
technologies. Research has been extensively carried out based on images
captured with a camera from various platforms. Unmanned aerial vehicles (UAVs)
and ground-based vehicles including agricultural robots are the two popular
platforms for data collection in fields. They all contribute to site-specific
weed management (SSWM) to maintain crop yield. Currently, the data from these
two platforms is processed …

arxiv farming patterns precision segmentation semantic