  This paper presents a novel metric to evaluate the robustness of deep
learning based semantic segmentation approaches for crop row detection under
different field conditions encountered by a field robot. A dataset with ten
main categories encountered under various field conditions was used for
testing. The effect on these conditions on the angular accuracy of crop row
detection was compared. A deep convolutional encoder decoder network is
implemented to predict crop row masks using RGB input images. The predicted
mask is then sent to a post processing algorithm to extract the crop rows. The
deep learning model was found to be robust against shadows and growth stages of
the crop while the performance was reduced under direct sunlight, increasing
weed density, tramlines and discontinuities in crop rows when evaluated with
the novel metric.
