Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards agricultural autonomy: crop row detection under varying field
  conditions using deep learning   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.

36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci