Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VddNet: Vine Disease Detection Network Based on Multispectral Images and
  Depth Map   Early detection of vine disease is important to avoid spread of virus or
fungi. Disease propagation can lead to a huge loss of grape production and
disastrous economic consequences, therefore the problem represents a challenge
for the precision farming. In this paper, we present a new system for vine
disease detection. The article contains two contributions: the first one is an
automatic orthophotos registration method from multispectral images acquired
with an unmanned aerial vehicle (UAV). The second one is a new deep learning
architecture called VddNet (Vine Disease Detection Network). The proposed
architecture is assessed by comparing it with the most known architectures:
SegNet, U-Net, DeepLabv3+ and PSPNet. The deep learning architectures were
trained on multispectral data and depth map information. The results of the
proposed architecture show that the VddNet architecture achieves higher scores
than the base line methods. Moreover, this study demonstrates that the proposed
system has many advantages compared to methods that directly use the UAV
images.

17
The categories of this paper are: q-fin
1
The categories of this paper are: cs
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int