  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.
