# VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map

arXiv: https://arxiv.org/abs/2009.01708 · HTML: https://mlsanity.com/paper/2009.01708/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Fusion of Satellite Images and Weather Data with Transformer Networks for Downy Mildew Disease Detection](https://mlsanity.com/paper/2209.02797/) (2022)
- [Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation](https://mlsanity.com/paper/2503.22909/) (2025)

## Abstract (arXiv)

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.