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2020 | OriginalPaper | Chapter

Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information

Authors : Mohamed Kerkech, Adel Hafiane, Raphael Canals, Frederic Ros

Published in: Image and Signal Processing

Publisher: Springer International Publishing

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Abstract

Vine disease detection (VDD) is an important asset to predict a probable contagion of virus or fungi. Diseases that spreads through the vineyard has a huge economic impact, therefore it is considered as a challenge for viticulture. Automatic detection and mapping of vine disease in earlier stage can help to limit its impact and reduces the use of chemicals. This study deals with the problem of locating symptomatic areas in images from an unmanned aerial vehicle (UAV) using the visible and infrared domains. This paper, proposes a new method, based on segmentation by a convolutional neuron network SegNet and a depth map (DM), to delineate the asymptomatic regions in the vine canopy. The results obtained showed that SegNet combined with the depth information give better accuracy than a SegNet segmentation alone.

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Metadata
Title
Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
Authors
Mohamed Kerkech
Adel Hafiane
Raphael Canals
Frederic Ros
Copyright Year
2020
Book
Image and Signal Processing

Print ISBN: 978-3-030-51934-6

Electronic ISBN: 978-3-030-51935-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-51935-3_9

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