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

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...

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Autores principales: Kerkech, Mohamed, Hafiane, Adel, Canals, Raphael, Ros, Frederic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340882/
http://dx.doi.org/10.1007/978-3-030-51935-3_9
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author Kerkech, Mohamed
Hafiane, Adel
Canals, Raphael
Ros, Frederic
author_facet Kerkech, Mohamed
Hafiane, Adel
Canals, Raphael
Ros, Frederic
author_sort Kerkech, Mohamed
collection PubMed
description 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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spelling pubmed-73408822020-07-08 Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information Kerkech, Mohamed Hafiane, Adel Canals, Raphael Ros, Frederic Image and Signal Processing Article 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. 2020-06-05 /pmc/articles/PMC7340882/ http://dx.doi.org/10.1007/978-3-030-51935-3_9 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Kerkech, Mohamed
Hafiane, Adel
Canals, Raphael
Ros, Frederic
Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
title Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
title_full Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
title_fullStr Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
title_full_unstemmed Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
title_short Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
title_sort vine disease detection by deep learning method combined with 3d depth information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340882/
http://dx.doi.org/10.1007/978-3-030-51935-3_9
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AT hafianeadel vinediseasedetectionbydeeplearningmethodcombinedwith3ddepthinformation
AT canalsraphael vinediseasedetectionbydeeplearningmethodcombinedwith3ddepthinformation
AT rosfrederic vinediseasedetectionbydeeplearningmethodcombinedwith3ddepthinformation

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