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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
Creator	Canals, Raphael Hafiane, Adel Ros, Frederic Kerkech, Mohamed
source	PMC
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.
has issue date	2020-06-05(xsd:dateTime)
bibo:doi	10.1007/978-3-030-51935-3_9
has license	no-cc
sha1sum (hex)	30d310642ffd1405a613965fc2ce9e2dcca9aaca
schema:url	https://doi.org/10.1007/978-3-030-51935-3_9
resource representing a document's title	Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information
has PubMed Central identifier	PMC7340882
schema:publication	Image and Signal Processing
resource representing a document's body	covid:30d310642ffd1405a613965fc2ce9e2dcca9aaca#body_text
is schema:about of	named entity 'UAV' named entity 'vine' named entity 'visible' named entity 'convolutional' named entity 'STAGE' named entity 'PROBLEM' named entity 'SEGNET' named entity 'VISIBLE' named entity 'UNMANNED' named entity 'AERIAL' named entity 'DEEP LEARNING' named entity 'COMBINED' named entity 'impact' named entity 'problem' named entity 'combined' named entity 'images' named entity 'segmentation' named entity 'Diseases' named entity 'unmanned aerial vehicle' named entity 'UAV' named entity 'DSM' named entity 'point cloud' named entity 'depth map' named entity 'rasters' named entity 'Precision viticulture' named entity 'Infrared' named entity 'image sensors' named entity 'infrared' named entity 'GPS' named entity 'pixel' named entity 'infrared' named entity 'UAV' named entity 'depth map' named entity 'pixel' named entity 'DSM' named entity 'background subtraction' named entity 'UAV' named entity 'digital terrain model' named entity 'DTM' named entity 'vineyard' named entity 'transpiration' named entity 'remote sensing' named entity 'infrared' named entity 'deep learning' named entity 'histogram' named entity 'RGB' named entity 'aerial images' named entity 'raster images' named entity 'infrared' named entity 'point cloud' named entity 'data acquisition' named entity 'DTM' named entity 'DSM' named entity 'rasters' named entity 'point cloud' named entity 'DSM' named entity 'water stress' named entity 'yellow grass' named entity 'GPS' named entity 'deep learning network' named entity 'GPS coordinates' named entity 'deep learning' named entity 'Raster' named entity 'NIR' named entity 'imaging systems' named entity 'rasters' named entity 'image data' named entity 'Normalized Difference Vegetation Index' named entity 'infrared spectrum' named entity 'graphics card' named entity 'evaporate' named entity 'GDAL' named entity 'NVidia' named entity 'digital surface model' named entity 'Intel Xeon' named entity 'DSM' named entity 'DSM' named entity 'mega pixels' named entity 'data acquisition' named entity 'UAV'

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