RGB Images Driven Recognition of Grapevine Varieties

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F20%3A39916809" target="_blank" >RIV/00216275:25530/20:39916809 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-57802-2_21" target="_blank" >http://dx.doi.org/10.1007/978-3-030-57802-2_21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-57802-2_21" target="_blank" >10.1007/978-3-030-57802-2_21</a>

Result language
angličtina
Original language name
RGB Images Driven Recognition of Grapevine Varieties
Original language description
We present a grapevine variety recognition system based on a densely connected convolutional network. The proposed solution is aimed as a data processing part of an affordable sensor for selective harvesters. The system classifies size normalized RGB images according to varieties of grapes captured in the images. We train and evaluate the system on in-field images of ripe grapes captured without any artificial lighting, in a direction of sunshine likewise in the opposite direction. A dataset created for this purpose consists of 7200 images classified into 8 categories. The system distinguishes among seven grapevine varieties and background, where four and three varieties have red and green grapes, respectively. Its average per-class classification accuracy is at 98.10% and 97.47% for red and green grapes, respectively. The system also well differentiates grapes from background. Its overall average per-class accuracy is over 98%. The evaluation results show that conventional cameras in combination with the proposed system allow construction of affordable automatic selective harvesters.
Czech name
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Czech description
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Project
<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)
ISBN
978-3-030-57801-5
ISSN
2194-5357
e-ISSN
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Number of pages
10
Pages from-to
216-225
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Burgos
Event date
Sep 16, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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