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ŠKRABÁNEK, P. DOLEŽEL, P. MATOUŠEK, R. JUNEK, P.
Originální název
RGB Images Driven Recognition of Grapevine Varieties
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Recognition of grapevine varieties; Densely connected convolutional network; In-field images; Agriculture mechanization
Autoři
ŠKRABÁNEK, P.; DOLEŽEL, P.; MATOUŠEK, R.; JUNEK, P.
Vydáno
29. 9. 2020
Nakladatel
Springer International Publishing
Místo
Cham
ISBN
978-3-030-57802-2
Kniha
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)
ISSN
2194-5365
Periodikum
Advances in Intelligent Systems and Computing
Ročník
1268
Stát
Švýcarská konfederace
Strany od
216
Strany do
225
Strany počet
10
BibTex
@inproceedings{BUT165498, author="Pavel {Škrabánek} and Petr {Doležel} and Radomil {Matoušek} and Petr {Junek}", title="RGB Images Driven Recognition of Grapevine Varieties", booktitle="15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)", year="2020", journal="Advances in Intelligent Systems and Computing", volume="1268", pages="216--225", publisher="Springer International Publishing", address="Cham", doi="10.1007/978-3-030-57802-2\{_}21", isbn="978-3-030-57802-2", issn="2194-5365" }