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ŠKRABÁNEK, P. DOLEŽEL, P. MATOUŠEK, R. JUNEK, P.
Original Title
RGB Images Driven Recognition of Grapevine Varieties
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Recognition of grapevine varieties; Densely connected convolutional network; In-field images; Agriculture mechanization
Authors
ŠKRABÁNEK, P.; DOLEŽEL, P.; MATOUŠEK, R.; JUNEK, P.
Released
29. 9. 2020
Publisher
Springer International Publishing
Location
Cham
ISBN
978-3-030-57802-2
Book
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)
2194-5365
Periodical
Advances in Intelligent Systems and Computing
Year of study
1268
State
Swiss Confederation
Pages from
216
Pages to
225
Pages count
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" }