Publication detail
RGB Images Driven Recognition of Grapevine Varieties
ŠKRABÁNEK, P. DOLEŽEL, P. MATOUŠEK, R. JUNEK, P.
English title
RGB Images Driven Recognition of Grapevine Varieties
Type
conference paper
Language
en
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.
English 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 in English
Recognition of grapevine varieties; Densely connected convolutional network; In-field images; Agriculture mechanization
Released
29.09.2020
Publisher
Springer International Publishing
Location
Cham
ISBN
978-3-030-57802-2
ISSN
2194-5365
Book
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)
Volume
1268
Pages from–to
216–225
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",
volume="1268",
month="September",
pages="216--225",
publisher="Springer International Publishing",
address="Cham",
isbn="978-3-030-57802-2",
issn="2194-5365"
}