Detail publikace
RGB images-driven recognition of grapevine varieties using a densely connected convolutional network
ŠKRABÁNEK, P. DOLEŽEL, P. MATOUŠEK, R.
Anglický název
RGB images-driven recognition of grapevine varieties using a densely connected convolutional network
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
en
Originální abstrakt
We present a pocket-size densely connected convolutional network (DenseNet) directed to classification of size-normalized colour images according to varieties of grapes captured in those images. We compare the DenseNet with three established small-size networks in terms of performance, inference time, and model size. We propose a data augmentation that we use in training the networks. We train and evaluate the networks on in-field images. The trained networks distinguish between seven grapevine varieties and background, where four and three varieties, respectively are of red and green grapes. Compared to the established networks, the DenseNet is characterized by near state-of-the-art performance, short inference time, and minimal model size. All these aspects qualify the network for real-time, mobile, and edge computing applications. The DenseNet opens possibilities for constructing affordable selective harvesters in accordance with agriculture 4.0.
Anglický abstrakt
We present a pocket-size densely connected convolutional network (DenseNet) directed to classification of size-normalized colour images according to varieties of grapes captured in those images. We compare the DenseNet with three established small-size networks in terms of performance, inference time, and model size. We propose a data augmentation that we use in training the networks. We train and evaluate the networks on in-field images. The trained networks distinguish between seven grapevine varieties and background, where four and three varieties, respectively are of red and green grapes. Compared to the established networks, the DenseNet is characterized by near state-of-the-art performance, short inference time, and minimal model size. All these aspects qualify the network for real-time, mobile, and edge computing applications. The DenseNet opens possibilities for constructing affordable selective harvesters in accordance with agriculture 4.0.
Klíčová slova anglicky
Recognition of grapevine varieties; Densely connected convolutional network; Data augmentation; In-field images; Edge-computing; Agricultural mechanization
Vydáno
16.02.2022
ISSN
1367-0751
Číslo
jzac029
Strany od–do
1–16
Počet stran
16
BIBTEX
@article{BUT176693,
author="Pavel {Škrabánek} and Petr {Doležel} and Radomil {Matoušek},
title="RGB images-driven recognition of grapevine varieties using a densely connected convolutional network",
year="2022",
number="jzac029",
month="February",
pages="1--16",
issn="1367-0751"
}