Detail publikace

RGB images-driven recognition of grapevine varieties using a densely connected convolutional network

ŠKRABÁNEK, P. DOLEŽEL, P. MATOUŠEK, R.

Originální název

RGB images-driven recognition of grapevine varieties using a densely connected convolutional network

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

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.

Klíčová slova

Recognition of grapevine varieties; Densely connected convolutional network; Data augmentation; In-field images; Edge-computing; Agricultural mechanization

Autoři

ŠKRABÁNEK, P.; DOLEŽEL, P.; MATOUŠEK, R.

Vydáno

16. 2. 2022

ISSN

1367-0751

Periodikum

LOGIC JOURNAL OF THE IGPL

Číslo

jzac029

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

16

Strany počet

16

URL

https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzac029/6529721

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",
  journal="LOGIC JOURNAL OF THE IGPL",
  year="2022",
  number="jzac029",
  pages="1--16",
  doi="10.1093/jigpal/jzac029",
  issn="1367-0751",
  url="https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzac029/6529721"
}