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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU143589" target="_blank" >RIV/00216305:26210/22:PU143589 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216275:25530/22:39919567

Výsledek na webu

<a href="https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzac029/6529721" target="_blank" >https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzac029/6529721</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/jigpal/jzac029" target="_blank" >10.1093/jigpal/jzac029</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

LOGIC JOURNAL OF THE IGPL

ISSN

1367-0751

e-ISSN

1368-9894

Svazek periodika

neuveden

Číslo periodika v rámci svazku

jzac029

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

000756664100001

EID výsledku v databázi Scopus

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