RGB Images Driven Recognition of Grapevine Varieties

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F20%3A39916809" target="_blank" >RIV/00216275:25530/20:39916809 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-57802-2_21" target="_blank" >http://dx.doi.org/10.1007/978-3-030-57802-2_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-57802-2_21" target="_blank" >10.1007/978-3-030-57802-2_21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

RGB Images Driven Recognition of Grapevine Varieties

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

RGB Images Driven Recognition of Grapevine Varieties

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)

ISBN

978-3-030-57801-5

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

10

Strana od-do

216-225

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Burgos

Datum konání akce

16. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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