IN VIVO FISH DIET DISCRIMINATION USING SELECTED HYPERSPECTRAL IMAGE CLASSIFICATION METHODS

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F18%3A43899358" target="_blank" >RIV/60076658:12520/18:43899358 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8747074" target="_blank" >https://ieeexplore.ieee.org/document/8747074</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

IN VIVO FISH DIET DISCRIMINATION USING SELECTED HYPERSPECTRAL IMAGE CLASSIFICATION METHODS

Popis výsledku v původním jazyce

The main aim of this study was to evaluate the performance of different supervised classification methods to discriminate live fish based on their diet received during cultivation using hyperspectral imagery system. 160 rainbow trout were fed either a commercial based diet or completely plant-based diet. Hyperspectral images of the live fish acquired in the spectral region of 394-1090 nm. Spectra were extracted from the region of interest and pre-processed using Savitzky-Golay smoothing algorithm to remove noise. Afterward, three classifiers including support vector machine, random forest and k-nearest neighbors were used. According to the criteria of correct classification rate and kappa coefficient, the support vector machine with linear kernel was achieved the best performance for classifying live fish due to their diet.

Název v anglickém jazyce

IN VIVO FISH DIET DISCRIMINATION USING SELECTED HYPERSPECTRAL IMAGE CLASSIFICATION METHODS

Popis výsledku anglicky

The main aim of this study was to evaluate the performance of different supervised classification methods to discriminate live fish based on their diet received during cultivation using hyperspectral imagery system. 160 rainbow trout were fed either a commercial based diet or completely plant-based diet. Hyperspectral images of the live fish acquired in the spectral region of 394-1090 nm. Spectra were extracted from the region of interest and pre-processed using Savitzky-Golay smoothing algorithm to remove noise. Afterward, three classifiers including support vector machine, random forest and k-nearest neighbors were used. According to the criteria of correct classification rate and kappa coefficient, the support vector machine with linear kernel was achieved the best performance for classifying live fish due to their diet.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 9TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS)

ISBN

978-1-72811-581-8

ISSN

2158-6268

e-ISSN

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Počet stran výsledku

5

Strana od-do

5

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Amsterdam

Datum konání akce

23. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000482659100036