Hyperspectral imaging coupled with multivariate analysis and artificial intelligence to the classification of maize kernels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F22%3A43904499" target="_blank" >RIV/60076658:12220/22:43904499 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.international-agrophysics.org/Hyperspectral-imaging-coupled-with-multivariate-analysis-and-artificial-intelligence,147227,0,2.html" target="_blank" >http://www.international-agrophysics.org/Hyperspectral-imaging-coupled-with-multivariate-analysis-and-artificial-intelligence,147227,0,2.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31545/intagr/147227" target="_blank" >10.31545/intagr/147227</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hyperspectral imaging coupled with multivariate analysis and artificial intelligence to the classification of maize kernels

Popis výsledku v původním jazyce

Maize (Zea mays) is one of the key crops in the world, taking third place after wheat and rice in terms of cultivated area. This study aimed to demonstrate the potential of non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm to discriminate between and classify maize kernels in three cultivars by using non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm. Three cultivars of maize kernels were exposed to hyperspectral imaging with 20 replications. Predictor variables included 28 intensities of reflection wave for spectral imaging and 4 variables in terms of the weight, length, width, and thickness of a single kernel. The classification was successfully performed through Linear Discriminant Analysis and Artificial Neural Network methods, taking into account 32, 15, and 5 predictor variables. According to the results, Linear Discriminant Analysis with 32 predictor variables is characterized by a high degree of accuracy (95%). The most important predictor variables included the reflection wave intensity of the third peak, the wavelength intensity of 490 nm, the wavelength intensity of 580 nm, and the weight and thickness of a single kernel.

Název v anglickém jazyce

Hyperspectral imaging coupled with multivariate analysis and artificial intelligence to the classification of maize kernels

Popis výsledku anglicky

Maize (Zea mays) is one of the key crops in the world, taking third place after wheat and rice in terms of cultivated area. This study aimed to demonstrate the potential of non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm to discriminate between and classify maize kernels in three cultivars by using non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm. Three cultivars of maize kernels were exposed to hyperspectral imaging with 20 replications. Predictor variables included 28 intensities of reflection wave for spectral imaging and 4 variables in terms of the weight, length, width, and thickness of a single kernel. The classification was successfully performed through Linear Discriminant Analysis and Artificial Neural Network methods, taking into account 32, 15, and 5 predictor variables. According to the results, Linear Discriminant Analysis with 32 predictor variables is characterized by a high degree of accuracy (95%). The most important predictor variables included the reflection wave intensity of the third peak, the wavelength intensity of 490 nm, the wavelength intensity of 580 nm, and the weight and thickness of a single kernel.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

40101 - Agriculture

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

International Agrophysics

ISSN

0236-8722

e-ISSN

2300-8725

Svazek periodika

36

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

9

Strana od-do

"83–91"

Kód UT WoS článku

000784765200001

EID výsledku v databázi Scopus

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