Image Processing Based Classification of Enzymatic Browning in Chopped Apples

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU129520" target="_blank" >RIV/00216305:26220/18:PU129520 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8464181" target="_blank" >https://ieeexplore.ieee.org/document/8464181</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IWOBI.2018.8464181" target="_blank" >10.1109/IWOBI.2018.8464181</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Image Processing Based Classification of Enzymatic Browning in Chopped Apples

Popis výsledku v původním jazyce

Apples are one of the most common fruit on the planet. It is rich in iron, fiber, antioxidants and other nutritive quality; which are incredibly important for human body and brain. The quality of an apple gets affected once they are chopped. This paper presents a non-destructive image processing based algorithm that identifies the presence of enzymatic browning in chopped apples for the determination of its nutrients loss. The proposed imperative assemblage of this image processing algorithm makes it flexible, automatic and non-destructive. The quantification of enzymatic browning in chopped apples has been obtained with high precision using this proposed imaging based method. The machine learning based on strategic selection of discriminatory statistical features of chopped apples extracted in wavelet domain makes it a novel approach. 85% of accuracy has been achieved by using machine learning based Support Vector Machine (SVM) classifier.

Název v anglickém jazyce

Image Processing Based Classification of Enzymatic Browning in Chopped Apples

Popis výsledku anglicky

Apples are one of the most common fruit on the planet. It is rich in iron, fiber, antioxidants and other nutritive quality; which are incredibly important for human body and brain. The quality of an apple gets affected once they are chopped. This paper presents a non-destructive image processing based algorithm that identifies the presence of enzymatic browning in chopped apples for the determination of its nutrients loss. The proposed imperative assemblage of this image processing algorithm makes it flexible, automatic and non-destructive. The quantification of enzymatic browning in chopped apples has been obtained with high precision using this proposed imaging based method. The machine learning based on strategic selection of discriminatory statistical features of chopped apples extracted in wavelet domain makes it a novel approach. 85% of accuracy has been achieved by using machine learning based Support Vector Machine (SVM) classifier.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)

ISBN

978-1-5386-7506-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)

Místo vydání

San Carlos, Costa Rica

Místo konání akce

Dubai, UAE

Datum konání akce

27. 8. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—