Detail publikačního výsledku

Image Processing Based Classification of Enzymatic Browning in Chopped Apples

ARORA, M; DUTTA, M. K.; TRAVIESO M. C.; BURGET R.

Original Title

Image Processing Based Classification of Enzymatic Browning in Chopped Apples

English Title

Image Processing Based Classification of Enzymatic Browning in Chopped Apples

Type

Paper in proceedings (conference paper)

Original Abstract

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.

English abstract

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.

Keywords

Image segmentation; Feature extraction; Image color analysis;Wavelet domain;Clustering algorithms;Cameras

Key words in English

Image segmentation; Feature extraction; Image color analysis;Wavelet domain;Clustering algorithms;Cameras

Authors

ARORA, M; DUTTA, M. K.; TRAVIESO M. C.; BURGET R.

RIV year

2021

Released

18.07.2018

Publisher

2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)

Location

San Carlos, Costa Rica

ISBN

978-1-5386-7506-9

Book

2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)

Pages from

1

Pages to

8

Pages count

8

URL

https://ieeexplore.ieee.org/document/8464181

Full text in the Digital Library

http://hdl.handle.net/

BibTex

@inproceedings{BUT150883,
  author="ARORA, M and DUTTA, M. K. and TRAVIESO M. C. and BURGET R.",
  title="Image Processing Based Classification of Enzymatic Browning in Chopped Apples",
  booktitle="2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)",
  year="2018",
  pages="1--8",
  publisher="2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)",
  address="San Carlos, Costa Rica",
  doi="10.1109/IWOBI.2018.8464181",
  isbn="978-1-5386-7506-9",
  url="https://ieeexplore.ieee.org/document/8464181"
}