Research Article Open Access

Detection of Rotten Fresh-Cut Cauliflowers based on Machine Vision Technology and Watershed Segmentation Method

Jianxin Xue1, Liang Huang1, Bingyu Mu1, Kai Wang1, Zihui Li1, Haixia Sun1, Huamin Zhao1 and Zezhen Li2
  • 1 College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, China
  • 2 College of Food Science and Engineering, Shanxi Agricultural University, Taigu 030801, China

Abstract

In this study, machine vision technology was used to separate the samples and detect the rotting degrees of fresh-cut cauliflowers. First, the improved watershed algorithm was used for the segmentation of fresh-cut cauliflower samples and the extraction of single-sample. Then, three color models, a gray co-occurrence matrix and two feature extraction algorithms were used to extract the color, texture and spectral feature parameters of the images. At the same time, the Partial Least Squares Discriminant Analysis (PLS-DA) and Extreme Learning Machines (ELM) discriminant models were established. The identification accuracy of PLS-DA and ELM discriminant models for rotting samples was 95 and 90.9%, respectively. Moreover, according to the size of rotten areas, the rotting grades were divided and the contours and feature areas of rotten cauliflower samples were identified by the region growth algorithm and the “Sobel” operator. Finally, the detection and identification of the rotting degree of cauliflower samples were realized. The results showed that machine vision technology can segment the cohesive fresh-cut cauliflower samples and can be used for qualitative and quantitative identification of the intact and rotten cauliflower samples.

American Journal of Biochemistry and Biotechnology
Volume 18 No. 2, 2022, 155-167

DOI: https://doi.org/10.3844/ajbbsp.2022.155.167

Submitted On: 11 October 2021 Published On: 13 April 2022

How to Cite: Xue, J., Huang, L., Mu, B., Wang, K., Li, Z., Sun, H. & Zhao, H. (2022). Detection of Rotten Fresh-Cut Cauliflowers based on Machine Vision Technology and Watershed Segmentation Method. American Journal of Biochemistry and Biotechnology, 18(2), 155-167. https://doi.org/10.3844/ajbbsp.2022.155.167

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Keywords

  • Machine Vision Technology
  • Fresh-Cut Cauliflower
  • Color Features
  • Texture Features
  • Watershed Algorithm