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

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.