Study of color and textural feature variation of carp meat using image processing

Document Type : Research Article

Authors

1 MSc student of Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Khuzestan Agricultural Sciences and Natural Resources University.

2 Assistant professor of Khuzestan Agricultural Sciences and Natural Resources University

3 Graduated MSc of Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Khuzestan Agricultural Sciences and Natural Resources University.

Abstract

Fish quality is affected by terms of handling, maintenance and storage time. These terms make chemical changes in fish and accelerate the deterioration its of tissue and make it dangerous for human body. There are several methods use for assessment of fish freshness, most of them are costly and destructive., Therefore, in this paper a non-destructive machine vision system based on gill and eye color and textural features is proposed. . Accordingly, after segmentation of region of interest in the images (eyes and gills), the color and textural properties of the images were extracted and the most suitable ones were selected using Fisher's selection algorithm and QDA and LDA classification methods were applied. For the QDA classifier , the V_HSV (extracted from the gills), the energy and the contrast (extracted from the fish's eye) and for the LDA classifier, the energy (extracted from the eye), the contrast )extraction from the eye) ,V_HSV (extracted from the gills) ,homogeneity (extracted from the eye) and H_HSV (extracted from the gills) were extracted. The classification accuracy for QDA and LDA were 93% and 96%, respectively.

Graphical Abstract

Study of color and textural feature variation of carp meat using image processing

Highlights

  • The image processing method was used to evaluate the freshness of the fish.
  • The color and textural features of the eye and gill images were extracted and the most suitable ones were selected using the Fisher's feature selection algorithm.
  • For prediction of freshness, the QDA and LDA classifications were used and the accuracy of 93 and 96% were acquired, respectively.

Keywords

Main Subjects


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