Development of an image processing system in splendid squid quality classification

Masunee, Niyada; Chaiprapat, Supapan; Waiyagan, Kriangkrai

doi:10.1117/12.2031254

Cited by 1 publication

(1 citation statement)

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“…The procedure must be handled only by well‐trained operators who have undergone rigorous training courses. Human inspection, however, is prone to errors (Masunee, ). Work performance reportedly deteriorates over extended working hours, leading to unacceptable repeatability and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Automated discrimination of deveined shrimps based on grayscale image parameters

Thanasarn

Chaiprapat

Waiyakan

et al. 2019

J Food Process Engineering

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The “Vein” in a shrimp is its digestive tract filled with grit, sand, and sediments stretching along the back of the abdomen. In most shrimp market forms, presence of vein is highly restricted and limited according to the U.S. standard for imports. This research aims to develop an image‐based approach for detection of improperly deveined shrimps. Two hundred shrimp images were subjected to a sequence of image processing techniques before extracting significant parameters from grayscale images. These parameters include shape measurements and pixel value measurements drawn from an image histogram. In this research, disqualified shrimps were identified by two classification techniques: linear discriminant analysis and support vector machine (SVM). Better than 98% classification accuracy was obtained with the SVM using a polynomial kernel function. The success of this research has filled a void left by past studies to facilitate fully automated shrimp quality inspection. Practical applications Rising wages and labor scarcity are among critical problems to seafood industries, along with low productivity due to ergonomics limitations. Such problems will be even worse in the near future and automated machines are becoming a popular alternative to tackle them. These machines must be driven by an intelligent processing unit capable of handling unavoidable variability naturally found in agricultural products. In most shrimp market forms, presence of veins is highly restricted and limited by the U.S. standards for imports. Deveining always leaves remnants of uncertain length. Employing statistical learning techniques, the approach developed in this study can accurately and automatically discriminate shrimps by acceptability based on the vein. Findings of this research contribute to the development of a fully automated shrimp processing machine, supporting sustainability of the industry by reducing reliance on labor policies and workforce availability.

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Section: Introductionmentioning

confidence: 99%