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Automated discrimination of deveined shrimps based on grayscale image parameters
Author(s) -
Thanasarn Nawaporn,
Chaiprapat Supapan,
Waiyakan Kriangkrai,
Thongkaew Kunlapat
Publication year - 2019
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.13041
Subject(s) - shrimp , grayscale , artificial intelligence , support vector machine , computer science , pixel , histogram , image processing , pattern recognition (psychology) , agricultural engineering , mathematics , image (mathematics) , biology , engineering , fishery
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.