Visual quality detection of aquatic products using machine vision

Abstract

Aquatic products are popular among consumers and their visual quality used to be detected manually for sorting, grading, species classification and freshness assessment. Machine vision, as a non-destructive method, has been used in external quality detection of aquatic products for its efficiency, objectiveness, consistency and reliability. Quite a number of researches have highlighted its potential for visual quality detection of fishes, fish filets and some other aquatic products (i.e. shrimp, oyster, and scallop). This review introduced detecting methods based on measurement of size, shape, and color using machine vision systems. Size measurement (i.e. length and area) was usually taken for sorting and grading, while shape was measured for species classification with the integration of size information. Color information was studied for analysis of fish filets, fish muscle, fish skin and shrimp, and for color changes of specially treated fish. Machine vision systems used for measuring size, shape, and color was described, including improvements of cameras, illumination settings, image processing and analysis methods, and experimental results as well. With the development in these areas, machine vision technique may achieve higher accuracy and efficiency, and wider application in visual quality detection of aquatic products. Besides, advantages and limitations of these machine vision systems were discussed, with recommendation on future developments.