Using Image Processing to Automatically Measure Pearl Oyster Size for Selective Breeding

Abstract

The growth rate is a genetic trait that is often recorded in pearl oyster farming for use in selective breeding programs. By tracking the growth rate of a pearl oyster, farmers can make better decisions on which oysters to breed or manage in order to produce healthier offspring and higher quality pearls. However, the current practice of measurement by hand results in measurement inaccuracies, slow processing, and unnecessary employee costs. To rectify this, we propose automating the workflow via computer vision techniques, which can be used to capture images of pearl oysters and process the images to obtain the absolute measurements of each oyster. Specifically, we utilise and compare a set of edge detection algorithms to produce an image-processing algorithm that automatically segments an image containing multiple oysters and returns the height and width of the oyster shell. Our final algorithm was tested on images containing 2523 oysters (Pinctada maxima) captured on farming boats in Indonesia. This algorithm achieved reliability (of identifying at least one required oyster measurement correctly) equal to 92.1%.