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Design of an Optimum Computer Vision‐Based Automatic Abalone ( Haliotis discus hannai ) Grading Algorithm
Author(s) -
Lee Donggil,
Lee Kyounghoon,
Kim Seonghun,
Yang Yongsu
Publication year - 2015
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/1750-3841.12799
Subject(s) - abalone , haliotis discus , artificial intelligence , grading (engineering) , computer vision , fishery , computer science , biology , ecology
An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R 2 value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R 2 value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half‐oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross‐validation indicate root mean square and worst‐case prediction errors of are 2.8 and ±8 g, respectively.