Design of an Optimum Computer Vision-Based Automatic Abalone (Haliotis discus hannai) Grading Algorithm

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 des...

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Veröffentlicht in:Journal of food science Jg. 80; H. 4; S. E729 - E733
Hauptverfasser: Lee, Donggil, Lee, Kyounghoon, Kim, Seonghun, Yang, Yongsu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Blackwell Publishing Ltd 01.04.2015
Wiley Subscription Services, Inc
Schlagworte:
abalone
Algorithms
Animals
Artificial Intelligence
automatic grading
Automation
Body Weight
computer vision
Design engineering
Estimates
Fish
Food science
Gastropoda
Grading
Haliotis discus hannai
linear models
Mathematical analysis
prediction
Regression
Regression analysis
Shellfish
sorting
volume
weight
volume
weight
abalone
automatic grading
computer vision
ISSN:0022-1147, 1750-3841, 1750-3841
Online-Zugang:Volltext
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Zusammenfassung: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 R2 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 R2 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.
Bibliographie:istex:A56F5CD3F6DCFEE9237DF713DEC450DEAEDBEDCE
National Fisheries Research and Development Institute - No. RP-2015-FE-002
ArticleID:JFDS12799
ark:/67375/WNG-H5BVPF1Q-5
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0022-1147
1750-3841
1750-3841
DOI:10.1111/1750-3841.12799