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Received: 2008-11-14

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Crosschecked: 2009-06-26

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Journal of Zhejiang University SCIENCE B 2009 Vol.10 No.8 P.589~594


Prediction of shelled shrimp weight by machine vision

Author(s):  Peng-min PAN, Jian-ping LI, Gu-lai LV, Hui YANG, Song-ming ZHU, Jian-zhong LOU

Affiliation(s):  Department of Biosystems Engineering, School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   jpli@zju.edu.cn

Key Words:  Shelled shrimp, Image, Feature, Length extracting, Weight prediction, Weight-area-perimeter (WAP) model

Peng-min PAN, Jian-ping LI, Gu-lai LV, Hui YANG, Song-ming ZHU, Jian-zhong LOU. Prediction of shelled shrimp weight by machine vision[J]. Journal of Zhejiang University Science B, 2009, 10(8): 589~594.

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author="Peng-min PAN, Jian-ping LI, Gu-lai LV, Hui YANG, Song-ming ZHU, Jian-zhong LOU",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

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%T Prediction of shelled shrimp weight by machine vision
%A Peng-min PAN
%A Jian-ping LI
%A Gu-lai LV
%A Song-ming ZHU
%A Jian-zhong LOU
%J Journal of Zhejiang University SCIENCE B
%V 10
%N 8
%P 589~594
%@ 1673-1581
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0820364

T1 - Prediction of shelled shrimp weight by machine vision
A1 - Peng-min PAN
A1 - Jian-ping LI
A1 - Gu-lai LV
A1 - Hui YANG
A1 - Song-ming ZHU
A1 - Jian-zhong LOU
J0 - Journal of Zhejiang University Science B
VL - 10
IS - 8
SP - 589
EP - 594
%@ 1673-1581
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0820364

The weight of shelled shrimp is an important parameter for grading process. The weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp thickness. In this paper, a multivariate prediction model containing area, perimeter, length, and width was established. A new calibration algorithm for extracting length of shelled shrimp was proposed, which contains binary image thinning, branch recognition and elimination, and length reconstruction, while its width was calculated during the process of length extracting. The model was further validated with another set of images from 30 shelled shrimps. For a comparison purpose, artificial neural network (ANN) was used for the shrimp weight predication. The ANN model resulted in a better prediction accuracy (with the average relative error at 2.67%), but took a tenfold increase in calculation time compared with the weight-area-perimeter (WAP) model (with the average relative error at 3.02%). We thus conclude that the WAP model is a better method for the prediction of the weight of shelled red shrimp.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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