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

http://doi.org/10.1631/jzus.B0820364


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.

@article{title="Prediction of shelled shrimp weight by machine vision",
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",
volume="10",
number="8",
pages="589~594",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B0820364"
}

%0 Journal Article
%T Prediction of shelled shrimp weight by machine vision
%A Peng-min PAN
%A Jian-ping LI
%A Gu-lai LV
%A Hui YANG
%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

TY - JOUR
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


Abstract: 
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

Reference

[1] Blasco, J., Aleixos, N., Moltó, E., 2003. Machine vision system for automatic quality grading of fruit. Biosystems Engineering, 85(4):415-423.

[2] Chen, H., Ding, Y.C., Xiong, L.R., Wen, Y.X., 2004. Study on automatic non-destructive detection and grading system of duck egg. Transactions of the Chinese Society for Agricultural Machinery, 35(6):127-129 (in Chinese).

[3] Costa, C., Loy, A., Cataudella, S., Davis, D., Scardi, M., 2006. Extracting fish size using dual underwater cameras. Aquacultural Engineering, 35(3):218-227.

[4] Dunbrack, R.L., 2006. In situ measurement of fish body length using perspective-based remote stereo-video. Fisheries Research, 82(1-3):327-331.

[5] Granitto, P.M., Navone, H.D., Verdes, P.F., Ceccatto, H.A., 2002. Weed seeds identification by machine vision. Computers and Electronics in Agriculture, 33(2):91-103.

[6] Granitto, P.M., Verdes, P.F., Ceccatto, H.A., 2005. Large-scale investigation of weed seed identification by machine vision. Computers and Electronics in Agriculture, 47(1):15-24.

[7] Harbitz, A., 2007. Estimation of shrimp (Pandalus borealis) carapace length by image analysis. ICES Journal of Marine Science, 64(5):939-944.

[8] Harvey, E., 2003. The accuracy and precision of underwater measurements of length and maximum body depth of southern bluefin tuna (Thunnus maccoyii) with a stereo-video camera system. Fisheries Research, 63(3):315-326.

[9] Kassler, M., Corke, P., Wong, P., 1993. Automatic grading and packing of prawns. Computers and Electronics in Agriculture, 9(4):319-333.

[10] Kim, Y., Reid, J.F., Zhang, Q., 2008. Fuzzy logic control of a multispectral imaging sensor for in-field plant sensing. Computers and Electronics in Agriculture, 60(2): 279-288.

[11] Kılıç, K., Boyacı, İ.K., Köksel, H., Küsmenoğlu, İ., 2007. A classification system for beans using computer vision system and artificial neural networks. Journal of Food Engineering, 78(3):897-904.

[12] Lin, A.G., Sun, B.Y., Yada, S., 2006. Studies on the detecting method of scallop grading based on machine vision. Journal of Fisheries of China, 30(3):397-403 (in Chinese).

[13] Ling, P.P., Searcy, S.W., 1991. Feature extraction for a machine-vision-based shrimp deheader. American Society of Agricultural Engineers, 34(6):2631-2636.

[14] Luzuriaga, D.A., Balaban, M.O., Yeralan, S., 1997. Analysis of visual quality attributes of white shrimp by machine vision. Journal of Food Science, 62(1):113-118.

[15] Menesatti, P., Zanella, A., D′Andrea, S., Costa, C., Paglia, G., Pallottino, F., 2009. Supervised multivariate analysis of hyperspectral NIR images to evaluate the starch index of apples. Food and Bioprocess Technology, 2(3):308-314.

[16] Systat Software Inc., 2002a. TableCurve3D for Windows. Version 4.0, San Jose, CA.

[17] Systat Software Inc., 2002b. TableCurve2D for Windows. Version 5.01, San Jose, CA.

[18] Throop, J.A., Aneshansley, D.J., Anger, W.C., Peterson, D.L., 2005. Quality evaluation of apples based on surface defects: development of an automated inspection system. Postharvest Biology and Technology, 36(3):281-290.

[19] White, D.J., Svellingen, C., Strachan, N.J.C., 2006. Automated measurement of species and length of fish by computer vision. Fisheries Research, 80(2-3):203-210.

[20] Xu, J.Y., Miao, X.W., Liu, Y., Cui, S.R., 2005. Behavioral response of tilapia (Oreochromis niloticus) to acute ammonia stress monitored by computer vision. Journal of Zhejiang University SCIENCE B, 6(8):812-816.

[21] Ying, Y.B., 2000. Research in method to detect size and area of fruits by machine vision. Journal of Zhejiang University (Agric. & Life Sci.), 26(3):229-232 (in Chinese).

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