Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009 Aug;10(8):589-94.
doi: 10.1631/jzus.B0820364.

Prediction of shelled shrimp weight by machine vision

Peng-min Pan  1 , Jian-ping Li, Gu-lai Lv, Hui Yang, Song-ming Zhu, Jian-zhong Lou
Affiliations

Prediction of shelled shrimp weight by machine vision

Peng-min Pan et al. J Zhejiang Univ Sci B. 2009 Aug.

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.

PubMed Disclaimer

Figures

Fig.1
Fig.1
Computer vision system and main steps of weight prediction
Fig.2
Fig.2
Shrimp image processing and feature extracting (a) Original image; (b) Gray scale map; (c) Threshold segmentation; (d) Morphological operation; (e) Edge extracting; (f) Image thinning
Fig.2
Fig.2
Shrimp image processing and feature extracting (a) Original image; (b) Gray scale map; (c) Threshold segmentation; (d) Morphological operation; (e) Edge extracting; (f) Image thinning
Fig.2
Fig.2
Shrimp image processing and feature extracting (a) Original image; (b) Gray scale map; (c) Threshold segmentation; (d) Morphological operation; (e) Edge extracting; (f) Image thinning
Fig.2
Fig.2
Shrimp image processing and feature extracting (a) Original image; (b) Gray scale map; (c) Threshold segmentation; (d) Morphological operation; (e) Edge extracting; (f) Image thinning
Fig.2
Fig.2
Shrimp image processing and feature extracting (a) Original image; (b) Gray scale map; (c) Threshold segmentation; (d) Morphological operation; (e) Edge extracting; (f) Image thinning
Fig.2
Fig.2
Shrimp image processing and feature extracting (a) Original image; (b) Gray scale map; (c) Threshold segmentation; (d) Morphological operation; (e) Edge extracting; (f) Image thinning
Fig.3
Fig.3
(a) Bifurcation-point analysis, (b) EPE method and (c) length reconstruction
Fig.3
Fig.3
(a) Bifurcation-point analysis, (b) EPE method and (c) length reconstruction
Fig.3
Fig.3
(a) Bifurcation-point analysis, (b) EPE method and (c) length reconstruction
See this image and copyright information in PMC

References

    1. Blasco J, Aleixos N, Moltó E. Machine vision system for automatic quality grading of fruit. Biosystems Engineering. 2003;85(4):415–423. doi: 10.1016/S1537-5110(03)00088-6. - DOI
    1. Chen H, Ding YC, Xiong LR, Wen YX. Study on automatic non-destructive detection and grading system of duck egg. Transactions of the Chinese Society for Agricultural Machinery. 2004;35(6):127–129. (in Chinese)
    1. Costa C, Loy A, Cataudella S, Davis D, Scardi M. Extracting fish size using dual underwater cameras. Aquacultural Engineering. 2006;35(3):218–227. doi: 10.1016/j.aquaeng.2006.02.003. - DOI
    1. Dunbrack RL. In situ measurement of fish body length using perspective-based remote stereo-video. Fisheries Research. 2006;82(1-3):327–331. doi: 10.1016/j.fishres.2006.08.017. - DOI
    1. Granitto PM, Navone HD, Verdes PF, Ceccatto HA. Weed seeds identification by machine vision. Computers and Electronics in Agriculture. 2002;33(2):91–103. doi: 10.1016/S0168-1699(02)00004-2. - DOI