Prediction of shelled shrimp weight by machine vision.
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.
Pan
Peng-min
PM
Department of Biosystems Engineering, School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Li
Jian-ping
JP
Lv
Gu-lai
GL
Yang
Hui
H
Zhu
Song-ming
SM
Lou
Jian-zhong
JZ
D000818:Animals; D001837:Body Weights and Measures; D016571:Neural Networks, Computer; D033561:Penaeidae / Q000033:anatomy & histology*