Prediction of shelled shrimp weight by machine vision

作     者：Peng-min PAN Jian-ping LI Gu-lai LV Hui YANG Song-ming ZHU Jian-zhong LOU 

作者机构：Department of Biosystems Engineering School of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310029 China 

出 版 物：《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 (浙江大学学报（英文版）B辑（生物医学与生物技术）)

年 卷 期：2009年第10卷第8期

页      面：589-594页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 08[工学] 080203[工学-机械设计及理论] 0714[理学-统计学（可授理学、经济学学位）] 0802[工学-机械工程] 070103[理学-概率论与数理统计] 0701[理学-数学] 

主　　题：Shelled shrimp Image Feature Length extracting Weight prediction Weight-area-perimeter (WAP) model 

摘      要：The weight of shelled shrimp is an important parameter for grading *** weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp *** 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 *** model was further validated with another set of images from 30 shelled *** a comparison purpose,artificial neural network(ANN) was used for the shrimp weight *** 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.