Prediction of shelled shrimp weight by machine vision

Abstract

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.

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

“…ANNs construct a suitable relationship between input data and the target responses without any need for a theoretical model with which to work (Hua et al 2011). ANNs can be applied in almost every aspect of food processing, from raw material assessing (Wang et al 2008a;Pan et al 2009), thermal processing (Houessou et al 2008;Hernandez 2009;Omid et al 2011), fermentation (Wang et al 2008b), enzymatic hydrolysis, antioxidant activity and anthocyanin content (Taghadomi-saberi et al 2013), ultra-filtrating (Sun et al 2004) and drying (Poonnoy et al 2007;Omid et al 2009) to composition detecting (Afkhami et al 2009;Torrecilla et al 2008), quality-assessing (Dutta et al 2003;Sobel and Ballantine 2008;Pan et al 2011) and safety-evaluating (Gupta et al 2004;Panagou et al 2007). …”

The Measurement of Fish Size by Machine Vision - A Review

“…The error was less than 3%. Pan et al [58] used area, perimeter, length and width information to estimate the weight of shelled shrimp in a light box. Image thinning was achieved by the MATLAB function bwmorph with parameter thin.…”

Classification of Boiled Shrimp's Shape Using Image Analysis and Artificial Neural Network Model

“…) and shrimp's weight estimation (Pan et al . ; Poonnoy and Chum‐in ), the development of a computer algorithm for boiled shrimp's shape classification has not been reported. This article presents the image analysis technique and artificial neural network (ANN) used for boiled shrimp's shape classification.…”

“…Numerous advanced-shape representation and description techniques had been developed for object classification (Zhang and Lu 2004). Although CVS has been successfully applied for many processes in shrimp manufacturing such as grading and packing (Kassler et al 1993), white shrimp's visual attributions evaluation (Luzuriaga et al 1997) and shrimp's weight estimation (Pan et al 2009;Poonnoy and Chum-in 2012), the development of a computer algorithm for boiled shrimp's shape classification has not been reported. This article presents the image analysis technique and artificial neural network (ANN) used for boiled shrimp's shape classification.…”