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Image processing for traceability: a system prototype for the Southern Rock Lobster (SRL) supply chain

Citation

Vo, SA and Scanlan, J and Mirowski, L and Turner, P, Image processing for traceability: a system prototype for the Southern Rock Lobster (SRL) supply chain, Proceedings of DICTA 2018, 10-13 December 2018, Canberra, Australia, pp. 1-8. (2018) [Refereed Conference Paper]


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Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Official URL: https://dicta2018.org/

DOI: doi:10.1109/DICTA.2018.8615842

Abstract

This paper describes how conventional image processing techniques can be applied to the grading of Southern Rock Lobsters (SRL) to produce a high quality data layer which could be an input into product traceability. The research is part of a broader investigation into designing a low-cost biometric identification solution for use along the entire lobster supply chain. In approaching the image processing for lobster grading a key consideration is to develop a system capable of using low cost consumer grade cameras readily available in mobile phones. The results confirm that by combining a number of common techniques in computer vision it is possible to capture and process a set of valuable attributes from sampled lobster image including color, length, weight, legs and sex. By combining this image profile with other pre-existing data on catch location and landing port each lobster can be verifiably tracked along the supply chain journey to markets in China. The image processing research results achieved in the laboratory show high accuracy in measuring lobster carapace length that is vital for weight conversion calculations. The results also demonstrate the capability to obtain reliable values for average color, tail shape and number of legs on a lobster used in grading classifications. The findings are a major first step in the development of individual lobster biometric identification and will directly contribute to automating lobster grading in this valuable Australian fishery.

Item Details

Item Type:Refereed Conference Paper
Keywords:Southern Rock Lobster, supply chain, traceability, automated grading, image processing
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Image Processing
Objective Division:Animal Production and Animal Primary Products
Objective Group:Fisheries - Wild Caught
Objective Field:Wild Caught Rock Lobster
UTAS Author:Vo, SA (Mr Son Vo)
UTAS Author:Scanlan, J (Dr Joel Scanlan)
UTAS Author:Mirowski, L (Dr Luke Mirowski)
UTAS Author:Turner, P (Associate Professor Paul Turner)
ID Code:128462
Year Published:2018
Deposited By:Information and Communication Technology
Deposited On:2018-09-24
Last Modified:2019-03-20
Downloads:72 View Download Statistics

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