A Simple Digital Imaging Method for Dirt Detection on Eggshells

Lunadei, Loredana and Ruiz García, Luis and Guidetti, Riccardo and Bodria, Luigi and Ruiz-Altisent, Margarita (2011). A Simple Digital Imaging Method for Dirt Detection on Eggshells. In: "2011 CIGR Section VI International Symposium", 18-20 Abril 2011, Nantes (France).

Description

Title: A Simple Digital Imaging Method for Dirt Detection on Eggshells
Author/s:
  • Lunadei, Loredana
  • Ruiz García, Luis
  • Guidetti, Riccardo
  • Bodria, Luigi
  • Ruiz-Altisent, Margarita
Item Type: Presentation at Congress or Conference (Poster)
Event Title: 2011 CIGR Section VI International Symposium
Event Dates: 18-20 Abril 2011
Event Location: Nantes (France)
Title of Book: Towards a Sustainable Food Chain - Food Process, Bioprocessing and Food Quality Management
Date: April 2011
Subjects:
Freetext Keywords: Brown eggs, eggshell defect, vision system, multispectral image, image analysis.
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Rural [hasta 2014]
UPM's Research Group: LPF-TAGRALIA
Creative Commons Licenses: None

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Abstract

The objective of this research was to develop an off-line vision system to detect defective eggshells, i.e., with dirty eggshell, by employing a classification algorithm based on a few logical operations, allowing a further implementation in an on-line grading process. In particular, this work was focused to study the feasibility of identifying and differentiating dirt stains on brown eggshells caused by organic residuals, from natural stains, caused by deposits of pigments. Digital images were acquired from 384 clean and dirty brown eggshells by employing a CCD camera endowed with 15 monochromatic filters (440-940 nm). Each dirty eggshell presented only one kind of defect, i.e., blood stains, feathers and white, clear or dark faces, while clean eggshells did not present organic residuals or evidences of feather, but their external color was characterized by clear or dark natural stains. A MatLab® devoted code was developed in order to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group. The proposed classification algorithm was able to correctly classify near 93% of the samples. The robustness of the proposed classification was observed applying an external validation to a second set of samples, obtaining similar percentage of correctly classified samples (92%).

More information

Item ID: 9709
DC Identifier: http://oa.upm.es/9709/
OAI Identifier: oai:oa.upm.es:9709
Deposited by: Investigador contratado Loredana Lunadei
Deposited on: 16 Nov 2011 11:59
Last Modified: 20 Apr 2016 18:01
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