African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6691

Full Length Research Paper

Single-line automated sorter using mechatronics and machine vision system for Philippine table eggs

Erwin P. Quilloy
  • Erwin P. Quilloy
  • Agricultural Machinery Division, Institute of Agricultural Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines, Los Baños, Philippines.
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Delfin C. Suministrado
  • Delfin C. Suministrado
  • Agricultural Machinery Division, Institute of Agricultural Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines, Los Baños, Philippines.
  • Google Scholar
Pepito M. Bato
  • Pepito M. Bato
  • Agricultural Machinery Division, Institute of Agricultural Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines, Los Baños, Philippines.
  • Google Scholar


  •  Received: 12 March 2018
  •  Accepted: 26 March 2018
  •  Published: 26 April 2018

Abstract

An automated single-line table egg sorting machine that integrates machine vision and mechatronics principles was developed in this study. The machine was fabricated using low cost and locally available materials. The developed machine was composed of the feeding unit, computing unit and the sorting unit. The conveyor was powered by a 12V DC geared motor, and the sorting arm was actuated by a DC servo motor which positions the arm. A machine vision software, EGGSoTiC, was used in the sorting of the table eggs – moving through a conveyor at 13 cm-s-1. Test for similarity of readings revealed that the developed machine is capable of yielding consistent results with low values of coefficient of variation ranging from 0.38 to 0.85 mm2 and 0.42 to 0.94 g for the projected area and estimated weight, respectively. Results of dynamic test for 100 table egg samples indicated that the machine could sort table eggs with an accuracy of 91% at 2.52 seconds per sample, yielding a projected capacity of 1,426 eggs per hour. Results also indicated that there were no large errors in the estimation of weights yielding a root mean square error of 1.90 g which is not significantly higher than the bias of 0.93 g. 
 
Key words: Machine vision, table eggs, sorting, mechatronics.