African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6902

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.
  • Google Scholar
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

References

Arakeri MP, Lakshmana (2016). Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. Procedia Comput. Sci. 79:426-433.
Crossref

 

Bato PM, Nagata M, Cao Q, Hiyoshi K, Kitahara T (2000). Study on sorting system for Strawberry using machine vision (Part 2): Development of sorting system with direction and judgement functions for strawberry (Alkihime var.). J. Japanese Soc. Agric. Mach. 62(2):101-110.

 
 

Billingsley J, Bradbeer R (2008). Mechatronics and machine vision in practice. Springer-Verlag Berlin Heidelberg. 305-312 pp. DOI: 10.1007/978-3-540-74027-8.
Crossref

 
 

Bureau of Agriculture and Fisheries Products Standard (BAFPS) (2005). Philippine National Standard/BAFPS. Table Egg – Specifications. PNS/BAFPS 35:2005. ICS 67.120.20.

 
 

Chen YR, Chao K, Kim, MS (2002). Machine vision technology for agricultural applications. Comput. Electron. Agric. 36:173-191.
Crossref

 
 

George M (2015). Multiple fruit and vegetable sorting system using machine vision. Int. J. Adv. Technol. 6:142.
Crossref

 
 

Gomes J, Leta F (2014). Applications of computer vision techniques in the agriculture and food industry: A review. Eur. Food Res. Technol. 235(6):989-1000.
Crossref

 
 

Hashemzadeh M, Farajzadeh N (2016). A machine vision system for detecting fertile eggs in the incubation industry. Int. J. Comput. Intell. Syst. 9(5): 850-862.
Crossref

 
 

Hashemzadeh M (2017). A vision machine for detecting fertile eggs and performance evaluation of neural networks and support vector machines in this machine. Signal and Data Processing. JSDP 2017, 14(3):97-112.
Crossref

 
 

Kopparapu SK (2006). Lighting design for machine vision system. Image Vision Comput. 24:720-726.
Crossref

 
 

Li Y, Dhakal S, Peng Y (2012). A Machine vision system for identification of micro-crack in egg shell. J. Food Eng. 109:127-134.
Crossref

 
 

Mohana SH, Prabhakar CJ (2014). A novel technique for grading of dates using shape and texture features. Machine Learning and Applications: An Int. J. (MLAIJ). 1(2):15-29.

 
 

Murchie L, Xia B, Madden RH, Whyte P, and Kelly L (2008). Qualitative exposure assessment for Salmonella spp. in shell eggs produced on the island of Ireland. Int. J. Food Microbiol. 125:308-319.
Crossref

 
 

Naidu DS (1995). Mechatronics: Designing intelligent machines: Volume 1: Perception, cognition and execution. Book Review. Mechatronics 5:715-716.
Crossref

 
 

Philippine Statistics Authority (PSA) (2017). Chicken situation report (January – December 2016). Diliman, Quezon City, Phils 30 p.

 
 

Quilloy EP, Bato PM (2015). Machine Vision-based software for automating the grading process of Philippine table eggs. Philipp. Agric. Sci. 90(2):148-156.

 
 

Raj MP, Swaminarayan PR (2015). Applications of image processing for grading agriculture products. Int. J. Recent Innov. Trends Comput. Comm. 3(3):1194-1201.
Crossref

 
 

Samiullah CKK, Roberts JR, Sexton M, May D, Kiermeier A (2013). Effects of egg shell quality and washing on Salmonella Infantis penetration. Int. J. Food Microbiol. 165(2):77-83.
Crossref

 
 

Soltani M, Omid M, Alimardani R (2014). Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network. J. Food Sci. Technol. 52(5):3065-3071.
Crossref

 
 

Tian S, Wang Z, Yang J, Huang Z, Wang R, Wang L, Dong J (2017). Development of an automatic visual grading system for grafting seedlings. Adv. Mech. Eng. 9(1):1-12.
Crossref

 
 

United States Department of Agriculture (2000). Egg-grading manual. Agricultural Marketing Service. Agricultural Handbook No. 75.

 
 

Whiley H, Ross K (2015). Salmonella and eggs: from production to plate. Int. J. Environ. Res. Public Health 12(3):2543-2556.
Crossref