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Ripeness determination in feijoa fruits by using a computer vision system and colour. information

Abstract

Determine the ripeness of agricultural products generally depends on an analysis by human experts. The final decision on the state of maturity where the product is found, requires correlating some of their physical features with chemical and internal characteristics of the fruit. The need to preserve the integrity of the fruit on this analysis requires implementation of technologies to pass judgment on its condition without destroying it. The use of colour index, as physical property, contributes to solving this problem. In this document is presented a machine vision system to classify into three stages of maturity a specific exotic fruit: feijoa -Acca sellowiana-. The obtained classification using artificial intelligence tools, as these are artificial neural networks, have shown an adequate classification over 90% from 156 images of Feijoa fruit used in the study.

Keywords

Acca sellowiana, feijoa, pattern recognition, computer vision system.

PDF (Español)

Author Biography

Juan Pablo Bonilla-González

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References

  1. Abdullah, M.Z., Mohamad-Saled, J., Fathinul-Syahir, A.S., & Mohd-Azemi, B.M.N. (2006). Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system. Journal of Food Engineering, 76 (4), 506-523. doi: 10.1016/j.jfoodeng.2005.05.053
  2. Arivazhagan, S., Newlin, R., Selva, S., & Ganesan L. (2010). Fruit Recognition using color and texture features. Journal of Emerging Trends in Computing and Information Sciences, 1 (2), 90-94. Recuperado de: http://www.cisjournal.org/archive/vol1no1/vol1no1_12.pdf
  3. Baron, G. (2014). Influence of data discretization on efficiency of Bayesian classifier for authorship attribution. Procedia Computer Science, 35, 1112-1121. doi: 10.1016/j.procs.2014.08.201
  4. Blasco, J., Aleixos, N., & Moltó E. (2003). Machine vision system for automatic quality grading of fruit. Biosystems Egineering, 85 (4), 415-423. doi: /10.1016/S1537-5110(03)00088-6
  5. Bustamante-Zapata, L.F., Porto-Pérez, I.A., & Hernández-Taboada, F. (2013). Gestión estratégica de las áreas funcionales de la empresa: una perspectiva competitiva internacional. Revista de Investigación, Desarrollo e Innovación, 4 (1), 56-68. doi: 10.19053/20278306.2607
  6. Cárdenas, J. A., & Prieto-Ortíz, F. A. (2015). Diseño de un algoritmo de corrección automática de posición para el proceso de perforado PCB, empleando técnicas de visión artificial. Revista de Investigación, Desarrollo e Innovación, 5 (2), 107-118. doi: 10.19053/20278306.3720
  7. Cerón-Correa, A., Salazar-Jiménez, A. E. & Prieto-Ortiz, F. A. (2013). Reconocimiento de rostros y gestos faciales mediante un análisis de relevancia con imágenes 3D. Revista de Investigación, Desarrollo e Innovación, 4 (1), 7-20. doi:10.19053/20278306.2563
  8. Danti, A., & Suresha. (2012). Segmentation and classification of raw arecanuts based on three sigma control limits. Procedia Technology, 4, 215-219. doi: 10.1016/j.protcy.2012.05.032
  9. Du, C.J., & Sun D.W. (2006). Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering, 72 (1), 39-55. doi: 10.1016/j.jfoodeng.2004.11.017
  10. East, A.R., Trejo-Araya, X.I., Hertog, M.L.A.T.M, Nicholson, S.E., & Mawson A.J. (2009). The effect of controlled atmospheres on respiration and rate of quality change in 'Unique' feijoa fruit. Postharvest Biology and Technology, 53 (1-2), 66-71. doi: 10.1016/j.postharvbio.2009.02.002
  11. García, Y., García, A., Hernández, A., & Pérez J. (2011). Estudio de la variación del índice de color durante la conservación de la piña variedad Cayena Lisa a temperatura ambiente. Revista Ciencias Técnicas Agropecuarias, 20 (4), 12-16. Recuperado de: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2071-00542011000400002
  12. Kondo, N., Ahmad, U., Monta, M., & Murase, H. (2000). Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Computers and electronics in agriculture, 29 (1-2), 135-147. doi: 10.1016/S0168-1699(00)00141-1
  13. León, K., Mery, D., Pedreschi, F., & León J. (2006). Color measurement in L*a*b* units from RGB digital images. Food Research International, 39 (10), 1084-1091. doi: 10.1016/j.foodres.2006.03.006
  14. Liu, Z., Pan, Q., & Dezert, J. (2013). A new belief-based K-nearest neighbor classification method. Pattern Recognition, 46 (3), 834-844. doi: 10.1016/j.patcog.2012.10.001
  15. Malaur, S., Manry, M., & Jesudhas, P. (2015). Multiple optimal learning factors for the multi-layer perceptron. Neurocomputing, 149 (Parte C), 1490-1501. doi: 10.1016/j.neucom.2014.08.043
  16. Manickavasagana, A., Al-Mezeinia, N.K., & Al-Shekaili, H.N. (2014). RGB color imaging technique for grading of dates. Scientia Horticulturae, 175, 87-94. doi: 10.1016/j.scienta.2014.06.003
  17. Ohali, Y.A. (2011). Computer vision based date fruit grading system: Design and implementation. Journal of King Saud University - Computer and Information Sciences, 23 (1), 29-36. doi: 10.1016/j.jksuci.2010.03.003
  18. Sanabria-Neira, N. C., & Puentes-Montañez, G. A. (2011). Sistema de gestión de calidad para el agronegocio de la uchuva en el municipio de Ventaquemada. Revista de Investigación, Desarrollo e Innovación, 1 (2), 28-39. Recuperado de: http://revistas.uptc.edu.co/revistas/index.php/investigacion_duitama/article/view/1300
  19. Sandoval, Z. (2005). Caracterización y clasificación de café cereza usando visión artificial (Tesis de Maestría). Universidad Nacional de Colombia - Sede Manizales, Colombia.
  20. Simpson, N. (2011). Color and contemporary digital botanical illustration. Optics & Laser Technology, 43 (2), 330-336. doi: 10.1016/j.optlastec.2008.12.014
  21. Thompson, K. (1998). Tecnología post-cosecha de frutas y hortalizas [CD-ROM]. Colombia: Distrididactika Ldta.
  22. Torres, R., Montes, E., Perez, O., & Andrade, R. (2013). Relación del color y del estado de madurez con las propiedades fisicoquímicas de frutas tropicales. Información Tecnológica, 24 (3), 51-56. doi: 10.4067/S0718-07642013000300007
  23. Torres-Barahona, E. A., León-Medina, J. X., & Torres-Díaz, E. (2012). Sistema de posicionamiento aplicado a la técnica de impresión 3D modelado por deposición fundida. Revista de Investigación, Desarrollo e Innovación, 3 (1), 25-32. Recuperado de http://revistas.uptc.edu.co/revistas/index.php/investigacion_duitama/article/view/2135/2091
  24. Vignoni, L., Cesari, R., Forte, M., & Mirabile, M. (2006). Determinación de índice de color en ajo picado. Información Tecnológica, 17 (6), 63-67. doi: 10.4067/S0718-07642006000600011
  25. Yama K.L., & Papadakis S.E. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering, 61 (1), 137-142. doi: 10.1016/S0260-8774(03)00195-X

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