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Vol. 13 Núm. 1 (2016)

ARTICULOS

Identificación del estado de madurez de las frutas con redes neuronales artificiales, una revisión

https://doi.org/10.19053/01228420.4811

Publicado 2016-01-01

  • Gustavo Andrés Figueredo-Ávila
    • ,
  • Javier Antonio Ballesteros-Ricaurte

Gustavo Andrés Figueredo-Ávila
Universidad Pedagógica y Tecnológica de Colombia (Tunja, Colombia).

Javier Antonio Ballesteros-Ricaurte
Universidad Pedagógica y Tecnológica de Colombia (Tunja, Colombia).

Resumen

La aplicación de las Redes Neuronales Artificiales (RNA) y de la visión artificial tiene cada vez más acogida en la industria de productos alimenticios, estas técnicas priorizan la clasificación, el reconocimiento de patrones y la predicción de las cosechas y de los cambios físicos de sus productos. En este artículo se define el concepto de red neuronal y se describen sus principales características y modelos, y, por otro lado, se define el concepto de procesamiento de imágenes digitales y las diferentes etapas que lo componen. Complementariamente, se exponen las generalidades de la inspección de frutas (enfocada en Colombia) y sus técnicas. Finalmente, se especifican diferentes trabajos en los que se aplicaron técnicas de RNA y visión artificial en el campo de los productos alimenticios, dispuestos por áreas de  aplicación, y se identifica de manera concluyente el impacto que estas dos técnicas tienen para la clasificación, el reconocimiento de patrones y la predicción en el sector de productos alimenticios.

Palabras clave

inspección de alimentos, procesamiento de imágenes, reconocimiento de objetos, Redes Neuronales Artificiales (RNA)

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Referencias

  • (1) Kung SY. Digital neural networks. Prentice Hall, 1993.
  • (2) Hassoun MH. Threshold Gates, in Fundamentals of Artificial Neural Networks. MIT Press, 1995.
  • (3) Bishop CM. Statical Pattern Recognition, in Neural Networks for Pattern Recognition. Oxford University Press, 1995.
  • (4) Chen G and Dong X, From Chaos to Order: Methodologies, Perspectives, and Applications. World Scientific, 1998. DOI: http://dx.doi.org/10.1142/3033.
  • (5) Fausett LV. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, 1994, pp. 1-7.
  • (6) Lin CT, Lin CT and Lee CSG. Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice Hall PTR, 1996.
  • (7) Rosenblatt F and Laboratory CA. The perceptron: a theory of statistical separability in cognitive systems (Project Para). Cornell Aeronautical Laboratory, 1958.
  • (8) Werbos PJ. The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting. J. Wiley & Sons, 1994.
  • (9) González JRH, Lez JRHG, Coaut MHVJ and Hernando VJM. Redes neuronales artificiales: Fundamentos, modelos y aplicaciones. Addison-Wesley Iberoamericana, 1995.
  • (10) Serrano AJ, Soria E and Martín JD. Redes Neuronales - OCW de la Universitat de Valencia, 2010. [En línea]. Disponible en: http://ocw.uv.es/ingenieria-y-arquitectura/1-2/Course_listing.
  • (11) Sucar LE and Gómez G. Procesamiento del color, in Visión Computacional. pp. 60.
  • (12) Mejía Vilet JR. Procesamiento del color, en Procesamiento Digital de Imágenes, pp. 64.
  • (13) Parker JR. Edge-Detection Techniques, in Algorithms for Image Processing and Computer Vision. John Wiley & Sons, 2010, pp. 39.
  • (14) Ben-Hanan U, Peleg K and Gutman P. Classification of fruits by a boltzmann perceptron neural network, Automatica, 1992; 28 (5): 961-968. DOI: http://dx.doi.org/10.1016/0005-1098(92)90148-9.
  • (15) Yang Q. Classification of apple surface features using machine vision and neural networks. Computers and Electronics in Agriculture, 1993; 9 (1): 1-12. DOI: http://dx.doi.org/10.1016/0168-1699(93)90025-V.
  • (16) Nakano K. Application of neural networks to the color grading of apples. Computers and Electronics in Agriculture, 1997; 18 (2-3): 105-116. DOI: http://dx.doi.org/10.1016/S0168-1699(97)00023-9.
  • (17) Morimoto T and Hashimoto Y. An intelligent control for greenhouse automation, oriented by the concepts of SPA and SFA - an application to a post-harvest process. Computers and Electronics in Agriculture, 2000; 29 (1-2): 179- 194. DOI: http://dx.doi.org/10.1016/S0168-1699(00)00133-2.
  • (18) Effendi Z, Ramli R, Ghani JA and Rahman MNA. A Back Propagation Neural Networks for Grading Jatropha curcas Fruits Maturitiy. American Journal of Applied Sciences, 2010; 7 (3): 390-394. DOI: http://dx.doi.org/10.3844/ajassp.2010.390.394.
  • (19) Mustafa NBA, Arumugam K, Ahmed SK and Sharrif ZAM. Classification of fruits using Probabilistic Neural Networks - Improvement using color features. IEEE, 2011, pp. 264-269. DOI: http://dx.doi.org/10.1109/TENCON.2011.6129105.
  • (20) Jayas DS, Paliwal J and Visen NS. Multi-layer Neural Networks for Image Analysis of Agricultural Products. Journal of Agricultural Engineering Research, 2000; 77 (2): 119-128. DOI: http://dx.doi.org/10.1006/jaer.2000.0559.
  • (21) Leemans V, Magein H and Destain MF. Fruit Grading according to their External Quality using Machine Vision. Biosystems Engineering, 2002; 83 (4): 397-404. DOI: http://dx.doi.org/10.1006/bioe.2002.0131.
  • (22) Kavdır I and Guyer DE. Comparison of Artificial Neural Networks and Statistical Classifiers in Apple Sorting using Textural Features. Biosyst. Eng., 2004; 89 (3): 331-344. DOI: http://dx.doi.org/10.1016/j.biosystemseng.2004.08.008.
  • (23) Xiaobo Z, Jiewen Z and Yanxiao L. Apple color grading based on organization feature parameters. Pattern Recognit. Lett, 2007; 28 (15): 2046-2053. DOI: http://dx.doi.org/10.1016/j.patrec.2007.06.001.
  • (24) Kondo N, Ahmad U, Monta M and Murase H. Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Computers and Electronics in Agriculture, 2000; 29 (1-2): 135-147.
  • (25) Morimoto T, Takeuchi T, Miyata H and Hashimoto Y. Pattern recognition of fruit shape based on the concept of chaos and neural networks. Computers of Electronics in Agriculture, 2000; 26 (2): 171-186. DOI: http://dx.doi.org/10.1016/S0168-1699(00)00070-3.
  • (26) Hobani AI, Thottam AM and Ahmed KAM. Development of a Neural Network Classifier for Date Fruit Varieties Using Some Physical Attributes. Agric Res Cent. King Saud Univ, 2003; 126: 5-18.
  • (27) Ashok V and Vinod DS. Automatic quality evaluation of fruits using Probabilistic Neural Network approach. IEEE, 2014; 308-311.
  • (28) Simões AS, Reali Costa A, Hirakawa AR and Saraiva AM. Applying neural networks to automated visual fruit sorting, in World Congress of Computer in Agriculture WCCA, 2001; 1-7.
  • (29) Ji H and Yuan J. The Application Study of Apple Color Grading by Particle Swarm Optimization Neural Networks. IEEE, 2006; 1: 2651-2654. DOI: http://dx.doi.org/10.1109/WCICA.2006.1712843.
  • (30) Unay D and Gosselin B. Automatic defect segmentation of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharvest Biol. Technol., 2006; 42 (3): 271-279. DOI: http://dx.doi.org/10.1016/j.postharvbio.2006.06.010.
  • (31) Zhang Y, Wang S, Ji G and Phillips P. Fruit classification using computer vision and feedforward neural network. Journal Food Engineering, 2014; 143: 167-177. DOI: http://dx.doi.org/10.1016/j.jfoodeng.2014.07.001.
  • (32) Brosnan T and Sun DW. Inspection and grading of agricultural and food products by computer vision systems–a review. Comput. Electron. Agric., 2002; 36 (2-3): 193-213. DOI: http://dx.doi.org/10.1016/S0168-1699(02)00101-1.
  • (33) Fernández Andrés JC, Suardíaz Muro J, Navarro Lorente PJ, Toledo Moreo A, Jiménez Buendía M, Ortiz Zaragoza FJ and Iborra García AJ. Uso de Redes Neuronales para el Análisis de Formas Naturales, 2002; 1-4.
  • (34) Kavdir I and Guyer DE. Apple sorting using artificial neural networks and spectral imaging. Trans.-Am. Soc. Agric. Eng., 2002; 45 (6): 1995-2006.
  • (35) Shahin MA, Tollner EW, McClendon RW and Arabnia HR. Apple classification based on surface bruises using image processing and neural networks. Trans.-Am. Soc. Agric. Eng., 2002; 45 (5): 1619-1628.
  • (36) Li and Zhu W. Apple Grading Method Based on Features Fusion of Size, Shape and Color. Procedia Engineering, 2011; 15: 2885-2891. DOI: http://dx.doi.org/10.1016/j.proeng.2011.08.543.
  • (37) Kurtulmus F, Lee W and Vardar A. Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network. Precision Agric, 2014; 15 (1): 57-79. DOI: http://dx.doi.org/10.1007/s11119-013-9323-8.
  • (38) Wang S, Zhang Y, Ji G, Yang J, Wu J and Wei L. Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization. Entropy, 2015; 17 (8): 5711-5728. DOI: http://dx.doi.org/10.3390/e17085711.
  • (39) Morimoto T, Purwanto W, Suzuki J and Hashimoto Y. Optimization of heat treatment for fruit during storage using neural networks and genetic algorithms. Computers of Electronics in Agriculture, 1997; 19 (1): 87-101. DOI: http://dx.doi.org/10.1016/S0168-1699(97)00037-9.
  • (40) Brosnan T and Sun DW. Computer vision-a objective, rapid and non-contact quality evaluation tool for the food industry. Journal Food Engineering, 2004; 61 (1): 3-16. DOI: http://dx.doi.org/10.1016/S0260-8774(03)00182-1.
  • (41) Hossein Nadian M, Rajiee S, Aghbashlo M, Hosseinpour S and Saeid Mohtasebi S. Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying, Food and Bioproducts Processing. Trans. Inst. Chem. Eng. Part C, 2015; 94: 263-274. DOI: http://dx.doi.org/10.1016/j.fbp.2014.03.005.
  • (42) Lin WC and Hill BD. Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers. Foreword, 2007; 87 (1): 137-143. DOI: http://dx.doi.org/10.4141/p05-231.
  • (43) Ochoa-Martínez CI and Ayala-Aponte AA. Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks. LWT - Food Sci. Technol., 2007; 40 (4): 638-645.
  • (44) Ehret DL, Hill BD, Raworth DA and Estergaard B. Artificial neural network modelling to predict cuticle cracking in greenhouse peppers and tomatoes. Computers of Electronics in Agriculture, 2008; 61 (2): 108-116. DOI: http://dx.doi.org/10.1016/j.compag.2007.09.011.
  • (45) Lin WC and Hill BD. Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse. Can. J. Plant Sci., 2008; 88 (3): 531-536. DOI: http://dx.doi.org/10.4141/cjps07165.
  • (46) Lin WC and Block GS. Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce. Algorithms, 2009; 2 (2): 623-637. DOI: http://dx.doi.org/10.3390/a2020623.
  • (47) Khoshhal A, Dakhel AA, Etemadi A and Zereshki S. Artificial Neural Network Modeling of Apple Drying Process. Journal Food Process Engineering, 2010; 33: 298-313. DOI: http://dx.doi.org/10.1111/j.1745-4530.2009.00435.x.
  • (48) Meisami-asl E and Rafiee S. Modeling of physical properties of apple slices (Golab variety) using artificial neural networks. Agric. Eng. Int. CIGR J., 2012; 14 (3): 175-178.
  • (49) Zarifneshat S, Rohani A, Ghassemzadeh HR, Sadeghi M, Ahmadi E and Zarifneshat M. Predictions of apple bruise volume using artificial neural network. Computers of Electronics in Agriculture, 2012; 82: 75-86. DOI: http://dx.doi.org/10.1016/j.compag.2011.12.015.
  • (50) Gatica G, Best S, Ceroni J and Lefranc G. Olive Fruits Recognition Using Neural Networks. Procedia Computer Science, 2013; 17: 412-419. DOI: http://dx.doi.org/10.1016/j.procs.2013.05.053.
  • (51) Soares JD, Pasqual M, Lacerda WS, Silva SO and Donato SLR. Utilization of artificial neural networks in the prediction of the bunches’ weight in banana plants. Scientia Horticulturae, 2013; 155: 24-29. DOI: http://dx.doi.org/10.1016/j.scienta.2013.01.026.
  • (52) Zuñiga A, Mora M, Oyarce M and Fredes C. Grape maturity estimation based on seed images and neural networks. Engineering Applications of Artificial Intelligence, 2014; 35: 95-104. DOI: http://dx.doi.org/10.1016/j.engappai.2014.06.007.
  • (53) Evin D, Hadad A, Martina M and Drozdowicz B. Predicción de estados de hipotensión empleando modelos ocultos de Markov. Fac. Ing., 2011; 20 (30): 55-63. DOI: http://dx.doi.org/10.19053/01211129.1418.
  • (54) Fuertes W, Rodas F and Toscano D. Evaluación de ataques UDP Flood utilizando escenarios virtuales como plataforma experimental. Fac. Ing., 2011; 20 (31): 37-53. DOI: http://dx.doi.org/10.19053/01211129.1422.

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