Evaluation the state of maturity of pineapple in its variety perolera using computer vision techniques
Keywords:
Computer vision system, Segmentation, Feature extraction, Statistical classification
Abstract
Computer vision systems allow identifying the physical characteristics and defects of a product in a non-invasive and reliable form.Due to these advantages, computer vision systems have gained acceptance in the food industry, since this industry requires a high demand for objectivity, consistency and efficiency in controlling the quality of the product, conditions that this systems can meet.This paper proposes a method for automatically evaluating the maturation state of the pineapple (AnanasComosus) perolera variety in postharvest usingcomputer vision system.The proposed evaluation procedure is implemented through an algorithm of color digital image processing based on the stages of preprocessing, segmentation, feature extraction and statistical classification.Algorithm used, images in the HSV color space, automatic segmentation using Otsu’s method, the first moment of the distributions of H and S planes as features, and the algorithm MBSAS (Modified Basic Sequential Algorithmic Scheme) for classification.319 images were used, of which 110 images were used in the process of training and 209 images were used in the evaluation process. The results of the evaluation procedure proposed in this paper were compared with the value judgment of three experts, showing that the proposed algorithm has an efficiency of close to 96.36% assessment.
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References
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[2] El país está preparado agrícolamente para el TLC, Revista Portafolio (Online), Octubre 11 de 2011, Disponible: http://www.portafolio.co/negocio
[3] R. D. Tillett, Image analysis for agricultural processes. En: Division Note DN 1585. Research Institute. 1990.
[4] L.G. Shapiro, G.C.Stockman, Computer Vision, Prentice Hall, 2001.
[5] Da-Wen Sun, Computer Vision Technology for Food Quality Evaluation, Ed. Elsevier, 2008.
[6] P. Chen, Z. Sun, A review of non-destructive methods for quality evaluation and sorting of agricultural products, Journal of Agricultural Engineering Research, Volume 49, May–August 1991, Pages 85-98.
[7] TadhgBrosnan, Da-Wen Sun, Inspection and grading of agricultural and food products by computer vision systems—a review, Computers and Electronics in Agriculture, Volume 36, Issues 2–3, November 2002, Pages 193-213.
[8] V Leemans, M-F Destain, A real-time grading method of apples based on features extracted from defects, Journal of Food Engineering, 61 (2004), pp. 83–89.
[9] JA Throop, DJ Aneshansley, WC Anger, DL Peterson, Quality evaluation of apples based on surface defects: development of an automated inspection system,Postharvest Biology and Technology, 36 (2005), pp. 281–290.
[10] D Unay, B Gosselin, Stem and calyx recognition on “Jonagold” apples by pattern recognition,Journal of food Engineering, 78 (2) (2007), pp. 597–605.
[11] J Blasco, S Cubero, R Arias, F Juste, E Moltó, On-line quality grading of mandarin segments by computer vision, XVIth CIGR World Congress: Agricultural Engineering for a Better World, Book of Abstracts, Bonn, Germany (2006), pp. 623– 624.
[12] J Blasco, N Aleixos, E Moltó, Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm, Journal of Food Engineering, 81 (3) (2007), pp. 535–543.
[13] N Hernandez, P Barreiro, M Ruiz-Altisent, J RuizCabello, ME Fernandez-Valle,Detection of seeds in citrus using MRI under motion conditions and improvement with motion correction, Concepts in Magnetic Resonance: Part B—Magnetic Resonance Engineering, 26B (1) (2005), pp. 81–92.
[14] M Nagata, PM Bato, M Mitarai, Q Cao, T Kitahara, Study on sorting system for strawberry using machine vision (part 1): development of software for determining the direction of strawberry (Akihime variety), Journal of Japan Society of Agricultural Machinery, 62 (1) (2000), pp. 100–110.
[15] R Díaz, M Blasco, J Blasco, E Moltó, Comparison of three algorithms in the classiication of table olives by means of computer vision, Journal of Food Engineering, 61 (2004), pp. 101–107.
[16] S Colmagro, G Collins, M Sedgley, Processing technology of the table olive, Horticultural Reviews, 25 (2001), pp. 235–260.
[17] JC Noordam, GW Otten, AJM Timmermans, BH van Zwol, High speed potato grading and quality inspection based on a color vision system, Proceedings of SPIE: The International Society for Optical Engineering, 3966 (2000), pp. 206–219.
[18] T Brosnan, D Sun, Improving quality inspection of food products by computer vision—a review, Journal of Food Engineering, 61 (2004), pp. 125– 135.
[19] C. Maya. Desarrollo de un algoritmo para la caracterización y clasiicación de granos de café empleando técnicas de visión artiicial. Manizales: U. Nacional de Manizales, 2001.
[20] J. Betancur, Segmentación de granos de café mediante métodos de crecimiento de regiones. Manizales: U Nacional de Manizales, 2002.
[21] Z. Sandoval, F. Prieto, Caracterización y clasiicación de café cereza por medio de visión artiicial, 2003.
[22] J. E. Hernández, Clasiicación de frutos de café según su etapa de maduración basada en redes neuronales artiiciales. Manizales: U. Nacional de Manizales, 2004.
[23] J. C. Mosquera, A. Sepúlveda, C, A. Isaza, Procesamiento de imágenes ópticas de frutos café en cereza por medio de iltros acusto-ópticos, Ingeniería y Desarrollo, Universidad del Norte, Numero 21, 2007.
[24] N.Otsu, A threshold selection method for gray level histograms, IEEE Trans. Syst. Man Cybern., 9, 62-6, 1979.
[25] S. Theodoridis, S. Koutroumbas, Pattern Recognition. Academic Press, San Diego, USA, 2008.
[26] G. Bradski, A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library,O’Reilly Media, 2008.
[27] González, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, 2008.
[28] Stricker, M. and Orengo, M., Similarity of color images, SPIE Conference on Storage and Retrieval for Image and Video Databases III. Vol. 2420, p. 381-392, 1995.
[29] Moore, A., Cross validation for detecting and preventing overitting, School of Computer Science, Carneigie Mellon University, 2001.
[30] Lizcano, S., Identiicación de las etapas de maduración de la piña perolera empleando técnicas de visión artiicial, Universidad Pontiicia Bolivariana, Seccional Bucaramanga, 2010.
Published
2014-11-26
How to Cite
Ángel Silva, L., & Lizcano, S. (2014). Evaluation the state of maturity of pineapple in its variety perolera using computer vision techniques. ITECKNE, 9(1), 31-41. https://doi.org/https://doi.org/10.15332/iteckne.v9i1.2744
Issue
Section
Research and Innovation Articles
