From real to virtual eyes
a classification almost 4.0 tomatoes.
DOI:
https://doi.org/10.31686/ijier.vol7.iss11.1994Keywords:
Agricultural machinery, sensing, technologies 3.0, CIELABAbstract
The change in the color of the vegetables peel during the ripening process is the main criterion used by the consumer to define the fruit ripeness degree and for the producer to determine the best time of harvest. This relationship between bark coloration and different maturation stages allows the producer to establish harvest planning and extend shelf life. Students and faculty of the Biosystems Engineering course at São Paulo State University (UNESP), Tupã Campus, designed and developed a low-cost prototype of a fruit sorting belt, specifically for cherry group tomatoes. In the future, improvement in machinery with the insertion of new devices such as cameras, embedded system, combines sensor technology 3.0 with machine learning 4.0.
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[2] C. Andreuccetti, M. D. Ferreira, A. S. D. Gutierrez, M. Tavares, “Classificação e padronização dos tomates cv. Carmem e Débora dentro da CEAGESP – SP”, Jaboticabal, v.24, n.3, 2004. pp.790-798.
[3] C. J. Du, Sun, D. W, “Recent developments in the applications of image processing techniques for food quality evaluation”, Trends in Food Science & Technology, v. 15, n.5, 2004, pp.230 -249.
[4] C. M. Stinco, F. J. Rodríguez-Pulido, M. L. Escudero-Gilete, et al., “Lycopene isomers in fresh and processed tomato products: correlations with instrumental color measurements by digital image analysis and spectroradiometry”, Food Research International, Ottawa, v.50, n.1, 2013, pp.111-120.
[5] Companhia de entrepostos e armazéns gerais de São Paulo, “Centro de Qualidade em Horticultura”, Programa Brasileiro para Modernização da Horticultura: Normas de classificação de tomates, São Paulo, 2003.
[6] F. A. R. Filgueira, “Novo manual de Olericultura: agrotecnologia moderna na produção e comercialização de hortaliças”, Viçosa-MG: UFV, 2008.
[7] Fao, “Food and Agriculture Organization of the United Nations”, Disponível em: < http://www.fao.org/statistics/en/>, Acesso em: out.2019
[8] G. Piatetsky-Shapiro, et al., “Advances in knowledge discovery and data mining”, Menlo Park: AAAI press, 1996.
[9] Globo rural, “Produção de tomate será 1,2 % inferior à prevista em janeiro”, disponível em <https://revistagloborural.globo.com/Noticias/Agricultura/Hortifruti/noticia/2018/03/producao-de-tomate-sera-12-inferior-prevista-em-janeiro.html>, Acesso em: mar.2018
[10] I. H. Witten, et al., “Data Mining: Practical machine learning tools and techniques”, Morgan Kaufmann, 2016.
[11] J. Carneiro, “Análise da reflectância de argamassas”, Relatório Técnico, Braga: Universidade do Minho, 2010.
[12] K. L.Yam, S. E. Papadakis, “A simple digital imaging method for measuring and analyzing color of food surfaces”, Journal of Food Engineering, v. 61, n.1, 2004, pp. 137-142.
[13] L. Ahmed, A. B. Martin-Diana, RICO, D. Rico, et al., “Quality and nutritional status of fresh-cut tomato as affected by spraying of delactosed whey permeate compared to industrial washing treatment”, New York, v. 5, n. 8, 2011, pp. 1-12.
[14] L. M. Régula, “Padrões virtuais e tolerâncias colorimétricas no controle instrumental das cores”, Dissertação (Mestrado em Metrologia para a Qualidade Industrial)- Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, 2004.
[15] P. B. Pathare, U. L., F. A. AL-Said, “Colour measurement and analysis in fresh and processed foods: a review”, Food and bioprocess technology, v. 6, n. 1, 2013, pp. 36-60.
[16] R. D. Tillett, “Image analysis for agricultural processes: a review of potential opportunities”, Journal of agricultural Engineering research, v. 50, 1991, pp. 247-258.
[17] R. Perveen, et al., “Tomato (Solanum Lycopersicum) carotenoids and lycopenes chemistry; metabolism, absorption, nutrition, and allied health claims-A comprehensive review”, Critical Reviews in Food Science and Nutrition, 55 (7), 2015, pp. 919-929
[18] R. S. Castro, J. M. O. Barth, J. V. Flores, A. T. Salton, “Modelagem e implementação de um sistema ballandplate controlado por servo-visão”, XI Simpósio Brasileiro de Automação Inteligente, Fortaleza, 2013.
[19] T. Brosnan, D. W. Sun, “Improving quality inspection of food products by computer vision—a review”, Journal of Food Engineering, v. 61, n. 1, 2004, pp. 3-16.
[20] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, “From data mining to knowledge discovery in databases”, AI Magazine, v. 17, n. 3, 1996, pp. 37-37.
[21] Z. Zhang, L. Liu, M. Zhang, et al., “Effect of carbon dioxide enrichment on healthpromoting compounds and organoleptic properties of tomato fruits grown in greenhouse”, Food Chemistry, Barking, v.153, 2014, pp.157-163.
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2019-11-01
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Copyright (c) 2019 Mariana Matulovic, Cleber Alexandre de Amorim, Angela Vacaro de Souza, Paulo Sérgio Barbosa dos Santos, Geovane Yuji Aparecido Sakata, Guilherme Pulizzi Costa, Douglas Cardozo de Almeida, Jéssica Marques de Mello
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How to Cite
Matulovic, M., Alexandre de Amorim, C., Vacaro de Souza, A., Sérgio Barbosa dos Santos, P., Yuji Aparecido Sakata, G., Pulizzi Costa, G., Cardozo de Almeida, D., & Marques de Mello, J. (2019). From real to virtual eyes: a classification almost 4.0 tomatoes. International Journal for Innovation Education and Research, 7(11), 1225-1234. https://doi.org/10.31686/ijier.vol7.iss11.1994
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