Neural networks for tea leaf classification

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually an...

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Autores:
Silva, Jesús
H, H
Niebles Núñez, William
Ruiz-Lazaro, Alex
Varela, Noel
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6236
Acceso en línea:
https://hdl.handle.net/11323/6236
https://repositorio.cuc.edu.co/
Palabra clave:
Raw material
Intelligence techniques (IA)
Neural networks
Tea leaf
Rights
openAccess
License
CC0 1.0 Universal
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repository_id_str
dc.title.spa.fl_str_mv Neural networks for tea leaf classification
title Neural networks for tea leaf classification
spellingShingle Neural networks for tea leaf classification
Raw material
Intelligence techniques (IA)
Neural networks
Tea leaf
title_short Neural networks for tea leaf classification
title_full Neural networks for tea leaf classification
title_fullStr Neural networks for tea leaf classification
title_full_unstemmed Neural networks for tea leaf classification
title_sort Neural networks for tea leaf classification
dc.creator.fl_str_mv Silva, Jesús
H, H
Niebles Núñez, William
Ruiz-Lazaro, Alex
Varela, Noel
dc.contributor.author.spa.fl_str_mv Silva, Jesús
H, H
Niebles Núñez, William
Ruiz-Lazaro, Alex
Varela, Noel
dc.subject.spa.fl_str_mv Raw material
Intelligence techniques (IA)
Neural networks
Tea leaf
topic Raw material
Intelligence techniques (IA)
Neural networks
Tea leaf
description The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-23T16:31:38Z
dc.date.available.none.fl_str_mv 2020-04-23T16:31:38Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 1742-6588
1742-6596
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/6236
dc.identifier.doi.spa.fl_str_mv doi:10.1088/1742-6596/1432/1/01207
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1742-6588
1742-6596
doi:10.1088/1742-6596/1432/1/01207
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/6236
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Ministerio de Ciencia, Tecnología e Innovación Productiva, Profecyt, Agencia de promoción Científica y Tecnológica, Unión Industrial Argentina. Té en Misiones: Debilidades y Desafíos tecnológicos del sector productivo. Buenos Aires, Argentina (2012).
[2] Zamora, K., Castro, L., Wang, A., Arauz, L. F., & Uribe, L. (2017). Potential use of vermicompost leachates and tea in the control of the American leaf spot of coffee Mycena citricolor. Agronomía Costarricense, 41(1), 33-51.
[3] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.
[4] Hariri S, and M. Parashar.Tools and Enviroments for Parallel and Distributed Computing. John Wiley & Sons. ISBN 0-471-33288-7, pag 229, 2014.
[5] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016).
[6] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371.
[7] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009.
[8] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.
[9] Venugopal K, K.G. Srinivasa and L. M. Patnaik. Soft Computing for Data Mining Applications. Springer Berlin Heidelberg: Springer-Verlag. ISBN 978-3-642-00192-5, pp 354, 2009.
[10] Brdar S., Culibrk D., Marinkovic B., Crnobarac J., Crnojevic V. Support Vector Machines with Features Contribution Analysis for Agricultural Yield Prediction, Second International Workshop on Sensing Technologies in Agriculture, Forestry and Environment, 43-47, 2011
[11] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014.
[12] R. Putha, L. Quadrifoglio, and E. Zechman. Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Computer‐ Aided Civil and Infrastructure Engineering, 27(1), 14-28, 2012.
[13] D. Teodorović, and M. Dell’Orco. Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology, 31(2), 135-152, 2008.
[14] Amelec, V., & Alexander, P. (2015). Improvements in the automatic distribution process of finished product for pet food category in multinational company. Advanced Science Letters, 21(5), 1419-1421.
[15] Salinas-Piélago, J. (2016). Revisión sobre el uso del mate de hoja de coca en la prevención del mal agudo de montaña. Revista de Neuro-Psiquiatría, 79(3), 166-168.
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institution Corporación Universidad de la Costa
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spelling Silva, Jesúse17281d02925301aa71681ad0d7b3e03H, Ha50768e271729098aea43bcf7439850cNiebles Núñez, William16e7911c93826189c8c01f9f8591e9d4Ruiz-Lazaro, Alex879d8688f809a555872c4868158c44cdVarela, Noel544417e3ea23421c46114ee4f01f436a2020-04-23T16:31:38Z2020-04-23T16:31:38Z20201742-65881742-6596https://hdl.handle.net/11323/6236doi:10.1088/1742-6596/1432/1/01207Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.engJournal of Physics: Conference SeriesCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Raw materialIntelligence techniques (IA)Neural networksTea leafNeural networks for tea leaf classificationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Ministerio de Ciencia, Tecnología e Innovación Productiva, Profecyt, Agencia de promoción Científica y Tecnológica, Unión Industrial Argentina. Té en Misiones: Debilidades y Desafíos tecnológicos del sector productivo. Buenos Aires, Argentina (2012).[2] Zamora, K., Castro, L., Wang, A., Arauz, L. F., & Uribe, L. (2017). Potential use of vermicompost leachates and tea in the control of the American leaf spot of coffee Mycena citricolor. Agronomía Costarricense, 41(1), 33-51.[3] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.[4] Hariri S, and M. Parashar.Tools and Enviroments for Parallel and Distributed Computing. John Wiley & Sons. ISBN 0-471-33288-7, pag 229, 2014.[5] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016).[6] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371.[7] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009.[8] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.[9] Venugopal K, K.G. Srinivasa and L. M. Patnaik. Soft Computing for Data Mining Applications. Springer Berlin Heidelberg: Springer-Verlag. ISBN 978-3-642-00192-5, pp 354, 2009.[10] Brdar S., Culibrk D., Marinkovic B., Crnobarac J., Crnojevic V. Support Vector Machines with Features Contribution Analysis for Agricultural Yield Prediction, Second International Workshop on Sensing Technologies in Agriculture, Forestry and Environment, 43-47, 2011[11] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014.[12] R. Putha, L. Quadrifoglio, and E. Zechman. Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Computer‐ Aided Civil and Infrastructure Engineering, 27(1), 14-28, 2012.[13] D. Teodorović, and M. Dell’Orco. Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology, 31(2), 135-152, 2008.[14] Amelec, V., & Alexander, P. (2015). Improvements in the automatic distribution process of finished product for pet food category in multinational company. Advanced Science Letters, 21(5), 1419-1421.[15] Salinas-Piélago, J. (2016). Revisión sobre el uso del mate de hoja de coca en la prevención del mal agudo de montaña. 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