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...
- 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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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. |
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2020 |
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2020-04-23T16:31:38Z |
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2020-04-23T16:31:38Z |
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2020 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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1742-6588 1742-6596 |
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https://hdl.handle.net/11323/6236 |
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doi:10.1088/1742-6596/1432/1/01207 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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1742-6588 1742-6596 doi:10.1088/1742-6596/1432/1/01207 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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| dc.language.iso.none.fl_str_mv |
eng |
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eng |
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[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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Silva, JesúsH, HNiebles Núñez, WilliamRuiz-Lazaro, AlexVarela, Noel2020-04-23T16:31:38Z2020-04-23T16:31:38Z20201742-65881742-6596https://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.Silva, JesúsHernandez Palma, Hugo Gaspar-will be generated-orcid-0000-0002-3873-0530-600Niebles Núñez, WilliamRuiz-Lazaro, AlexVarela, NoelengJournal 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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