Prediction of the corn grains yield through artificial intelligence
Currently, the determination of the quality of the cereals is done manually by grain classifier experts prior to the marketing stage. In this paper we present a web software tool that allows determining the quality level of a corn sample automatically from an image of it. Image processing algorithms...
- Autores:
-
Varela, Noel
Silva, Jesus
Bonerge Pineda, Omar
Cabrera, Danelys
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8785
- Acceso en línea:
- https://hdl.handle.net/11323/8785
https://doi.org/10.1016/j.procs.2020.03.080
https://repositorio.cuc.edu.co/
- Palabra clave:
- Cereal quality
Image processing
Web tool
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Prediction of the corn grains yield through artificial intelligence |
| title |
Prediction of the corn grains yield through artificial intelligence |
| spellingShingle |
Prediction of the corn grains yield through artificial intelligence Cereal quality Image processing Web tool |
| title_short |
Prediction of the corn grains yield through artificial intelligence |
| title_full |
Prediction of the corn grains yield through artificial intelligence |
| title_fullStr |
Prediction of the corn grains yield through artificial intelligence |
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Prediction of the corn grains yield through artificial intelligence |
| title_sort |
Prediction of the corn grains yield through artificial intelligence |
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Varela, Noel Silva, Jesus Bonerge Pineda, Omar Cabrera, Danelys |
| dc.contributor.author.spa.fl_str_mv |
Varela, Noel Silva, Jesus Bonerge Pineda, Omar Cabrera, Danelys |
| dc.subject.spa.fl_str_mv |
Cereal quality Image processing Web tool |
| topic |
Cereal quality Image processing Web tool |
| description |
Currently, the determination of the quality of the cereals is done manually by grain classifier experts prior to the marketing stage. In this paper we present a web software tool that allows determining the quality level of a corn sample automatically from an image of it. Image processing algorithms were implemented to correct distortions caused mainly by the capture process. The K-Means classification algorithm was used and a function was developed to calculate the hectolitre weight in relation to the sample area. The results obtained by the application for grades 1 and 2, are close to those measured by the experts. However, those for grade 3 have not been similar since the subsamples selected were not representative. |
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2021 |
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eng |
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[1] Ramírez, J. (2010). Rendimiento y calidad de cinco gramíneas en el Valle del Cauto. Tesis en opción al grado de Doctor en Ciencias Veterinarias. Instituto de Ciencias Agrícolas. La Habana. Cuba. [2] SenseFly. (2014). El dron para la agricultura de precisión. Disponible en: https://www.sensefly.com/fileadmin/user_upload/sensefly/documents/bro chures/eBee_Ag_es.pdf [Consultado el 29 de septiembre del 2015]. [3] Cruz, M. C., Rodríguez, L. C. y Vi, R. G. (2013). Evaluación agronómica de cuatro nuevas variedades de pastos. Revista de Producción Animal, 25(1). [4] Erenturk, K., Erenturk, S. yTabil, L. G. (2004). A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Computers and Electronics in Agriculture, 45(1-3). [5] Hernández, D., Carballo, M. y Reyes, F. (2000). Reflexiones sobre el uso de los pastos en la producción sostenible de leche y carne de res en el trópico. Pastos y Forrajes,23(4). [6] Hernández, R. M., Pérez, V. R. y Caraballo, E. A. H. (2012). Predicción del rendimiento de un cultivo de plátano mediante redes neuronales artificiales de regresión generalizada. Publicaciones en Ciencias y Tecnología, 6(1). [7] López, A. M., Adolfo, A., Guido, J. P. y Ortega, A. C. (2006). Software de Predicción de la Producción Forrajera. Disponible en: http://www.unne.edu.ar/unnevieja/Web/cyt/cyt/2001/8-Exactas/E-002.pdf [Consultado el 29 de septiembre del 2015]. [8] Martín, B. yMolina, A. S. (2001). Redes neuronales y sistemas borrosos. 2ªed.España: Alfaomega. Ra-Ma. [9] Lezama, O. B. P., Izquierdo, N. V., Fernández, D. P., Dorta, R. L. G., Viloria, A., Marín, L. R.: Models of Multivariate Regression for Labor Accidents in Different Production Sectors: Comparative Study. In International Conference on Data Mining and Big Data, Springer, Cham, 10942 (1), 43-52 (2018). [10] Suárez, J. A., Beatón, P. A., Escalona, R. F., Montero, O. P.: Energy, environment and development in Cuba. Renewable and Sustainable Energy Reviews, 16(5), 2724-2731 (2012). [11] Sala, S., Ciuffo, B., Nijkamp, P.: A systemic framework for sustainability assessment. Ecological Economics 119 (1), 314-325 (2015). [12] Singh, R. K., Murty, H. R., Gupta, S. K., Dikshit, A. K.: An overview of sustainability assessment methodologies. Ecological indicators 9(2), 189-212 (2009). [13] Sánchez L., Vásquez C., Viloria A., Rodríguez Potes L. (2018) Greenhouse Gases Emissions and Electric Power Generation in Latin American Countries in the Period 2006–2013. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [14] Aitkenhead, M. J., Dalgetty, I. A., Mullins, C. E. y Strachan, N. J. C. (2003). Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture, 39(3). [15] Varela, N., Fernandez, D., Pineda, O., Viloria, A.: Selection of the best regression model to explain the variables that influence labor accident case electrical company. Journal of Engineering and Applied Sciences 12 (1), 2956-2962 (2017). [16] Olivera, Y., Machado, R. y Pozo, P. P. (2006). Características botánicas y agronómicas de especies forrajeras importantes del género Brachiaria. Pastos y Forrajes, 29(1). [17] Bezerra, B. G.; Da Silva, B. B., Bezerra, J. R. C., Brandão, Z. N.: Evapotranspiração real obtida através da relação entre o coeficiente dual de cultura da FAO-56 e o NDVI. Revista Brasileira de Meteorologia 25 (3), 404 – 414 (2010). [18] Diaz-Balteiro, L., González-Pachón, J., Romero, C.: Forest management with multiple criteria and multiple stakeholders: An application to two public forests in Spain. Scandinavian Journal of Forest Research 24(1), 87-93 (2009). [19] Hák, T., Janoušková, S., Moldan, B.: Sustainable Development Goals: A need for relevant indicators. Ecological Indicators 60 (1), 565-573 (2016). [20] Lampayan, R. M., Rejesus, R. M., Singleton, G. R., Bouman, B. A.: Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Research 170 (1), 95-108 (2015). [21] Delgado. A., Blanco, F. M.: Modelo Multicriterio Para El Análisis De Alternativas De Financiamiento De Productores De Arroz En El Estado Portuguesa, Venezuela. AGROALIMENTARIA 28 (1), 35-48 (2009). |
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Varela, NoelSilva, JesusBonerge Pineda, OmarCabrera, Danelys2021-10-08T20:49:28Z2021-10-08T20:49:28Z20211877-0509https://hdl.handle.net/11323/8785https://doi.org/10.1016/j.procs.2020.03.080Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Currently, the determination of the quality of the cereals is done manually by grain classifier experts prior to the marketing stage. In this paper we present a web software tool that allows determining the quality level of a corn sample automatically from an image of it. Image processing algorithms were implemented to correct distortions caused mainly by the capture process. The K-Means classification algorithm was used and a function was developed to calculate the hectolitre weight in relation to the sample area. The results obtained by the application for grades 1 and 2, are close to those measured by the experts. However, those for grade 3 have not been similar since the subsamples selected were not representative.Varela, NoelSilva, JesusBonerge Pineda, OmarCabrera, Danelysapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305172#!Cereal qualityImage processingWeb toolPrediction of the corn grains yield through artificial intelligenceArtículo 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] Ramírez, J. (2010). Rendimiento y calidad de cinco gramíneas en el Valle del Cauto. Tesis en opción al grado de Doctor en Ciencias Veterinarias. Instituto de Ciencias Agrícolas. La Habana. Cuba.[2] SenseFly. (2014). El dron para la agricultura de precisión. Disponible en: https://www.sensefly.com/fileadmin/user_upload/sensefly/documents/bro chures/eBee_Ag_es.pdf [Consultado el 29 de septiembre del 2015].[3] Cruz, M. C., Rodríguez, L. C. y Vi, R. G. (2013). Evaluación agronómica de cuatro nuevas variedades de pastos. Revista de Producción Animal, 25(1).[4] Erenturk, K., Erenturk, S. yTabil, L. G. (2004). A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Computers and Electronics in Agriculture, 45(1-3).[5] Hernández, D., Carballo, M. y Reyes, F. (2000). Reflexiones sobre el uso de los pastos en la producción sostenible de leche y carne de res en el trópico. Pastos y Forrajes,23(4).[6] Hernández, R. M., Pérez, V. R. y Caraballo, E. A. H. (2012). Predicción del rendimiento de un cultivo de plátano mediante redes neuronales artificiales de regresión generalizada. Publicaciones en Ciencias y Tecnología, 6(1).[7] López, A. M., Adolfo, A., Guido, J. P. y Ortega, A. C. (2006). Software de Predicción de la Producción Forrajera. Disponible en: http://www.unne.edu.ar/unnevieja/Web/cyt/cyt/2001/8-Exactas/E-002.pdf [Consultado el 29 de septiembre del 2015].[8] Martín, B. yMolina, A. S. (2001). Redes neuronales y sistemas borrosos. 2ªed.España: Alfaomega. Ra-Ma.[9] Lezama, O. B. P., Izquierdo, N. V., Fernández, D. P., Dorta, R. L. G., Viloria, A., Marín, L. R.: Models of Multivariate Regression for Labor Accidents in Different Production Sectors: Comparative Study. In International Conference on Data Mining and Big Data, Springer, Cham, 10942 (1), 43-52 (2018).[10] Suárez, J. A., Beatón, P. A., Escalona, R. F., Montero, O. P.: Energy, environment and development in Cuba. Renewable and Sustainable Energy Reviews, 16(5), 2724-2731 (2012).[11] Sala, S., Ciuffo, B., Nijkamp, P.: A systemic framework for sustainability assessment. Ecological Economics 119 (1), 314-325 (2015).[12] Singh, R. K., Murty, H. R., Gupta, S. K., Dikshit, A. K.: An overview of sustainability assessment methodologies. Ecological indicators 9(2), 189-212 (2009).[13] Sánchez L., Vásquez C., Viloria A., Rodríguez Potes L. (2018) Greenhouse Gases Emissions and Electric Power Generation in Latin American Countries in the Period 2006–2013. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham[14] Aitkenhead, M. J., Dalgetty, I. A., Mullins, C. E. y Strachan, N. J. C. (2003). Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture, 39(3).[15] Varela, N., Fernandez, D., Pineda, O., Viloria, A.: Selection of the best regression model to explain the variables that influence labor accident case electrical company. Journal of Engineering and Applied Sciences 12 (1), 2956-2962 (2017).[16] Olivera, Y., Machado, R. y Pozo, P. P. (2006). Características botánicas y agronómicas de especies forrajeras importantes del género Brachiaria. Pastos y Forrajes, 29(1).[17] Bezerra, B. G.; Da Silva, B. B., Bezerra, J. R. C., Brandão, Z. N.: Evapotranspiração real obtida através da relação entre o coeficiente dual de cultura da FAO-56 e o NDVI. Revista Brasileira de Meteorologia 25 (3), 404 – 414 (2010).[18] Diaz-Balteiro, L., González-Pachón, J., Romero, C.: Forest management with multiple criteria and multiple stakeholders: An application to two public forests in Spain. Scandinavian Journal of Forest Research 24(1), 87-93 (2009).[19] Hák, T., Janoušková, S., Moldan, B.: Sustainable Development Goals: A need for relevant indicators. Ecological Indicators 60 (1), 565-573 (2016).[20] Lampayan, R. M., Rejesus, R. M., Singleton, G. R., Bouman, B. A.: Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Research 170 (1), 95-108 (2015).[21] Delgado. A., Blanco, F. M.: Modelo Multicriterio Para El Análisis De Alternativas De Financiamiento De Productores De Arroz En El Estado Portuguesa, Venezuela. AGROALIMENTARIA 28 (1), 35-48 (2009).PublicationORIGINALPrediction of the Corn Grains Yield through Artificial Intelligence.pdfPrediction of the Corn Grains Yield through Artificial Intelligence.pdfapplication/pdf1478055https://repositorio.cuc.edu.co/bitstreams/9d60fbb9-81fd-45b7-ba2d-6386765bd6f4/download29a515f84b57ec4feca3fc8932b59d41MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/4621fc04-841a-44b5-b0cd-d351af186f77/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/c05e7da5-b8dd-43f2-adbf-a850bd8963d8/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILPrediction of the Corn Grains Yield through Artificial Intelligence.pdf.jpgPrediction of the Corn Grains Yield through Artificial 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