Prediction of the yield of grains through artificial intelligence

Grass turns out to be an appropriate food for cattle, mainly in tropical climate countries such as Latin American countries. This is due to the high number of species that can be used, the possibility of growing them year-round, the ability of the ruminant to use fibrous supplies and be an economic...

Full description

Autores:
Silva, Jose
Varela Izquierdo, Noel
Cabrera, Danelys
Lezama, Omar
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:11323/7720
Acceso en línea:
https://hdl.handle.net/11323/7720
https://doi.org/10.1007/978-981-15-7907-3_34
https://repositorio.cuc.edu.co/
Palabra clave:
Artificial intelligence
Forage
Grass
Neural networks
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_53e15eb349ddbeb135a285a046749dc9
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7720
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Prediction of the yield of grains through artificial intelligence
title Prediction of the yield of grains through artificial intelligence
spellingShingle Prediction of the yield of grains through artificial intelligence
Artificial intelligence
Forage
Grass
Neural networks
title_short Prediction of the yield of grains through artificial intelligence
title_full Prediction of the yield of grains through artificial intelligence
title_fullStr Prediction of the yield of grains through artificial intelligence
title_full_unstemmed Prediction of the yield of grains through artificial intelligence
title_sort Prediction of the yield of grains through artificial intelligence
dc.creator.fl_str_mv Silva, Jose
Varela Izquierdo, Noel
Cabrera, Danelys
Lezama, Omar
dc.contributor.author.spa.fl_str_mv Silva, Jose
Varela Izquierdo, Noel
Cabrera, Danelys
Lezama, Omar
dc.subject.spa.fl_str_mv Artificial intelligence
Forage
Grass
Neural networks
topic Artificial intelligence
Forage
Grass
Neural networks
description Grass turns out to be an appropriate food for cattle, mainly in tropical climate countries such as Latin American countries. This is due to the high number of species that can be used, the possibility of growing them year-round, the ability of the ruminant to use fibrous supplies and be an economic source (Sánchez et al., Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham, 2018, [1]). In this work, an application of neural networks was carried out in the forecasting of more accurate values of production and quality of grasslands.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-01-19T21:22:02Z
dc.date.available.none.fl_str_mv 2021-01-19T21:22:02Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7720
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-7907-3_34
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/
url https://hdl.handle.net/11323/7720
https://doi.org/10.1007/978-981-15-7907-3_34
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. 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
2. Aitkenhead MJ, Dalgetty IA, Mullins CE, Strachan NJ C (2003) Weed and crop discrimination using image analysis and artificial intelligence methods. Comput Electron Agric 39(3)
3. Bustos JR (2005) Inteligencia Artificial en el Sector Agropecuario.
4. Olivera Y, Machado R, Pozo PP (2006) Características botánicas y agronómicas de especies forrajeras importantes del género Brachiaria. Pastos y Forrajes 29(1)
5. 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
6. SenseFly (2014) El dron para la agricultura de precisión
7. Cruz MC, Rodríguez LC, Vi RG (2013) Evaluación agronómica de cuatro nuevas variedades de pastos. Revista de Producción Animal 25(1)
8. Erenturk K, Erenturk S, Tabil LG (2004) A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Comput Electron Agric 45(1–3)
9. Hernández D, Carballo M, 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)
10. Hernández RM, Pérez VR, Caraballo EAH (2012) Predicción del rendimiento de un cultivo de plátano mediante redes neuronales artificiales de regresión generalizada. Publicaciones en Ciencias y Tecnología 6(1)
11. López AM, Adolfo A, Guido JP, Ortega AC (2006) Software de Predicción de la Producción Forrajera.
12. Martín B, Molina AS (2001) Redes neuronales y sistemas borrosos. 2ªed. Alfaomega, España. Ra-Ma
13. Carrilho PHM, Alonso J, Santo LDT, Sampaio RA (2012) Comportamiento vegetativo y reproductivo de Brachiariadecumbensvc. Basilisk bajo diferentes niveles de sombra. Revista Cubana de Ciencia Agrícola 46(1)
14. Lezama OBP, Izquierdo NV, Fernández DP, Dorta RLG, Viloria A, Marín LR (2018) Models of multivariate regression for labor accidents in different production sectors: comparative study. In International conference on data mining and big data, vol 10942(1). Springer, Cham, pp 43–52
15. Suárez JA, Beatón PA, Escalona RF, Montero OP (2012) Energy, environment and development in Cuba. Renew Sustain Energy Rev 16(5):2724–2731
16. Sala S, Ciuffo B, Nijkamp P (2015) A systemic framework for sustainability assessment. Ecol Econ 119(1):314–325
17. Singh RK, Murty HR, Gupta SK, Dikshit AK (2009) An overview of sustainability assessment methodologies. Ecol Ind 9(2):189–212
18. Varela N, Fernandez D, Pineda O, Viloria A (2017) Selection of the best regression model to explain the variables that influence labor accident case electrical company. J Eng Appl Sci 12(1):2956–2962
19. Yao Z, Zheng X, Liu C, Lin S, Zuo Q, Butterbach-Bahl K (2017) Improving rice production sustainability by reducing water demand and greenhouse gas emissions with biodegradable films. Sci Rep 7(1):1–12
20. Suárez DFP, Román RMS (2016) Consumo de água em arroz irrigado por inundação em sistema de multiplas entradas. IRRIGA 1(1):78–95
21. Stuart AM, Pame ARP, Vithoonjit D, Viriyangkura L, Pithuncharurnlap J, Meesang N, Lampayan RM (2018) The application of best management practices increases the profitability and sustainability of rice farming in the central plains of Thailand. Field Crops Res 220(1):78–87
22. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran Coffee Sector Case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. In: ICSI 2018. Lecture notes in computer science, vol 10942(1). Springer Cham, pp 1–12
23. Bezerra BG, Da Silva BB, Bezerra JRC, Brandão ZN (2010) 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
24. Diaz-Balteiro L, González-Pachón J, Romero C (2009) Forest management with multiple criteria and multiple stakeholders: an application to two public forests in Spain. Scand J For Res 24(1):87–93
25. Hák T, Janoušková S, Moldan B (2016) Sustainable development goals: a need for relevant indicators. Ecol Ind 60(1):565–573
26. Lampayan RM, Rejesus RM, Singleton GR, Bouman BA (2015) Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Res 170(1):95–108
27. Delgado A, Blanco FM (2009) Modelo Multicriterio Para El Análisis De Alternativas De Financiamiento De Productores De Arroz En El Estado Portuguesa, Venezuela. AGROALIMENTARIA 28(1):35–48
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Advances in Intelligent Systems and Computing
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://link.springer.com/chapter/10.1007/978-981-15-7907-3_34
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/e22a2473-6950-422f-bf8a-c83af38d7550/download
https://repositorio.cuc.edu.co/bitstreams/5132550c-648a-4b7e-bd27-338914ebe8dd/download
https://repositorio.cuc.edu.co/bitstreams/01302841-8c8e-44b7-a864-57102ea0f572/download
https://repositorio.cuc.edu.co/bitstreams/6a83e979-a1f9-49cb-bfcb-b7f5537bee37/download
https://repositorio.cuc.edu.co/bitstreams/dd2b186e-caa1-4208-84f7-559c94ddc092/download
bitstream.checksum.fl_str_mv 4460e5956bc1d1639be9ae6146a50347
e30e9215131d99561d40d6b0abbe9bad
63356d93e851a887ef88923598835425
e234337fbc81cbb45eb545fb8adaf411
a5be442e3c63c34332953c314a4a48cd
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760676644847616
spelling Silva, JoseVarela Izquierdo, NoelCabrera, DanelysLezama, Omar2021-01-19T21:22:02Z2021-01-19T21:22:02Z2021https://hdl.handle.net/11323/7720https://doi.org/10.1007/978-981-15-7907-3_34Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Grass turns out to be an appropriate food for cattle, mainly in tropical climate countries such as Latin American countries. This is due to the high number of species that can be used, the possibility of growing them year-round, the ability of the ruminant to use fibrous supplies and be an economic source (Sánchez et al., Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham, 2018, [1]). In this work, an application of neural networks was carried out in the forecasting of more accurate values of production and quality of grasslands.Silva, JoseVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Cabrera, Danelys-will be generated-orcid-0000-0002-9486-9764-600Lezama, Omarapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7907-3_34Artificial intelligenceForageGrassNeural networksPrediction of the yield of grains 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/acceptedVersion1. 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, Cham2. Aitkenhead MJ, Dalgetty IA, Mullins CE, Strachan NJ C (2003) Weed and crop discrimination using image analysis and artificial intelligence methods. Comput Electron Agric 39(3)3. Bustos JR (2005) Inteligencia Artificial en el Sector Agropecuario.4. Olivera Y, Machado R, Pozo PP (2006) Características botánicas y agronómicas de especies forrajeras importantes del género Brachiaria. Pastos y Forrajes 29(1)5. 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, Cuba6. SenseFly (2014) El dron para la agricultura de precisión7. Cruz MC, Rodríguez LC, Vi RG (2013) Evaluación agronómica de cuatro nuevas variedades de pastos. Revista de Producción Animal 25(1)8. Erenturk K, Erenturk S, Tabil LG (2004) A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Comput Electron Agric 45(1–3)9. Hernández D, Carballo M, 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)10. Hernández RM, Pérez VR, Caraballo EAH (2012) Predicción del rendimiento de un cultivo de plátano mediante redes neuronales artificiales de regresión generalizada. Publicaciones en Ciencias y Tecnología 6(1)11. López AM, Adolfo A, Guido JP, Ortega AC (2006) Software de Predicción de la Producción Forrajera.12. Martín B, Molina AS (2001) Redes neuronales y sistemas borrosos. 2ªed. Alfaomega, España. Ra-Ma13. Carrilho PHM, Alonso J, Santo LDT, Sampaio RA (2012) Comportamiento vegetativo y reproductivo de Brachiariadecumbensvc. Basilisk bajo diferentes niveles de sombra. Revista Cubana de Ciencia Agrícola 46(1)14. Lezama OBP, Izquierdo NV, Fernández DP, Dorta RLG, Viloria A, Marín LR (2018) Models of multivariate regression for labor accidents in different production sectors: comparative study. In International conference on data mining and big data, vol 10942(1). Springer, Cham, pp 43–5215. Suárez JA, Beatón PA, Escalona RF, Montero OP (2012) Energy, environment and development in Cuba. Renew Sustain Energy Rev 16(5):2724–273116. Sala S, Ciuffo B, Nijkamp P (2015) A systemic framework for sustainability assessment. Ecol Econ 119(1):314–32517. Singh RK, Murty HR, Gupta SK, Dikshit AK (2009) An overview of sustainability assessment methodologies. Ecol Ind 9(2):189–21218. Varela N, Fernandez D, Pineda O, Viloria A (2017) Selection of the best regression model to explain the variables that influence labor accident case electrical company. J Eng Appl Sci 12(1):2956–296219. Yao Z, Zheng X, Liu C, Lin S, Zuo Q, Butterbach-Bahl K (2017) Improving rice production sustainability by reducing water demand and greenhouse gas emissions with biodegradable films. Sci Rep 7(1):1–1220. Suárez DFP, Román RMS (2016) Consumo de água em arroz irrigado por inundação em sistema de multiplas entradas. IRRIGA 1(1):78–9521. Stuart AM, Pame ARP, Vithoonjit D, Viriyangkura L, Pithuncharurnlap J, Meesang N, Lampayan RM (2018) The application of best management practices increases the profitability and sustainability of rice farming in the central plains of Thailand. Field Crops Res 220(1):78–8722. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran Coffee Sector Case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. In: ICSI 2018. Lecture notes in computer science, vol 10942(1). Springer Cham, pp 1–1223. Bezerra BG, Da Silva BB, Bezerra JRC, Brandão ZN (2010) Evapotranspiração real obtida através da relação entre o coeficiente dual de cultura da FAO-56 e o NDVI. Revista Brasileira De Meteorologia 25(3):404–41424. Diaz-Balteiro L, González-Pachón J, Romero C (2009) Forest management with multiple criteria and multiple stakeholders: an application to two public forests in Spain. Scand J For Res 24(1):87–9325. Hák T, Janoušková S, Moldan B (2016) Sustainable development goals: a need for relevant indicators. Ecol Ind 60(1):565–57326. Lampayan RM, Rejesus RM, Singleton GR, Bouman BA (2015) Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Res 170(1):95–10827. Delgado A, Blanco FM (2009) Modelo Multicriterio Para El Análisis De Alternativas De Financiamiento De Productores De Arroz En El Estado Portuguesa, Venezuela. AGROALIMENTARIA 28(1):35–48PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/e22a2473-6950-422f-bf8a-c83af38d7550/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/5132550c-648a-4b7e-bd27-338914ebe8dd/downloade30e9215131d99561d40d6b0abbe9badMD53ORIGINALPrediction of the yield of grains through artificial intelligence.pdfPrediction of the yield of grains through artificial intelligence.pdfapplication/pdf97668https://repositorio.cuc.edu.co/bitstreams/01302841-8c8e-44b7-a864-57102ea0f572/download63356d93e851a887ef88923598835425MD51THUMBNAILPrediction of the yield of grains through artificial intelligence.pdf.jpgPrediction of the yield of grains through artificial intelligence.pdf.jpgimage/jpeg25378https://repositorio.cuc.edu.co/bitstreams/6a83e979-a1f9-49cb-bfcb-b7f5537bee37/downloade234337fbc81cbb45eb545fb8adaf411MD54TEXTPrediction of the yield of grains through artificial intelligence.pdf.txtPrediction of the yield of grains through artificial intelligence.pdf.txttext/plain817https://repositorio.cuc.edu.co/bitstreams/dd2b186e-caa1-4208-84f7-559c94ddc092/downloada5be442e3c63c34332953c314a4a48cdMD5511323/7720oai:repositorio.cuc.edu.co:11323/77202024-09-16 16:43:48.512http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares