Prediction of the yield of grains through artificial intelligence

Silva, Jose; Varela Izquierdo, Noel; Cabrera, Danelys; Lezama, Omar

doi:https://doi.org/10.1007/978-981-15-7907-3_34

dc.contributor.author	Silva, Jose	spa
dc.contributor.author	Varela Izquierdo, Noel	spa
dc.contributor.author	Cabrera, Danelys	spa
dc.contributor.author	Lezama, Omar	spa
dc.date.accessioned	2021-01-19T21:22:02Z
dc.date.available	2021-01-19T21:22:02Z
dc.date.issued	2021
dc.identifier.uri	https://hdl.handle.net/11323/7720	spa
dc.description.abstract	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.	spa
dc.format.mimetype	application/pdf	spa
dc.language.iso	eng
dc.publisher	Corporación Universidad de la Costa	spa
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	spa
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	spa
dc.source	Advances in Intelligent Systems and Computing	spa
dc.subject	Artificial intelligence	spa
dc.subject	Forage	spa
dc.subject	Grass	spa
dc.subject	Neural networks	spa
dc.title	Prediction of the yield of grains through artificial intelligence	spa
dc.type	Artículo de revista	spa
dc.source.url	https://link.springer.com/chapter/10.1007/978-981-15-7907-3_34	spa
dc.rights.accessrights	info:eu-repo/semantics/openAccess	spa
dc.identifier.doi	https://doi.org/10.1007/978-981-15-7907-3_34	spa
dc.identifier.instname	Corporación Universidad de la Costa	spa
dc.identifier.reponame	REDICUC - Repositorio CUC	spa
dc.identifier.repourl	https://repositorio.cuc.edu.co/	spa
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