Data/hora: 13/06/2024 01:31:09

Provedor de dados: 	91	
País: 	Brazil	
Título: 	Artificial intelligence in seeding density optimization and yield simulation for oat	
Autores: 	Dornelles,Eldair F.; Kraisig,Adriana R.; Silva,José A. G. da; Sawicki,Sandro; Roos-Frantz,Fabricia; Carbonera,Roberto.	
Data: 	2018-03-01	
Ano: 	2018	
Palavras-chave: 	Avena sativa; Artificial neural networks; Genetic algorithms; Innovation.	
Resumo: 	ABSTRACT Artificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m-2) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were implemented in Java programming language, and the results obtained from this implementation were compared with traditional polynomial regression. The use of artificial intelligence through neural networks and genetic algorithms allows the efficient simulation of oat grain yield and better optimization of seeding density in comparison to polynomial regression, considering the main succession systems in southern Brazil.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000300183	
Editor: 	Departamento de Engenharia Agrícola  - UFCG	
Relação: 	10.1590/1807-1929/agriambi.v22n3p183-188	
Formato: 	text/html	
Fonte: 	Revista Brasileira de Engenharia Agrícola e Ambiental  v.22 n.3 2018	
Fonte: 	info:eu-repo/semantics/openAccess