Data/hora: 23/09/2024 14:42:27

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	16/08/2017	
Data da última atualização: 	07/01/2020	
Autoria: 	DRURY, B.; VALVERDE-REBAZA, J.; MOURA, M. F.; LOPES, A. de A.	
Afiliação: 	BRETT DRURY, ICMC/USP; JORGE VALVERDE-REBAZA, ICMC/USP; MARIA FERNANDA MOURA, CNPTIA; ALNEU DE ANDRADE LOPES, ICMC/USP.	
Título: 	A survey of the applications of Bayesian networks in agriculture.	
Ano de publicação: 	2017	
Fonte/Imprenta: 	Engineering Applications of Artificial Intelligence, v. 65, p. 29-42, Oct. 2017.	
DOI: 	https://doi.org/10.1016/j.engappai.2017.07.003	
Idioma: 	Inglês	
Conteúdo: 	The application of machine learning to agriculture is currently experiencing a "surge of interest" from the academic community as well as practitioners from industry. This increased attention has produced a number of differing approaches that use varying machine learning frameworks. It is arguable that Bayesian Networks are particularly suited to agricultural research due to their ability to reason with incomplete information and incorporate new information. Bayesian Networks are currently underrepresented in the machine learning applied to agriculture research literature, and to date there are no survey papers that currently centralize the state of the art. The aim of this paper is rectify the lack of a survey paper in this area by providing a self-contained resource that will: centralize the current state of the art, document the historical progression of Bayesian Networks in agriculture and indicate possible future lines of research as well as providing an introduction to Bayesian Networks for researchers who are new to the area.	
Palavras-Chave: 	Bayesian inference; Bayesian networks; Computational agriculture; Machine learning.	
Thesagro: 	Agricultura.	
Thesaurus Nal: 	Agriculture; Bayesian theory.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (19252-1) - UPC - AP - DD -  -  -  - DIS