Data/hora: 19/06/2024 18:40:30

Biblioteca(s): 	Embrapa Gado de Leite.	
Data corrente: 	20/05/2022	
Data da última atualização: 	15/07/2023	
Autoria: 	FRANCO, V. R.; HOTT, M. C.; ANDRADE, R. G.; GOLIATT, L.	
Afiliação: 	VICTOR REZENDE FRANCO, Universidade Federal de Juiz de Fora; MARCOS CICARINI HOTT, CNPGL; RICARDO GUIMARAES ANDRADE, CNPGL; LEONARDO GOLIATT, Universidade Federal de Juiz de Fora.	
Título: 	Hybrid machine learning methods combined with computer vision approaches to estimate biophysical parameters of pastures.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Evolutionary Intelligence, v. 16, p. 1271-1284, 2023.	
DOI: 	https://doi.org/10.1007/s12065-022-00736-9	
Idioma: 	Inglês	
Conteúdo: 	With population growth, the search for technologies that enable improvements in production respecting the environment and people?s health has become an essential point for society. In this context, this paper presents a study based on computer vision techniques and Machine Learning (ML) to extract information from pastures Panicum maximum cv. BRS Zuri to assist in the management and research on pasture conditions, possibilitando a obtenção de informações da. Computer vision aproaches are used to extract biophysical parameters from images acquired orthogonally from the canopy of vegetation. The extracted information serves as input for Machine Learning (ML) methods to predict pasture height and biomass. The contribution of this paper is developing a possible new solution compared to traditional methods in the large-scale study of plant biophysical parameters, which can be laborious and costly and sometimes depend on destructive harvesting. For this, three techniques were used: Support Vector Regression, Multi-Layer Perceptron (MLP), and Least Absolute Shrinkage and Selection. In addition, the Diferential Evolution technique was used to select the best model. Thirty independent runs of the Diferential Evolution technique were performed to assess the approach?s performance. The cross-validation method results show the MLP obtained the best results reaching an average of Coefcient of Determination (R2) equal 0.496 to estimate biomass and 0.656 to estimate the pasture height.	
Palavras-Chave: 	Diferential evolution; Evolução diferencial; Evolutionary model selection; Machine learning; Modelo evolutivo; Parâmetro biofísico; Rede neural; Visão computacional.	
Thesagro: 	Pastagem.	
Thesaurus Nal: 	Computer vision; Neural networks; Pastures.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Gado de Leite (CNPGL)	
Exemplares: 	CNPGL - (25648-1) - UPC - AP - DD -  -  -  - DIS