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| Registros recuperados : 326 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
20/05/2022 |
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Data da última atualização: |
15/07/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
FRANCO, V. R.; HOTT, M. C.; ANDRADE, R. G.; GOLIATT, L. |
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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. |
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Título: |
Hybrid machine learning methods combined with computer vision approaches to estimate biophysical parameters of pastures. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Evolutionary Intelligence, v. 16, p. 1271-1284, 2023. |
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DOI: |
https://doi.org/10.1007/s12065-022-00736-9 |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Diferential evolution; Evolução diferencial; Evolutionary model selection; Machine learning; Modelo evolutivo; Parâmetro biofísico; Rede neural; Visão computacional. |
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Thesagro: |
Pastagem. |
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Thesaurus NAL: |
Computer vision; Neural networks; Pastures. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02475naa a2200313 a 4500
001 2143284
005 2023-07-15
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s12065-022-00736-9$2DOI
100 1 $aFRANCO, V. R.
245 $aHybrid machine learning methods combined with computer vision approaches to estimate biophysical parameters of pastures.$h[electronic resource]
260 $c2023
520 $aWith 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.
650 $aComputer vision
650 $aNeural networks
650 $aPastures
650 $aPastagem
653 $aDiferential evolution
653 $aEvolução diferencial
653 $aEvolutionary model selection
653 $aMachine learning
653 $aModelo evolutivo
653 $aParâmetro biofísico
653 $aRede neural
653 $aVisão computacional
700 1 $aHOTT, M. C.
700 1 $aANDRADE, R. G.
700 1 $aGOLIATT, L.
773 $tEvolutionary Intelligence$gv. 16, p. 1271-1284, 2023.
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