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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. |
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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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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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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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Origem |
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Cutter |
Registro |
Volume |
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URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
24/11/2021 |
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Data da última atualização: |
03/12/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
YASSITEPE, J. E. de C. T.; SILVA, V. C. H. da; HERNANDES-LOPES, J.; DANTE, R. A.; GERHARDT, I. R.; FERNANDES, F. R.; SILVA, P. A. da; VIEIRA, L. R.; BONATTI, V.; ARRUDA, P. |
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Afiliação: |
JULIANA ERIKA DE C T YASSITEPE, CNPTIA; VIVIANE CRISTINA HEINZEN DA SILVA, UNICAMP; JOSÉ HERNANDES-LOPES, Colaborador CNPTIA, UNICAMP; RICARDO AUGUSTO DANTE, CNPTIA; ISABEL RODRIGUES GERHARDT, CNPTIA; FERNANDA RAUSCH FERNANDES, CNPTIA; PRISCILA ALVES DA SILVA, UNICAMP; LETICIA RIOS VIEIRA, UNICAMP; VANESSA BONATTI, UNICAMP; PAULO ARRUDA, UNICAMP. |
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Título: |
Maize transformation: from plant material to the release of genetically modified and edited varieties. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v. 12, p. 1-17, Oct. 2021. |
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DOI: |
https://doi.org/10.3389/fpls.2021.766702 |
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Idioma: |
Inglês |
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Notas: |
Article 766702. |
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Conteúdo: |
Over the past decades, advances in plant biotechnology have allowed the development of genetically modified maize varieties that have significantly impacted agricultural management and improved the grain yield worldwide. To date, genetically modified varieties represent 30% of the world´s maize cultivated area and incorporate traits such as herbicide, insect and disease resistance, abiotic stress tolerance, high yield, and improved nutritional quality. Maize transformation, which is a prerequisite for genetically modified maize development, is no longer a major bottleneck. Protocols using morphogenic regulators have evolved significantly towards increasing transformation frequency and genotype independence. Emerging technologies using either stable or transient expression and tissue culture-independent methods, such as direct genome editing using RNA-guided endonuclease system as an in vivo desired-target mutator, simultaneous double haploid production and editing/haploid-inducer-mediated genome editing, and pollen transformation, are expected to lead significant progress in maize biotechnology. This review summarises the significant advances in maize transformation protocols, technologies, and applications and discusses the current status, including a pipeline for trait development and regulatory issues related to current and future genetically modified and genetically edited maize varieties. |
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Palavras-Chave: |
Gene editing; Genetic modification; Maize; Modificação genética; Morphogenic regulator-mediated transformation; Plant biotechnology; Plant transformation; Transformação de planta. |
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Thesagro: |
Biotecnologia; Genética; Genética Vegetal; Milho. |
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Thesaurus NAL: |
Biotechnology; Genetics; Plant genetics. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228048/1/AP-Maize-Transformation-2021.pdf
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Marc: |
LEADER 02711naa a2200433 a 4500
001 2136495
005 2021-12-03
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fpls.2021.766702$2DOI
100 1 $aYASSITEPE, J. E. de C. T.
245 $aMaize transformation$bfrom plant material to the release of genetically modified and edited varieties.$h[electronic resource]
260 $c2021
500 $aArticle 766702.
520 $aOver the past decades, advances in plant biotechnology have allowed the development of genetically modified maize varieties that have significantly impacted agricultural management and improved the grain yield worldwide. To date, genetically modified varieties represent 30% of the world´s maize cultivated area and incorporate traits such as herbicide, insect and disease resistance, abiotic stress tolerance, high yield, and improved nutritional quality. Maize transformation, which is a prerequisite for genetically modified maize development, is no longer a major bottleneck. Protocols using morphogenic regulators have evolved significantly towards increasing transformation frequency and genotype independence. Emerging technologies using either stable or transient expression and tissue culture-independent methods, such as direct genome editing using RNA-guided endonuclease system as an in vivo desired-target mutator, simultaneous double haploid production and editing/haploid-inducer-mediated genome editing, and pollen transformation, are expected to lead significant progress in maize biotechnology. This review summarises the significant advances in maize transformation protocols, technologies, and applications and discusses the current status, including a pipeline for trait development and regulatory issues related to current and future genetically modified and genetically edited maize varieties.
650 $aBiotechnology
650 $aGenetics
650 $aPlant genetics
650 $aBiotecnologia
650 $aGenética
650 $aGenética Vegetal
650 $aMilho
653 $aGene editing
653 $aGenetic modification
653 $aMaize
653 $aModificação genética
653 $aMorphogenic regulator-mediated transformation
653 $aPlant biotechnology
653 $aPlant transformation
653 $aTransformação de planta
700 1 $aSILVA, V. C. H. da
700 1 $aHERNANDES-LOPES, J.
700 1 $aDANTE, R. A.
700 1 $aGERHARDT, I. R.
700 1 $aFERNANDES, F. R.
700 1 $aSILVA, P. A. da
700 1 $aVIEIRA, L. R.
700 1 $aBONATTI, V.
700 1 $aARRUDA, P.
773 $tFrontiers in Plant Science$gv. 12, p. 1-17, Oct. 2021.
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Embrapa Agricultura Digital (CNPTIA) |
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