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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
17/10/2023 |
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Data da última atualização: |
17/10/2023 |
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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: |
RODRIGUES, L. de S.; CAIXETA FILHO, E.; SAKIYAMA, K.; SANTOS, M. F.; JANK, L.; CARROMEU, C.; SILVEIRA, E.; MATSUBARA, E. T.; MARCATO JUNIOR, J.; GONCALVES, W. N. |
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Afiliação: |
LUCAS DE SOUZA RODRIGUES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDMAR CAIXETA FILHO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; KENZO SAKIYAMA, UNIVERSIDADE DE SÃO PAULO; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; CAMILO CARROMEU, GTI; ELOISE SILVEIRA, CNPGC; EDSON TAKASHI MATSUBARA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; WESLEY NUNES GONCALVES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL. |
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Título: |
Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 211, 2023. |
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Páginas: |
14 p. |
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DOI: |
https://doi.org/10.1016/j.compag.2023.107957 |
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Idioma: |
Inglês |
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Conteúdo: |
Deep learning methods have become one of the fundamental blocks of high-throughput phenotyping using RGB imagery. In this study, we go beyond applying deep learning algorithms; we improve deep learning models using a multi-view fusion approach. The proposal dynamically merges information from two deep-learning models. We evaluate this approach to improve the estimation of total dry matter yield, leaf dry matter yield and total green matter yield of plots of Guineagrass, an important tropical forage species. The proposed approach, named Deep4Fusion fusion network, can be set to use two different deep learning models. The experimental results indicated that our approach improved the performance between 20% to 33% when compared with standard models reported in previous works, with a significant improvement (p-value < 0.05) for leaf dry matter and total dry matter yield. We believe that the flexibility of multi-view fusion in merging the predictions of several CNNs models through shared layers across the network has the potential to improve the results of many other single-view deep learning approaches. |
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Thesaurus NAL: |
Forage dryers; Forage grasses; Forage yield; Phenotype. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02021naa a2200301 a 4500
001 2157276
005 2023-10-17
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2023.107957$2DOI
100 1 $aRODRIGUES, L. de S.
245 $aDeep4Fusion$ba Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits.$h[electronic resource]
260 $c2023
300 $a14 p.
520 $aDeep learning methods have become one of the fundamental blocks of high-throughput phenotyping using RGB imagery. In this study, we go beyond applying deep learning algorithms; we improve deep learning models using a multi-view fusion approach. The proposal dynamically merges information from two deep-learning models. We evaluate this approach to improve the estimation of total dry matter yield, leaf dry matter yield and total green matter yield of plots of Guineagrass, an important tropical forage species. The proposed approach, named Deep4Fusion fusion network, can be set to use two different deep learning models. The experimental results indicated that our approach improved the performance between 20% to 33% when compared with standard models reported in previous works, with a significant improvement (p-value < 0.05) for leaf dry matter and total dry matter yield. We believe that the flexibility of multi-view fusion in merging the predictions of several CNNs models through shared layers across the network has the potential to improve the results of many other single-view deep learning approaches.
650 $aForage dryers
650 $aForage grasses
650 $aForage yield
650 $aPhenotype
700 1 $aCAIXETA FILHO, E.
700 1 $aSAKIYAMA, K.
700 1 $aSANTOS, M. F.
700 1 $aJANK, L.
700 1 $aCARROMEU, C.
700 1 $aSILVEIRA, E.
700 1 $aMATSUBARA, E. T.
700 1 $aMARCATO JUNIOR, J.
700 1 $aGONCALVES, W. N.
773 $tComputers and Electronics in Agriculture$gv. 211, 2023.
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