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| 2. |  | SONE, J. S.; OLIVEIRA, P. T. S. de; ZAMBONI, P. A. P.; VIEIRA, N. O. M.; CARVALHO, A. C.; MACEDO, M. C. M.; ARAUJO, A. R. de; MONTAGNER, D. B.; ALVES SOBRINHO, T. Effects of Long-Term Crop-Livestock-Forestry Systems on Soil Erosion and Water Infiltration in a Brazilian Cerrado Site. Sustainability, v. 11, 2019.| Biblioteca(s): Embrapa Gado de Corte. |
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| 3. | | SONE, J. S.; OLIVEIRA, P. T. S.; EUCLIDES, V. P. B.; MONTAGNER, D. B.; ARAUJO, A. R. de; ZAMBONI, P. A. P.; VIEIRA, N. O. M.; CARVALHO, G. A.; ALVES SOBRINHO, T. Effects of Nitrogen fertilisation and stocking rates on soil erosion and water infiltration in a Brazilian Cerrado farm. Agriculture, Ecosystems and Environment, n. 304, 2020.| Biblioteca(s): Embrapa Gado de Corte. |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
25/01/2023 |
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Data da última atualização: |
25/01/2023 |
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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: |
SANTOS; MARCATO JUNIOR, J.; ZAMBONI, P.; SANTOS, M. F.; JANK, L.; CAMPOS, E.; MATSUBARA, E. T. |
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Afiliação: |
LUIZ SANTOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; PEDRO ZAMBONI, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; EDILENE CAMPOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDSON TAKASHI MATSUBARA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL. |
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Título: |
Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Sensors, v. 22, article 4116, 2022. |
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ISSN: |
1424-8220 |
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DOI: |
https://doi.org/10.3390/s22114116 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Mateus Figueiredo Santos. |
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Conteúdo: |
We assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 labeled images with 2 mobile phones 7 days after the harvest of the forage plants. Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89. Our findings suggest that our proposal using deep learning on mobile phone images can successfully be used to estimate regrowth density in forages. |
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Thesagro: |
Banco de Germoplasma; Forragem; Panicum Maximum; Tecnologia. |
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Thesaurus NAL: |
Forage; Mechanical harvesting; Regression analysis; Tillering. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1151204/1/Deep-learning-regression-2022.pdf
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Marc: |
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022 $a1424-8220
024 7 $ahttps://doi.org/10.3390/s22114116$2DOI
100 1 $aSANTOS
245 $aDeep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.$h[electronic resource]
260 $c2022
500 $aNa publicação: Mateus Figueiredo Santos.
520 $aWe assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 labeled images with 2 mobile phones 7 days after the harvest of the forage plants. Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89. Our findings suggest that our proposal using deep learning on mobile phone images can successfully be used to estimate regrowth density in forages.
650 $aForage
650 $aMechanical harvesting
650 $aRegression analysis
650 $aTillering
650 $aBanco de Germoplasma
650 $aForragem
650 $aPanicum Maximum
650 $aTecnologia
700 1 $aMARCATO JUNIOR, J.
700 1 $aZAMBONI, P.
700 1 $aSANTOS, M. F.
700 1 $aJANK, L.
700 1 $aCAMPOS, E.
700 1 $aMATSUBARA, E. T.
773 $tSensors$gv. 22, article 4116, 2022.
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Embrapa Gado de Corte (CNPGC) |
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