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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
12/04/2021 |
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Data da última atualização: |
16/08/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OSCO, L. P.; NOGUEIRA, K.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; FURUYA, D. E. G.; GONÇALVES, W. N.; JORGE, L. A. de C.; MARCATO JUNIOR, J.; SANTOS, J. A. |
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Afiliação: |
LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
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Título: |
Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Precision Agriculture, v. 22, n. 4,2021. |
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Páginas: |
1171-1188 |
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DOI: |
https://doi.org/10.1007/s11119-020-09777-5 |
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Idioma: |
Inglês |
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Conteúdo: |
Accurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation felds from the remaining objects in a multispectral scene is a difcult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following fve state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3+. The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-afected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 min per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of plantation felds. MenosAccurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation felds from the remaining objects in a multispectral scene is a difcult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following fve state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3+. The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-afected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here pro... Mostrar Tudo |
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Palavras-Chave: |
Convolutional neural network; Thematic map. |
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Categoria do assunto: |
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Marc: |
LEADER 02591naa a2200265 a 4500
001 2131208
005 2022-08-16
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11119-020-09777-5$2DOI
100 1 $aOSCO, L. P.
245 $aSemantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery.$h[electronic resource]
260 $c2021
300 $a1171-1188
520 $aAccurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation felds from the remaining objects in a multispectral scene is a difcult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following fve state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3+. The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-afected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 min per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of plantation felds.
653 $aConvolutional neural network
653 $aThematic map
700 1 $aNOGUEIRA, K.
700 1 $aRAMOS, A. P. M.
700 1 $aPINHEIRO, M. M. F.
700 1 $aFURUYA, D. E. G.
700 1 $aGONÇALVES, W. N.
700 1 $aJORGE, L. A. de C.
700 1 $aMARCATO JUNIOR, J.
700 1 $aSANTOS, J. A.
773 $tPrecision Agriculture$gv. 22, n. 4,2021.
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| 11. |  | MIRANDA, J. R. Breve histórico da soja no mundo. In: MIRANDA, J. R. História da soja: a trajetória da soja na história da humanidade. Campinas: Komedi, 2010. p. 17-28.| Tipo: Capítulo em Livro Técnico-Científico |
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