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Registro Completo |
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Biblioteca(s): |
Embrapa Tabuleiros Costeiros. |
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Data corrente: |
25/10/2013 |
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Data da última atualização: |
09/09/2016 |
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Autoria: |
TUPINANBA, E. A.; BUENO, A. S. |
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Título: |
Características básicas das variedades de coqueiro do BAG de coco. |
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Ano de publicação: |
1999 |
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Fonte/Imprenta: |
Aracaju: Embrapa Tabuleiros Costeiros, 1999. |
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Páginas: |
3 p. |
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Série: |
(EMBRAPA-CPATC. Pesquisa em Andamento, 79). |
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Idioma: |
Português |
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Palavras-Chave: |
Coconut; Coqueiro. |
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Thesagro: |
Coco. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/91469/1/CPATC-PESQ.-AND.-79-99.pdf
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Marc: |
LEADER 00503nam a2200169 a 4500
001 1969407
005 2016-09-09
008 1999 bl uuuu u0uu1 u #d
100 1 $aTUPINANBA, E. A.
245 $aCaracterísticas básicas das variedades de coqueiro do BAG de coco.$h[electronic resource]
260 $aAracaju: Embrapa Tabuleiros Costeiros$c1999
300 $a3 p.
490 $a(EMBRAPA-CPATC. Pesquisa em Andamento, 79).
650 $aCoco
653 $aCoconut
653 $aCoqueiro
700 1 $aBUENO, A. S.
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Registro original: |
Embrapa Tabuleiros Costeiros (CPATC) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Uva e Vinho. Para informações adicionais entre em contato com cnpuv.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Uva e Vinho. |
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Data corrente: |
16/12/2020 |
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Data da última atualização: |
20/12/2022 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
MELO, R. F. de; LIMA, G. L. de; CORRÊA, G. R.; ZATT, B.; AGUIAR, M. S. de; NACHTIGALL, G. R.; ARAÚJO, R. M. |
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Afiliação: |
RAMÁSIO FERREIRA DE MELO; GUSTAVO LAMEIRÃO DE LIMA; GUILHERME RIBEIRO CORRÊA; BRUNO ZATT; MARILTON SANCHOTENE DE AGUIAR; GILMAR RIBEIRO NACHTIGALL, CNPUV; RICARDO MATSUMURA ARAÚJO. |
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Título: |
Diagnosis of apple fruit diseases in the wild with Mask R-CNN. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
In: CERRI, R.; PRATI, R. C. (Eds). Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science, vol 12319. Springer, 2020. p. 256?270. |
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DOI: |
https://doi.org/10.1007/978-3-030-61377-8_18 |
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Idioma: |
Português |
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Conteúdo: |
A major challenge in image classification tasks using Machine Learning, and in particular when using deep neural networks, is domain shifting in deployment. This happens when images during usage are capture in different conditions from those used during training. In this paper, we show that despite previous works on the diagnosis of apple tree diseases using standard Convolutional Neural Networks displaying high accuracy, they do so only when no domain shift is present. When the trained model is asked to classify photos of apples taken in the wild, a 22% reduction in F1 score is observed. We propose to treat the task as a segmentation problem and test two different approaches, showing that using Mask R-CNN allows not only to improve performance in the original domain by 3%, but also significantly reduce losses in the new domain (only 6% reduction in F1 score). We establish segmentation as an important alternative towards improving diagnosis of apple tree diseases from photos. |
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Palavras-Chave: |
Apple fruits; Deep learning; Instance segmentation. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 01790naa a2200241 a 4500
001 2128251
005 2022-12-20
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/978-3-030-61377-8_18$2DOI
100 1 $aMELO, R. F. de
245 $aDiagnosis of apple fruit diseases in the wild with Mask R-CNN.$h[electronic resource]
260 $c2020
520 $aA major challenge in image classification tasks using Machine Learning, and in particular when using deep neural networks, is domain shifting in deployment. This happens when images during usage are capture in different conditions from those used during training. In this paper, we show that despite previous works on the diagnosis of apple tree diseases using standard Convolutional Neural Networks displaying high accuracy, they do so only when no domain shift is present. When the trained model is asked to classify photos of apples taken in the wild, a 22% reduction in F1 score is observed. We propose to treat the task as a segmentation problem and test two different approaches, showing that using Mask R-CNN allows not only to improve performance in the original domain by 3%, but also significantly reduce losses in the new domain (only 6% reduction in F1 score). We establish segmentation as an important alternative towards improving diagnosis of apple tree diseases from photos.
653 $aApple fruits
653 $aDeep learning
653 $aInstance segmentation
700 1 $aLIMA, G. L. de
700 1 $aCORRÊA, G. R.
700 1 $aZATT, B.
700 1 $aAGUIAR, M. S. de
700 1 $aNACHTIGALL, G. R.
700 1 $aARAÚJO, R. M.
773 $tIn: CERRI, R.; PRATI, R. C. (Eds). Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science, vol 12319. Springer, 2020. p. 256?270.
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