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Registro Completo |
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Biblioteca(s): |
Embrapa Uva e Vinho. |
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Data corrente: |
30/08/2016 |
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Data da última atualização: |
08/03/2019 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
NACHTIGALL, L. G.; ARAUJO, R. M.; NACHTIGALL, G. R. |
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Afiliação: |
GILMAR RIBEIRO NACHTIGALL, CNPUV. |
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Título: |
Classification of apple tree disorders using Convolutional Neural Networks. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENSE, 28., 2016, San Jose, United States. Anais...San Jose, United States: IEEE, Paper Submission 127, p. 472-476, 2016. |
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Idioma: |
Português |
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Conteúdo: |
Abstract?This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of their leaves. This task is fundamental to guarantee a high quality of the resulting yields and is currently largely performed by experts in the field, which can severely limit scale and add to costs. By using a novel data set containing labeled examples consisting of 2539 images from 6 known disorders, we show that trained Convolutional Neural Networks are able to match or outperform experts in this task, achieving a 97.3% accuracy on a hold-out set. |
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Palavras-Chave: |
Apple trees; Convolutional Neural Networks; Damage; Diseases; Herbicide; Macieira; Nutritional deficiencies; Redes neurais. |
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Thesagro: |
Maca. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/157421/1/Nachtigall-IEE28-2016-472-476.pdf
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Marc: |
LEADER 01465nam a2200241 a 4500
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008 2016 bl uuuu u00u1 u #d
100 1 $aNACHTIGALL, L. G.
245 $aClassification of apple tree disorders using Convolutional Neural Networks.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENSE, 28., 2016, San Jose, United States. Anais...San Jose, United States: IEEE, Paper Submission 127, p. 472-476$c2016
520 $aAbstract?This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of their leaves. This task is fundamental to guarantee a high quality of the resulting yields and is currently largely performed by experts in the field, which can severely limit scale and add to costs. By using a novel data set containing labeled examples consisting of 2539 images from 6 known disorders, we show that trained Convolutional Neural Networks are able to match or outperform experts in this task, achieving a 97.3% accuracy on a hold-out set.
650 $aMaca
653 $aApple trees
653 $aConvolutional Neural Networks
653 $aDamage
653 $aDiseases
653 $aHerbicide
653 $aMacieira
653 $aNutritional deficiencies
653 $aRedes neurais
700 1 $aARAUJO, R. M.
700 1 $aNACHTIGALL, G. R.
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Embrapa Uva e Vinho (CNPUV) |
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| Registros recuperados : 4 | |
| 2. |  | MAGALHÃES, E. E. de; SILVA, C. S. B. da; PEREIRA, A. V. da S.; MARTINS, C. de A.; CABRAL, C. da P.; LOPES, D. B. Influência da posição de semeio na germinação de sementes de mulungu (Erythrina vellutina Wild.). In: REUNIÃO NORDESTINA DE BOTÂNICA, 27., 2004, Petrolina. Anais... Petrolina: SBB; Embrapa Semi-Árido; UNEB, 2004. 1 CD-ROM.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Semiárido. |
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| 3. | | MAGALHÃES, E. E. de; SILVA, C. S. B. da; PEREIRA, A. V. da S; MARTINS, C. de A.; CABRAL, C. da P.; MOREIRA, W. A. Influência da profundidade de semeio na germinação de sementes de mulungu (Erythrina vellutina Wild - Leguminosae). In: REUNIÃO NORDESTINA DE BOTÂNICA, 27., 2004, Petrolina. Anais... Petrolina: SBB; Embrapa Semi-Árido; UNEB, 2004. 1 CD-ROM.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Semiárido. |
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| Registros recuperados : 4 | |
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