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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
26/11/2021 |
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Data da última atualização: |
13/12/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
SANTOS, T. T.; GEBLER, L. |
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Afiliação: |
THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV. |
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Título: |
A methodology for detection and localization of fruits in apples orchards from aerial images. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa, 2021. |
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Páginas: |
p. 1-9. |
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ISBN: |
978-65-00-34526-1 |
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ISSN: |
2177-9724 |
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Idioma: |
Inglês |
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Notas: |
Organizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021. |
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Conteúdo: |
Abstract. Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available. |
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Palavras-Chave: |
Contagem automática de frutas; Convolutional neural networks; Detecção de maçãs; Fruit detection; Redes neurais. |
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Thesagro: |
Maçã. |
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Thesaurus NAL: |
Apples; Neural networks. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228160/1/PL-Methodology-detection-localization-SBIAgro-2021.pdf
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Marc: |
LEADER 01630nam a2200265 a 4500
001 2136667
005 2023-12-13
008 2021 bl uuuu u00u1 u #d
020 $a978-65-00-34526-1
022 $a2177-9724
100 1 $aSANTOS, T. T.
245 $aA methodology for detection and localization of fruits in apples orchards from aerial images.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa$c2021
300 $ap. 1-9.
500 $aOrganizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021.
520 $aAbstract. Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available.
650 $aApples
650 $aNeural networks
650 $aMaçã
653 $aContagem automática de frutas
653 $aConvolutional neural networks
653 $aDetecção de maçãs
653 $aFruit detection
653 $aRedes neurais
700 1 $aGEBLER, L.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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