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http://www.alice.cnptia.embrapa.br/alice/handle/doc/1136667
Título: | A methodology for detection and localization of fruits in apples orchards from aerial images. |
Autor: | SANTOS, T. T.![]() ![]() GEBLER, L. ![]() ![]() |
Afiliación: | THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV. |
Año: | 2021 |
Referencia: | In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa, 2021. |
Páginas: | p. 1-9. |
Descripción: | 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. |
Thesagro: | Maçã |
NAL Thesaurus: | Neural networks Apples |
Palabras clave: | Redes neurais Contagem automática de frutas Detecção de maçãs Convolutional neural networks Fruit detection |
ISBN: | 978-65-00-34526-1 |
ISSN: | 2177-9724 |
Notas: | Organizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021. |
Tipo de Material: | Artigo em anais e proceedings |
Acceso: | openAccess |
Aparece en las colecciones: | Artigo em anais de congresso (CNPTIA)![]() ![]() |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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PL-Methodology-detection-localization-SBIAgro-2021.pdf | 4.14 MB | Adobe PDF | ![]() Visualizar/Abrir |