Data/hora: 10/06/2024 04:41:37

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 26/11/2021
Data da última atualização: | 13/12/2023
Autoria: | SANTOS, T. T.; GEBLER, L.
Afiliação: | THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV.
Título: | A methodology for detection and localization of fruits in apples orchards from aerial images.
Ano de publicação: | 2021
Fonte/Imprenta: | In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa, 2021.
Páginas: | p. 1-9.
ISBN: | 978-65-00-34526-1
ISSN: | 2177-9724
Idioma: | Inglês
Notas: | Organizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021.
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.
Palavras-Chave: | Contagem automática de frutas; Convolutional neural networks; Detecção de maçãs; Fruit detection; Redes neurais.
Thesagro: | Maçã.
Thesaurus Nal: | Apples; Neural networks.
Categoria do assunto: | --
URL: | http://ainfo.cnptia.embrapa.br/digital/bitstream/item/228160/1/PL-Methodology-detection-localization-SBIAgro-2021.pdf
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (21035-1) - UPC - AA - DD -  -  -  - DIS