Data/hora: 22/05/2024 13:29:53

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