Título:	A methodology for detection and localization of fruits in apples orchards from aerial images.
Autoria:	SANTOS, T. T. GEBLER, L.
Afiliação:	THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV.
Ano de publicação:	2021
Referência:	In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa, 2021.
Páginas:	p. 1-9.
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.
Thesagro:	Maçã
NAL Thesaurus:	Neural networks Apples
Palavras-chave:	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 do material:	Artigo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Artigo em anais de congresso (CNPTIA)