Data/hora: 23/09/2024 15:57:12

Biblioteca(s): 	Embrapa Instrumentação.	
Data corrente: 	09/06/2022	
Data da última atualização: 	23/01/2024	
Autoria: 	MORENO, B. M.; CRUVINEL, P. E.	
Afiliação: 	PAULO ESTEVAO CRUVINEL, CNPDIA.	
Título: 	Computer vision system for identifying on farming weed species.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	In: IEEE International Conference on Semantic Computing (ICSC), 16th, Laguna Hills, CA, USA, 2022.	
Páginas: 	287 - 292	
ISBN: 	978-1-6654-3418-8	
ISSN: 	2325-6516	
DOI: 	10.1109/ICSC52841.2022.00054	
Idioma: 	Inglês	
Conteúdo: 	In the world, agriculture has been developed by combining new technologies for aid production and profitability while keeping environmental and social responsibility. The sector is primarily responsible to supply food for people, as well as fibers and energy. To keep such results, farmers have faced the need to seek, increasingly rational use of inputs, as is the use of pesticides, plant regulators, and liquid fertilizers. This paper presents a discussion related to the design and development of a computer vision system for precision spraying in the control of weed species into agricultural crops, based on the identification of invasive plants and their quantities. Concepts such as plant segmentation using field-acquired images, leaf features and descriptors, and classification of species are analyzed and implemented in a prototype.	
Palavras-Chave: 	Embedded platform; Plant recognition.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Instrumentação (CNPDIA)	
Exemplares: 	CNPDIA - (18017-1) - UPC - AA - DD - PROCI.22/62 -  - 2022/67 - DIS