Data/hora: 25/05/2024 14:48:38

Biblioteca(s): 	Embrapa Algodão; Embrapa Instrumentação.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	B - 5	
Data corrente: 	25/10/2017	
Data da última atualização: 	18/07/2018	
Autoria: 	PEÑALOZA, E. A. G.; CRUVINEL, P. E.; OLIVEIRA, V. A.; COSTA, A. G. F.	
Afiliação: 	PAULO ESTEVAO CRUVINEL, CNPDIA.	
Título: 	A model approach to infer the quality in agricultural sprayers supported by knowledge bases and experimental measurements.	
Ano de publicação: 	2017	
Fonte/Imprenta: 	In: International Journal of Semantic Computing, v. 11, n. 3, p. 279-292, 2017.	
ISSN: 	1793-351X	
DOI: 	https://doi.org/10.1142/S1793351X17400104	
Idioma: 	Inglês	
Conteúdo: 	This paper presents a method to infer the quality of sprayers based on data collection of the drop spectra and their physical descriptors, which are used to generate a knowledge base to support decision-making in agriculture. The knowledge base is formed by collected experimental data, obtained in a controlled environment under specific operating conditions, and the semantics used in the spraying process to infer the quality in the application. The electro-hydraulic operating conditions of the sprayer system, which include speed and flow measurements, are used to define experimental tests, perform calibration of the spray booms and select the nozzle types. Using the Grubbs test and the quartile-quartile plot an exploratory analysis of the collected data was made in order to determine the data consistency, the deviation of atypical values, the independence between the data of each test, the repeatability and the normal representation of them. Therefore, integrating measurements to a knowledge base it was possible to improve the decision-making in relation to the quality of the spraying process defined in terms of a distribution function. Results shown that the use of advanced models and semantic interpretation improved the decision-making processes related to the quality of the agricultural sprayers.	
Palavras-Chave: 	Advanced modeling; Agricultural sprayers; Average diameters; Drop size distribution; Quality of agricultural application; Semantic computing.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Instrumentação (CNPDIA)	
Exemplares: 	CNPDIA - (16319-1) - UPC - AP - PP - PROCI.17-0037 -  - 2017.0037 - DIS
CNPA - (28435-1) - UPC - AP - PP -  -  -  - DIS