Please use this identifier to cite or link to this item: http://repositorio.ufc.br/handle/riufc/69503
Type: Artigo de Evento
Title: Outlier detection methods and sensor data fusion for precision agriculture
Authors: Torres, Andrei Bosco Bezerra
Adriano Filho, José
Rocha, Atslands Rego da
Gondim, Rubens Sonsol
Souza, José Neuman de
Issue Date: 2017
Publisher: Simpósio Brasileiro de Computação Ubíqua e Pervasiva
Citation: ROCHA, A. R. et al. Outlier detection methods and sensor data fusion for precision agriculture. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA, 9., 2017, São Paulo. Anais... São Paulo: SBC, 2017. p. 928-937.
Abstract: Precision agriculture is a concept regarding the use of technology to increase production yield while preserving and optimizing resources. One of the means to achieve that goal is to use sensors to monitor crops and adjust the cultivation according to its needs. This paper compares different techniques for sensor data fusion and detection and removal of outliers from gathered data to improve sensors accuracy and to identify possible sensor malfunction. As a case study, we monitored an experimental crop of precocious dwarf cashew using soil moisture sensors. Combining generalized ESD method and a weighted outlier- robust Kalman filter generated the best result, leading to more accurate data.
URI: http://www.repositorio.ufc.br/handle/riufc/69503
Appears in Collections:DETE - Trabalhos apresentados em eventos

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