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 |
Files in This Item:
File | Description | Size | Format | |
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2017_eve_arrocha.pdf | 1,14 MB | Adobe PDF | View/Open |
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