Outlier detection methods and sensor data fusion for precision agriculture

  • Andrei B. B. Torres
  • José Adriano Filho
  • Atslands R. da Rocha
  • Rubens Sonsol Gondim
  • José Neuman de Souza
DOI: https://doi.org/10.5753/sbcup.2017.3316

Resumo


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 outlierrobust Kalman filter generated the best result, leading to more accurate data.

Publicado
06/07/2017
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TORRES, Andrei B. B.; ADRIANO FILHO, José; DA ROCHA, Atslands R.; GONDIM, Rubens Sonsol; DE SOUZA, José Neuman. Outlier detection methods and sensor data fusion for precision agriculture. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 9. , 2017, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2017 . ISSN 2595-6183. DOI: https://doi.org/10.5753/sbcup.2017.3316.