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Sensors 2017, 17(6), 1410; doi:10.3390/s17061410

Root System Water Consumption Pattern Identification on Time Series Data

Telefonica Investigación y Desarrollo Chile, Manuel Montt 1404, 7501105 Santiago, Chile
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Academic Editor: Lammert Kooistra
Received: 6 March 2017 / Revised: 25 May 2017 / Accepted: 2 June 2017 / Published: 16 June 2017
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)
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Abstract

In agriculture, soil and meteorological sensors are used along low power networks to capture data, which allows for optimal resource usage and minimizing environmental impact. This study uses time series analysis methods for outliers’ detection and pattern recognition on soil moisture sensor data to identify irrigation and consumption patterns and to improve a soil moisture prediction and irrigation system. This study compares three new algorithms with the current detection technique in the project; the results greatly decrease the number of false positives detected. The best result is obtained by the Series Strings Comparison (SSC) algorithm averaging a precision of 0.872 on the testing sets, vastly improving the current system’s 0.348 precision. View Full-Text
Keywords: time series analysis; precision agriculture; data science; pattern recognition; Internet of Things time series analysis; precision agriculture; data science; pattern recognition; Internet of Things
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Figueroa, M.; Pope, C. Root System Water Consumption Pattern Identification on Time Series Data. Sensors 2017, 17, 1410.

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