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ThinkMind // SENSORDEVICES 2020, The Eleventh International Conference on Sensor Device Technologies and Applications // View article sensordevices_2020_1_120_20057
Multi-Sensor Data Fusion and Artificial Neural Network to Estimate the Velocity of Sportive Turfgrass in Putting Green Areas
 Authors:
Lorena Parra
Pedro V. Mauri
Jaime Lloret
Salima Yousfi
José F. Marin Keywords: velocity of the putting greens; soil moisture; soil temperature; Normalized Difference Vegetation Index; turfgrass Abstract:
Although the use of sensors is extended in the environmental monitoring, there are some variables which cannot be directly measured and must be estimated. The velocity of sportive turfgrass is one of them. In this paper, we attempt to estimate the velocity in two putting greens of a golf course, before and after a maintenance action, by the measurement of agronomic variables with digital devices. We measure the soil moisture and temperature, the canopy temperature and the Normalised Difference Vegetation Index. The measurements are performed during five months in two putting greens of Agrostis stolonifera. The results indicated that the monitoring of a single agronomic parameter is not useful to evaluate the recovery of the putting green. The agronomic variables showed a total recovery 22 after the maintenance action. Meanwhile, the data of velocity indicates that full recovery was not achieved after 124 days. Finally, we use the agronomic variables to estimate the velocity. A multiple regression model was defined with Normalised Difference Vegetation Index, soil moisture, and soil temperature. Then, those variables are included in an artificial neural network model to generate graphics, which can be used by greenkeepers to estimate the velocity. The model archived 70% of correctly classified cases. Graphics of classification to be used by the greenkeepers, which include the estimated velocity based on soil moisture and Normalised Difference Vegetation Index, for four different temperatures are generated. Pages: 58 to 63 Copyright: Copyright (c) IARIA, 2020 Publication date: November 21, 2020 Published in: conference ISSN: 2308-3514 ISBN: 978-1-61208-820-4 Location: Valencia, Spain Dates: from November 21, 2020 to November 25, 2020
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