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Sensors 2017, 17(12), 2794; https://doi.org/10.3390/s17122794

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field

1
CREA Research Centre for Agriculture and Environment, 70125 Bari, Italy
2
National Research Council of Italy, Institute for Agricultural and Forest Systems in the Mediterranean, 87036 Rende (CS), Italy
3
Earth and Geoenvironmental Sciences Department, University of Bari Aldo Moro, 700125 Bari, Italy
4
Department of Agriculture, University of Naples Federico II, 8055 Portici (NA), Italy
5
CREA Research Center for Vegetable and Ornamental Crops, 84098 Pontecagnano (SA), Italy
*
Author to whom correspondence should be addressed.
Received: 15 October 2017 / Revised: 27 November 2017 / Accepted: 1 December 2017 / Published: 3 December 2017
(This article belongs to the Special Issue Sensors in Agriculture)
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Abstract

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed. View Full-Text
Keywords: spatial data; sensor; data fusion; change of support; geostatistics; precision agriculture; management zones spatial data; sensor; data fusion; change of support; geostatistics; precision agriculture; management zones
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Castrignanò, A.; Buttafuoco, G.; Quarto, R.; Vitti, C.; Langella, G.; Terribile, F.; Venezia, A. A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field. Sensors 2017, 17, 2794.

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