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

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.

Impact of Data Processing and Antenna Frequency on Spatial Structure Modelling of GPR Data.

Over the last few years high-resolution geophysical techniques, in particular ground-penetrating radar (GPR), have been used in agricultural applications for assessing soil water content variation in a non-invasive way. However, the wide use of GPR is greatly limited by the data processing complexity. In this paper, a quantitative analysis of GPR data is proposed. The data were collected with 250, 600 and 1600 MHz antennas in a gravelly soil located in south-eastern Italy. The objectives were: (1) to investigate the impact of data processing on radar signals; (2) to select a quick, efficient and error-effective data processing for detecting subsurface features; (3) to examine the response of GPR as a function of operating frequency, by using statistical and geostatistical techniques. Six data processing sequences with an increasing level of complexity were applied. The results showed that the type and range of spatial structures of GPR data did not depend on data processing at a given frequency. It was also evident that the noise tended to decrease with the complexity of processing, then the most error-effective procedure was selected. The results highlight the critical importance of the antenna frequency and of the spatial scale of soil/subsoil processes being investigated.

A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees.

Xylella fastidiosa is one of the most destructive plant pathogenic bacteria worldwide, affecting more than 500 plant species. In Apulia region (southeastern Italy), X. fastidiosa subsp. pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism, and the cultural heritage of this region. An early detection of the infected plants would hinder the rapid spread of the disease. The main objective of this paper was to define a geostatistical approach of data fusion, which combines remote (radiometric), and proximal (geophysical) sensor data and visual inspections with plant diagnostic tests, to provide probabilistic maps of Xfp infection risk. The study site was an olive grove located at Oria (province of Brindisi, Italy), where at the time of monitoring (September 2017) only few plants showed initial symptoms of the disease. The measurements included: 1) acquisitions of reflected electromagnetic radiation with UAV (Unmanned Aerial Vehicle) equipped with a multi-spectral camera; 2) geophysical surveys on the trunks of 49 plants with Ground Penetrating Radar (GPR); 3) disease severity rating, by visual inspection of the proportion of canopy with symptoms; 4) qPCR (real time-quantitative Polymerase Chain Reaction) data from tests on 61 plants. The data were submitted to a set of processing techniques to define a "data fusion" procedure, based on non-parametric multivariate geostatistics. The approach allowed marking those areas where the risk of infection was higher, and identifying the possible infection entry routes into the field. The probability map of infection risk could be used as an effective tool for a preventive action and for a better organization of the monitoring plans.