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.

Hyperspectral Reflectance Response of Wild Rocket (Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model.

Baby leaf wild rocket cropping systems feeding the high convenience salad chain are prone to a set of disease agents that require management measures compatible with the sustainability-own features of the ready-to-eat food segment. In this light, bio-based disease resistance inducers able to elicit the plant's defense mechanism(s) against a wide-spectrum of pathogens are proposed as safe and effective remedies as alternatives to synthetic fungicides, to be, however, implemented under practical field applications. Hyperspectral-based proximal sensing was applied here to detect plant reflectance response to treatment of wild rocket beds with Trichoderma atroviride strain TA35, laminarin-based Vacciplant®, and Saccharomyces cerevisiae strain LAS117 cell wall extract-based Romeo®, compared to a local standard approach including synthetic fungicides (i.e., cyprodinil, fludioxonil, mandipropamid, and metalaxyl-m) and a not-treated control. Variability of the spectral information acquired in VIS-NIR-SWIR regions per treatment was explained by three principal components associated with foliar absorption of water, structural characteristics of the vegetation, and the ecophysiological plant status. Therefore, the following model-based statistical approach returned the interpretation of the inducers' performances at field scale consistent with their putative biological effects. The study stated that compost and laminarin-based treatments were the highest crop impacting ones, resulting in enhanced water intake and in stress-related pigment adjustment, respectively. Whereas plants under the conventional chemical management proved to be in better vigor and health status than the untreated control.

A combined geostatistical approach of data fusion and stochastic simulation for probabilistic assessment of shallow water table depth risk.

In general, water table depth risks are estimated from monitoring networks that mostly provide scarce and irregular data. When jointly analysed, environmental, agricultural and geotechnical variables, treated as stochastic spatial variables, can better describe and interpret the states of a certain system subject to estimation uncertainty. Risk assessment consists essentially in calculating the frequency (probability) with which specified criteria are exceeded or fail to be met by creating multiple stochastic realizations. The aim of this paper is to propose a novel geostatistical methodology, based on the integration into one approach of multi-source data fusion and stochastic simulation, to estimate the risk of extreme (shallow) water table depth, and illustrate a demonstrative example of application of the approach to a case study in a Cerrado conservation area in Brazil. The risk of shallow water table depth was determined by using critical thresholds for water table level and a binary transformation into an indicator variable depending on whether the conditions expressed by the threshold values are met or not. Firstly, auxiliary variables were jointly, analysed to provide a delineation of the study area into homogeneous zones. Secondly, sequential indicator simulation provided a-posteriori probabilities taking into account spatial proximity. The final maps show the most probable risk category for the whole area and spatial entropy as a measure of local uncertainty. Areas nearby watershed divisors and in the north part of the region have a high risk of shallow groundwater. Informed decision-making supported by probabilistic maps and uncertainty evaluation is essential for the success of the projects of Cerrado restoration.

Assessment of the Hyperspectral Data Analysis as a Tool to Diagnose Xylella fastidiosa in the Asymptomatic Leaves of Olive Plants.

Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources.

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.

Modelling spatial and temporal variability of water quality from different monitoring stations using mixed effects model theory.

Polder-type agricultural catchments within river deltas are specific land formations which management is highly demanding from several aspects. The close contact with the coastal sea may additionally affect the quality of adjacent marine environment. This study uses the case of the Lower Neretva Valley (LNV) to test the efficiency of applying Linear Mixed Effect (LME) theory in modelling spatial and temporal variations of surface and groundwater quality within a polder-type agricultural catchment. The methodology uses linear regressive techniques while taking into account spatial and temporal autocorrelation of residuals. The objective was to assess and model the spatial and temporal variability of the quality of surface- and ground-waters, in order to predict the impact of natural processes and human activities. A dataset of physicochemical properties of surface and groundwater quality of the LNV, recorded monthly in the period 2009-2017, was used to model the spatial and temporal variations of water salinity and nitrate concentrations. The network of water quality monitoring sites covers four polders on five thousand hectares of agricultural land, including the following types of water bodies: river streams, lateral canals, pumping stations, drainage canals and groundwater. The method of data analysis, based on LME theory with correlated spatial and temporal residuals, takes also into account the heteroscedasticity of the variance associated with each type of water quality monitoring station. The two Linear Mixed Effects models proposed for the prediction of electrical conductivity and nitrate concentration in the surface waters and groundwater, proved to be efficient at adequately reproducing the heterogeneity and complexity of the study area. However, the prediction of nitrate concentration in the water was not equally satisfactory of the one of electrical conductivity due to the large variation in nutrient concentrations. To improve spatial prediction, the density of monitoring network should be increased.

A geostatistical approach for multi-source data fusion to predict water table depth.

Accurate water table depth mapping is important for water management and activity planning. The joint use of exhausted geospatial raster data with sparse field measurements could improve predictions. The aim of this work was to fuse different support data, collected with remote sensors, with point soil field observations to improve water table depth prediction. A method for multi-source data fusion is described in detail, based on multivariate geostatistics and exemplified with a case study in a conservation area of 5700 ha in the state of São Paulo, Brazil. TanDEM-X digital surface model with 90 m resolution and SAFER (Simple Algorithm for Evapotranspiration Retrieving) data calculated from Sentinel-2 images with 20 m resolution, were jointly used with water table depth and soil physical variables measured at 56 locations to predict water table depth in two hydrological years (2015-16 and 2016-17). Data were transformed to normal distributions using the Gaussian anamorphosis approach. A Linear Model of Coregionalization (LMC), calculated for all direct and cross-variograms of the eleven variables of study, was regularized at block support for multi-collocated block cokriging predictions. Support change correction was made to reduce punctual variance to block variances. Univariate and multivariate geostatistical interpolation methods were compared through cross validation. The uncertainty associated to the water table depths estimated by multivariate approach was lower than those by the univariate approach. Moreover, multivariate predictions incorporated the influences induced by local relief, vegetation and soil properties. Confidence interval maps, presented as uncertainty measure, reveal areas with higher and lower precision of groundwater level prediction that could be effectively used as support in land use management.

A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece.

Precision Viticulture requires very fine-scale spatial and temporal resolution to assess quite accurately variation in a vineyard. Many studies have used proximal sensing technology and spatial-temporal data analysis to characterize the local variation of plant vigour over time. The objective of this study was to present the potential of multivariate geostatistical techniques to fuse multi-temporal data from a multi-band radiometer and a geophysical sensor with different support for delineation of a vineyard into homogeneous zones, to be submitted to differential agricultural management. The study was conducted in a commercial table grape vineyard located in southern Greece during the years 2016 and 2017. Soil electrical conductivity was measured using an EM38 sensor, while Crop Circle canopy sensor, with the sensor located at 1.5 m height from the soil surface and 1.2 m horizontally from the vines, was used for scanning the side canopy area at different crop stages. The temporal multi-sensor data were analysed with the geostatistical data fusion techniques of block cokriging, to produce thematic maps, and factorial block cokriging to estimate synthetic scale-dependent regionalized factors. The factor maps at different scales are characterised by random variability with several micro-structures of different plant and soil properties, which leads to difficulties in delineating macro-areas with homogeneous features. In such conditions, high resolution VRA technology should be preferred to management by homogeneous zones for precision viticulture. The results have shown the potential of the proposed approach to deal with multi-source data in precision viticulture. However, further statistical research on data fusion of the outcomes from different sensors is still needed.