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De Benedetto D.,Italian Agricultural Research Council | Castrignano A.,Italian Agricultural Research Council | Rinaldi M.,Italian Agricultural Research Council | Ruggieri S.,Italian Agricultural Research Council | And 5 more authors.
Geoderma | Year: 2013

Spatial heterogeneity in soil properties has an impact on crop response. There is a growing demand for rapid and non-invasive acquisition of fine-scale information on soil and plant variation for site-specific management. Proximal sensing (Electromagnetic Induction (EMI), Ground Penetrating Radar (GPR), hyperspectral spectroscopy (HS)) and remote sensing (RS) can complement direct sampling. However, sensor data fusion techniques, jointly analysing data from different sources, are still being developed.The objective of this work was to define a multivariate and multi-sensor approach by combining EMI, GPR, RS and HS data, without any previous weighing, in order to differentiate an 1.5-ha arable field into homogenous zones.The multi-sensor data were split into four groups: 1) bulk electrical conductivity (EC) from EMI data, 2) amplitude of GPR signal data, 3) the first principal components relating to five bands (green, yellow, red, rededge, near-infrared (NIR) PCs) of hyperspectral reflectance data and 4) the vegetation indices (NDVI, NDRE and NIR/Green) calculated from the remote sensing image. The data of each group were separately analysed and interpolated at the nodes of a same grid by using cokriging or kriging. To obtain spatially contiguous clusters, a combined approach was used, based on multivariate geostatistics and a non-parametric density function algorithm of clustering, applied to the overall multi-sensor data set of the estimates.The full approach allowed to identify three homogenous areas. In particular cluster 1, in the NW part of the field, with the lowest values of bulk electrical conductivity and GPR amplitude, and the highest red PC values. The other two clusters were delineated in the SE part of the field, with the highest values of green, yellow, red edge and NIR PCs for cluster 2, and the highest values of bulk electrical conductivity and vegetation indices for cluster 3. The delineation might be related to the intrinsic spatial variability of soil and the health status of plants and be used to produce a prescription map for site-specific management. © 2012 Elsevier B.V..

Rinaldi M.,Italian Agricultural Research Council | Castrignano A.,Italian Agricultural Research Council | De Benedetto D.,Italian Agricultural Research Council | Sollitto D.,Italian Agricultural Research Council | And 6 more authors.
Environmetrics | Year: 2015

The development and implementation of both economically and environmentally sustainable precision crop management systems can be greatly enhanced through the use of hyperspectral sensing. In this study, the potential of narrow-waveband hyperspectral observations for the discrimination of water-stressed tomato plants (Solanum lycopersicum L.) was investigated in a field experiment conducted in southern Italy. The tomato crop was grown in a 1.8-ha test field that was split into two plots with different irrigation treatments: optimal and deficit water supplies, with the deficit supply using half of the water of the optimal supply in the second half of the crop growing cycle. Hyperspectral measurements were taken with a field spectroradiometer. To reduce the number of variables, principal component analysis was applied to each of six wavelength band sub-intervals across the whole wavelength interval from 400 to 1000nm. The retained principal components were then submitted to canonical discriminant analysis. Finally, the principal components and the canonical component were interpolated using multivariate and univariate geostatistical techniques, respectively, and then mapped. The two irrigation treatments produced different plant biomass and leaf area indices, which were higher under optimal than deficit water conditions, as was the plant water potential. These data show that the correlation between the individual bands varied during the crop cycle, so it was not feasible to choose a specific band to discriminate between the water treatments. However, we show that only a combination of all of the bands that use the full spectral information with differential weighting leads to clear discrimination of the two differently irrigated areas, with a mean accuracy of 75% to 77%. The processing of hyperspectral reflectance data using canonical discriminant analysis can thus provide valuable information for the agricultural producer for the identification of within-field areas of plant stress, so as to implement site-specific irrigation strategies. © 2014 John Wiley & Sons, Ltd.

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