A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture
|dc.contributor.author||Viscarra Rossel, Raphael|
|dc.identifier.citation||Castrignanò, A. and Buttafuoco, G. and Quarto, R. and Parisi, D. and Viscarra Rossel, R. and Terribile, F. and Langella, G. et al. 2018. A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture. CATENA. 167: pp. 293-304.|
Application of Precision Agriculture requires an accurate assessment of fine-resolution spatial variation. At present, advances in proximal sensing and spatial data analysis are available to characterize soil systems and detect changes in physical or chemical properties useful to understand and manage the variation within fields in a site-specific way. The objective of this work was to verify the suitability of geostatistical techniques to fuse data measured with different geophysical sensors for delineating homogeneous within-field zones for Precision Agriculture. A geophysical survey, using electromagnetic induction (EMI) and ground penetrating radar (GPR), was carried out at Montecorvino Rovella in the southern Apennines (Salerno, Italy). Both sensors (EMI and GPR) enabled the assessment of variation of soil dielectric properties both laterally and vertically. The study area is a 5 ha terraced olive grove under organic cropping. The sensor surveys were carried out along the terraces and over the entire field. The multi-sensor data were analyzed using geostatistical techniques to estimate synthetic scale-dependent regionalized factors. The results allowed the division of the study area into smaller areas, characterized by different properties that could impact agronomic management. In particular, a large area was delineated in the northern part of the grove, where apparent soil electrical conductivity and radar attenuation were greater. Through soil profiling it was shown that soils of the northern macro-area refer to deep, well developed, clayey Luvic Phaezem, whereas soils of the southern macro-area are shallower and less developed, sandy loam Leptic Calcisol. The proposed geostatistical approach effectively combined the complementary 2D EMI and 3D GPR measurements, to delineate areas characterized by different soil horizontal and vertical conditions. This within-olive grove partition might be advantageously used for site-specific tillage and fertilization.
|dc.title||A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture|
|curtin.department||School of Molecular and Life Sciences (MLS)|
|curtin.accessStatus||Fulltext not available|
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