A system for concurrent on-the-go soil apparent electrical conductivity and gamma-ray sensing in micro-irrigated orchards

Abstract

On-the-go soil apparent electrical conductivity (ECa) sensors are great tools for mapping and monitoring soil properties such as water content, texture, and salinity. ECa maps and surveys are most useful and reliable when obtained in uniformly wet fields. However, soil moisture in micro-irrigated (e.g., drip or micro-sprinklers) orchards is typically non-uniform, with moist soil along tree and irrigation lines, and dry soil between tree rows. We developed a mobile platform and data post-processing algorithm to facilitate geospatial ECa measurements along or near driplines. Gamma-ray (γ-ray) spectrometry is commonly used for clay content and type mapping. Fusion between ECa and γ-ray is often reported to increase the accuracy of field-scale soil maps. However, contrarily to ECa, γ-ray spectrometry is best suited for sensing soils in dry conditions. Micro-irrigated orchards are ideal environments for the combined application of these two sensor technologies. The fusion of topsoil (top 0.5 m) ECa (measured along the driplines) and γ-ray total counts (TC) (measured between the tree rows) data was tested at a 0.4-ha sandy loam citrus orchard in Southern California. Here, we discuss sensor data acquisition, data processing, sensor-directed sampling scheme delineation, and characterization of field-scale soil particle size fraction (0–0.4 m soil profile) spatial variability. Pearson correlation coefficients between sand and silt content with both ECa and TC were significant (p < 0.05). A principal component analysis biplot suggested strong positive relationship with TC and clay content. Backwards stepwise multiple linear regression predicted sand content using TC, elevation, and spatial coordinates as explanatory variables with mean absolute error (MAE) of 3.06 %. Silt content was predicted (MAE=1.55 %) using ECa, elevation, and spatial coordinates. The development of this platform enables better characterization of soil properties in micro-irrigated orchard systems using on-the-go sensing technology.