Soil Moisture Sensing Determines Soil Type

Digitized images of the propagated waveforms returned by Time-Domain (TD) sensors reveal the effects of bound water in soil samples, causing errors in the volumetric water content reported for samples due to soil type. Pattern analysis yields not only correct volumetric water content values but also the soil type from which the waveform was taken. Collection and analysis of waveforms from soils having precisely known properties yield computer-generated algorithms to improve the accuracy and number of reporting variables for integrated TD sensors. These algorithms can be deployed in the Cloud for external analysis, or can be incorporated within the sensor to report highly accurate readings of water content, conductivity and soil type under all conditions of these variables with no interpretive burden placed on the user of the sensor. Machine learning algorithms are intended to externally augment data measured by these sensors, or can be added to similar sensors.