Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Irrigation Control Through Acoustic Proximal Sensing of the Onset of Surface Water

Version 1 : Received: 14 August 2020 / Approved: 15 August 2020 / Online: 15 August 2020 (09:31:37 CEST)

How to cite: Bradley, S.; Radionova, A.; Ghimire, C.; Grundy, L.; Laurenson, S.; Snow, V. Irrigation Control Through Acoustic Proximal Sensing of the Onset of Surface Water. Preprints 2020, 2020080345 (doi: 10.20944/preprints202008.0345.v1). Bradley, S.; Radionova, A.; Ghimire, C.; Grundy, L.; Laurenson, S.; Snow, V. Irrigation Control Through Acoustic Proximal Sensing of the Onset of Surface Water. Preprints 2020, 2020080345 (doi: 10.20944/preprints202008.0345.v1).

Abstract

Irrigation is a useful crop enhancement procedure up to the point where free surface water appears. Thereafter, water can begin to flow into waterways, leaching nutrients and giving rise to environmental damage, as well as being a waste of a precious resource. The current work addresses the problem of measuring free water on the surface of agricultural soils by a real-time acoustic remote sensing method. Directional acoustic transmitter and receiver arrays are used to define a ‘footprint’ on the ground from which changes in reflectance are sensed. These arrays are mounted on a moving irrigator. Chirp signals are used to provide along-path resolution and to ensure robustness against unwanted acoustic background noise from farm machinery and the irrigator. Field measurements have been conducted above a well-defined ‘quadrat’ with controlled and measured water content, and also with the instrument mounted on an operational irrigator. A structured light camera mounted above the footprint is used to validate surface water fraction. It is found that the areal fraction of free water on the soil surface can be reliably estimated from changes in the amplitude of the reflected sound waves. The mechanism giving rise to the observed acoustic reflectivity changes is discussed and a model is developed which agrees with normalized intensity observations with a coefficient of determination R2 between 0.65 and 0.83. The rms error between model predictions and observations is comparable to the rms variation of the measurements, indicating that there is insignificant error due to the choice of model.

Subject Areas

surface water runoff; acoustic sensor array; reflected sound wave; reflection from rough surfaces

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