Cosmic Ray Neutron Detectors for Smart Agriculture/Civil Engineering Monitors
Durham University / Physics
Detailed mapping of soil moisture is a major problem in the
agriculture and civil engineering industries. Since 70% of all fresh
water is used for crop irrigation, methods that promote sustainable
irrigation practices have the potential to drastically reduce water
wastage globally. Measuring ground saturation can also improve
understanding of landslide conditions in civil engineering, helping to
build resilience to extreme weather events. Unfortunately, soil moisture
monitoring techniques such as manual probes or satellite imaging
currently have inadequate resolution and coverage to be an effective
tool in these industries.
This project will research an alternative sensing technique that uses
cosmic ray neutrons (CRN), radiation from the upper atmosphere, to
measure soil moisture on a much finer scale then current methods. Whilst
this technique has already been well tested by hydrologists using
Helium-3 neutron detectors, applications in industrial settings have
been limited by the lack of angular information a single sensor provides
and the large associated costs per area.
This proposal will research novel cosmic ray neutron detection
techniques and data analysis methods that will transform CRN monitoring
into a technique applicable to a wider range of problems. Taking
advantage of recent developments in high sensitivity radiation
detectors, I aim to design a robust neutron sensor module with
significantly reduced cost over Helium-3 detectors.
These new modular sensors will
enable feasibility studies of using interconnected sensor arrays to
extract soil moisture variations as a function of angle or distance from
a fixed position. Machine learning algorithms developed for these
sensor arrays will make it possible to automatically extract calibrated
data mapping soil moisture variations over large areas. Doing so in a
much shorter space of time compared to existing mapping techniques.
The enhanced data provided by these methods will open up the
possibility of using the CRN technique in smart irrigation systems, or
infrastructure safety monitors in the future. These technological
advances aim to support the adoption of the technique in industrial
settings, particularly where cost and data processing capabilities are a
limiting factor.