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.

Dr John Patrick Stowell

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.