Mary Stuart

Dr. Mary Stuart

College of Science and Engineering, University of Derby, Derby, UK
orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 July 2019 in Sensors
Verified
Journal Article
Open access
PDF file
MSMary Stuart
Mary Stuart
Andrew McgonigleAndrew Mcgonigle
Andrew Mcgonigle
JWJon Willlmott
Jon Willlmott
The development and uptake of field deployable hyperspectral imaging systems within environmental monitoring represents an exciting and innovative development that could revolutionize a number of sensing applications in the coming decades. In this article we focus on the successful miniaturization and improved portability of hyperspectral sensors, covering their application both from aerial and ground-based platforms in a number of environmental application areas, highlighting in particular the recent implementation of low-cost consumer technology in this context. At present, these devices largely complement existing monitoring approaches, however, as technology continues to improve, these units are moving towards reaching a standard suitable for stand-alone monitoring in the not too distant future. As these low-cost and light-weight devices are already producing scientific grade results, they now have the potential to significantly improve accessibility to hyperspectral monitoring technology, as well as vastly proliferating acquisition of such datasets.

20 June 2022 in Sensors
Verified
Journal Article
Open access
PDF file
MSMary Stuart
Mary Stuart
MDMatthew Davies
Matthew Davies
MHMatthew Hobbs
Matthew Hobbs
TPTom Pering
Tom Pering
Andrew McgonigleAndrew Mcgonigle
Andrew Mcgonigle
JWJon Willlmott
Jon Willlmott
High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider implications, limiting data collection opportunities, and therefore our knowledge, across a wide range of environments. In this article we introduce a low-cost alternative to the currently available instrumentation. This instrument provides hyperspectral datasets capable of resolving spectral variations in mm-scale targets, that cannot typically be resolved with many existing low-cost hyperspectral imaging alternatives. Instrument metrology is provided, and its efficacy is demonstrated within a mineralogy-based environmental monitoring application highlighting it as a valuable addition to the field of low-cost hyperspectral imaging.

9 June 2020 in Sensors
Verified
Journal Article
Open access
PDF file
MSMary Stuart
Mary Stuart
LSLeigh Stanger
Leigh Stanger
MHMatthew Hobbs
Matthew Hobbs
TPTom Pering
Tom Pering
DTDaniel Thio
Daniel Thio
JWJon Willlmott
Jon Willlmott
The recent surge in the development of low-cost, miniaturised technologies provides a significant opportunity to develop miniaturised hyperspectral imagers at a fraction of the cost of currently available commercial set-ups. This article introduces a low-cost laboratory-based hyperspectral imager developed using commercially available components. The imager is capable of quantitative and qualitative hyperspectral measurements, and it was tested in a variety of laboratory-based environmental applications where it demonstrated its ability to collect data that correlates well with existing datasets. In its current format, the imager is an accurate laboratory measurement tool, with significant potential for ongoing future developments. It represents an initial development in accessible hyperspectral technologies, providing a robust basis for future improvements.

5 August 2021 in Journal of Imaging
Verified
Journal Article
Open access
PDF file
MSMary Stuart
Mary Stuart
Andrew McgonigleAndrew Mcgonigle
Andrew Mcgonigle
MDMatthew Davies
Matthew Davies
MHMatthew Hobbs
Matthew Hobbs
NBNicholas Boone
Nicholas Boone
JWJon Willlmott
Jon Willlmott
Recent advances in smartphone technologies have opened the door to the development of accessible, highly portable sensing tools capable of accurate and reliable data collection in a range of environmental settings. In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that can convert a standard smartphone camera into a visible wavelength hyperspectral sensor for ca. £100. To the best of our knowledge, this represents the first smartphone capable of hyperspectral data collection without the need for extensive post processing. The Hyperspectral Smartphone’s abilities are tested in a variety of environmental applications and its capabilities directly compared to the laboratory-based analogue from our previous research, as well as the wider existing literature. The Hyperspectral Smartphone is capable of accurate, laboratory- and field-based hyperspectral data collection, demonstrating the significant promise of both this device and smartphone-based hyperspectral imaging as a whole.

25 February 2022 in Remote Sensing
Verified
Journal Article
Open access
PDF file
MDMatthew Davies
Matthew Davies
MSMary Stuart
Mary Stuart
MHMatthew Hobbs
Matthew Hobbs
Andrew McgonigleAndrew Mcgonigle
Andrew Mcgonigle
JWJon Willlmott
Jon Willlmott
Developments in the portability of low-cost hyperspectral imaging instruments translate to significant benefits to agricultural industries and environmental monitoring applications. These advances can be further explicated by removing the need for complex post-processing and calibration. We propose a method for substantially increasing the utility of portable hyperspectral imaging. Vertical and horizontal spatial distortions introduced into images by ‘operator shake’ are corrected by an in-scene reference card with two spatial references. In situ light-source-independent spectral calibration is performed. This is achieved by a comparison of the ground-truth spectral reflectance of an in-scene red–green–blue target to the uncalibrated output of the hyperspectral data. Finally, bias introduced into the hyperspectral images due to the non-flat spectral output of the illumination is removed. This allows for low-skilled operation of a truly handheld, low-cost hyperspectral imager for agriculture, environmental monitoring, or other visible hyperspectral imaging applications.

9 August 2022 in Remote Sensing
Verified
Journal Article
Open access
PDF file
MSMary Stuart
Mary Stuart
MDMatthew Davies
Matthew Davies
MHMatthew Hobbs
Matthew Hobbs
AMAndrew J. S. McGonigle
Andrew J. S. McGonigle
JWJon Willlmott
Jon Willlmott
Peatland habitats represent key environmental resources that are a critical component in climate change mitigation strategies. However, many of these environmental settings are facing significant levels of erosion and degradation which, over time, will result in the loss of these key environments. Traditional monitoring techniques for these settings require invasive methods, disrupting the natural environment and potentially leading to further losses if incorrectly administered. In this article, we provide a non-invasive, cost-effective alternative to peatland health monitoring through the implementation of low-cost hyperspectral imaging techniques. Using common peatland plant species as a proxy for underlying peat health, we monitor the spectral response of Sphagnum plants under varying degrees of water stress to document their spectral response under these conditions. For this research, we utilise a low-cost, semi-portable High-Resolution Hyperspectral Imager capable of resolving mm-scale targets in conjunction with the ultra-low-cost Hyperspectral Smartphone that represents a completely accessible fully field portable instrument allowing for rapid and accurate on-site measurements. Both instruments are shown to provide accurate and robust results, capturing subtle changes in spectral response prior to their appearance within visual datasets enabling the use of mitigation and restoration techniques before the onset of more damaging conditions.