4D-HSI-4-Free: Integrating Sensors & Vision Process Engineering to Deliver a Tool for revealing 3D Hyperspectral Agri-data from 2D Images

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Hyperspectral and multispectral imaging for crop phenotyping and stress analysis has seen significant growth in recent years for wide area scanning, notably from satellites and aircraft. This has been principally based on variants of the Normalised Difference Vegetation Index, 'red edge', ratios and / or chlorophyll fluorescence bands. Though more subtle agri-analysis is being introduced within these remote monitoring technologies, notably with the launch of the ESA Sentinel family of satellites, the reliance on sunlit imaging and the physical engineering limits on resolving power of the optics, alongside atmospheric variations in absorbance and refraction, set hard boundaries on the spatial and spectral sensitivity.In more recent years Close-Proximity Hyperspectral Imaging (CP-HSI) has been progressed by a number of research groups, in order to ground-truth remotely collected images as well as to gain data with greater subtlety and differentiating power than can be obtained from the remote platforms, due to the physical constraints described. Additionally, CP-HSI offers the potential for greater data availability by avoiding the issues of cloud-cover, and other obstructions, as well as the lack of access to images whilst satellites are orbiting. These CP-HSI studies have tended to adopt the same high-cost passive HSI instruments, as used by the geospatial imaging sector, and then take advantage of the variations in plant and soil data arising intra- and inter-crop canopy to identify the new agri-features.The e-Agri research team at the University of Manchester have worked over recent years on translating the above concepts into lower-cost and miniaturised engineered systems through replacing the passive HSI instrumentation with active systems, based on broadband proprietary silicon imaging detectors coupled with narrowband LED sources. The resulting Active Close-Proximity (ACP) HSI systems are not only several orders of magnitude cheaper and higher spatial resolution, than their remote passive equivalents, they also offer spectral signal-to-noise that can far exceed what is possible from the passive instruments.Encouraging as this is, there are engineering challenges associated with active CP-HSI, not least the need to compensate for the orientation affects from position of the biological specimen with respect to the sensor system as well prevent corruption of the spectral data by variations in the lighting location and optical power of the LEDs. These are reconcilable through integrating the optical engineering of multiple sources alongside Photometric Stereo (PS) image reconstruction. The PS technique has arisen as an alternate technique to structured-light for delivering surface normals and texture information at unprecedented spatial resolution, while using inherently low-cost equipment. PS involves capturing a number of images (3+) of an object under varying but known lighting, with each image providing a constraint on the orientation at each pixel. Work pioneered by the CMV has allowed the PS technique to move beyond a laboratory setting towards real-word applications, by relaxing prior assumptions, such as Lambertian surface reflection, collimated illumination and by extending the technique to moving applications.The team will deliver a new class of 4-Dimensional imaging within an accessible tool which will only marginally increases the component cost versus the 2D hyperspectral precursor. The project is achievable as it will be accelerated via the e-Agri UoM team partnering with the Centre for Machine Vision, at the Bristol Robotics Laboratory, to translate their PS algorithms so that they may be integrated within the ACP-HSI sensors research. The merger of these capabilities will prove the viability of a miniaturised 4D imaging system and deliver a proof-of-principal prototype system for characterisation against plant, animal and aquatic samples.