A low-cost hyperspectral scanner for natural imaging and the study of animal colour vision above and under water

Hyperspectral imaging is a widely used technology for industrial and scientific purposes, but the high cost and large size of commercial setups have made them impractical for most basic research. Here, we designed and implemented a fully open source and low-cost hyperspectral scanner based on a commercial spectrometer coupled to custom optical, mechanical and electronic components. We demonstrate our scanner's utility for natural imaging in both terrestrial and underwater environments. Our design provides sub-nm spectral resolution between 350-950 nm, including the UV part of the light spectrum which has been mostly absent from commercial solutions and previous natural imaging studies. By comparing the full light spectra from natural scenes to the spectral sensitivity of animals, we show how our system can be used to identify subtle variations in chromatic details detectable by different species. In addition, we have created an open access database for hyperspectral datasets collected from natural scenes in the UK and India. Together with comprehensive online build- and use-instructions, our setup provides an inexpensive and customisable solution to gather and share hyperspectral imaging data

Fusion of multispectral imagery and spectrometer data in UAV remote sensing

High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors have been developed for UAVs, but th

Framework for semi-automated object-based image classification of invasive alien plant species in South Africa: Harrisia Pomanensis as a case study

Invasive alien plants (IAPs) not only pose a serious threat to biodiversity and water resources but also have impacts on human and animal wellbeing. An important step in IAPs management is to map their location as there is a strong correlation between the spatial extent of an invaded area and the effort required for clearing the plant invasion. However, the traditional GPS based IAPs mapping field campaigns are costly, time consuming and labour intensive. The developments in the Unmanned Aerial Vehicle (UAV) technology have afforded the remote sensing (RS) community the opportunity to map IAPs at enhanced temporal and spatial resolutions. As a result, this framework synthesises a UAV-RS approach for mapping invasive alien plants in South African semi-arid woodlands using Harrisia pomanensis (the Midnight lady) as a case study. In particular, this framework outlines procedures for geometric and radiometric calibration of UAV-derived orthomosaics as well a semi-automated object-based image classification technique for mapping IAPs. The geometric calibration was conducted in the Agisoft Lens software package to determine the camera interior orientation parameters. Since sample photos of the LCD screen were taken from a short-range, there were more radial than tangential distortions. In addition, a scene illumination uniformity statistical inference allowed for the radiometric calibration of the entire scene using parameters derived from radiometric calibration targets placed only in one spot within the study area using the empirical line method (ELM). In particular, accuracy assessment of the radiometric calibration resulted in a correlation coefficient (r) value of 0.977 between in situ measured reflectance and the reflectance values derived from the calibrated image wavebands. This strong correlation validated the proposed UAV-RS ELM based radiometric calibration method for applications in semi-arid woodlands. Furthermore, out of the five evaluated image classifiers, the case study demonstrated that the object-based supervised Bhattacharya classifier which gave 90% and 95.7% producer and user accuracies, respectively, produced more accurate results for mapping Harrisia pomanensis. Even more so, an area based accuracy assessment showed that the Bhattacharya classifier mapped Harrisia pomanensis better than the Maxver classifier (i.e. the second best algorithm) with mapping accuracy averages of 86.1% and 65.2%, respectively, for all the different polygon area sizes. Future research should ascertain whethe radiometric calibration increases mapping accuracy in large scale (>100ha) UAV-RS applications.Dissertation (MSc)--University of Pretoria, 2018.Geography, Geoinformatics and MeteorologyMScUnrestricte

Custom scanning hyperspectral imaging system for biomedical applications: modeling, benchmarking, and specifications

Prototyping hyperspectral imaging devices in current biomedical optics research requires taking into consideration various issues regarding optics, imaging, and instrumentation. In summary, an ideal imaging system should only be limited by exposure time, but there will be technological limitations (e.g., actuator delay and backlash, network delays, or embedded CPU speed) that should be considered, modeled, and optimized. This can be achieved by constructing a multiparametric model for the imaging system in question. The article describes a rotating-mirror scanning hyperspectral imaging device, its multiparametric model, as well as design and calibration protocols used to achieve its optimal performance. The main objective of the manuscript is to describe the device and review this imaging modality, while showcasing technical caveats, models and benchmarks, in an attempt to simplify and standardize specifications, as well as to incentivize prototyping similar future designs.This research, as well as APC charges, was funded by: CIBER-BBN; MINECO (Ministerio de Economía y Competitividad) and Instituto de Salud Carlos III (ISCIII), grant numbers DTS15/00238, DTS17/00055, and TEC2016-76021-C2-2-R; Instituto de Investigación Sanitaria Valdecilla (IDIVAL), grant number INNVAL16/02; Ministry of Education, Culture and Sports, PhD grant number FPU16/05705

Mapping the natural visual world of the zebrafish (Danio rerio): from sensory input to behavioural output

Vision is one of the most crucial senses for animals to catch prey, find mates and stay alive. The tetrachromatic zebrafish (Danio rerio) is a widely used model animal in visual neuroscience with four cone photoreceptors sensitive to UV, blue, green and red light. However, a detailed understanding of how their visual system is adapted to the natural environment, and what is important for the fish to see in their shallow freshwater habitats of the Indian subcontinent, has been missing. Therefore, it also has not been possible to carefully assess the importance of different parts of the light spectrum for their natural behaviours. In this thesis I introduce a new method for natural imaging, characterise the spectral composition of zebrafish’s natural visual world and demonstrate the role of UV light in their prey capture behaviours. To characterise the light conditions in natural environments, I developed and built two hyperspectral scanners to take spectrally detailed light measurements in shallow ponds and slowly moving streams in North-East India. As expected, the spectral profile becomes increasingly monochromatic and red shifted when moving from surface to the bottom. However, the short wavelength dominated surface and long wavelength dominated bottom are separated with colour-rich horizon. These spectral statistics match rather perfectly with the cone densities and colour processing abilities of the bipolar cells in the larval zebrafish retina. Previous work has demonstrated how prey capture behaviours on larval zebrafish can be triggered by small, bright spots. The short wavelength dominated upper part of the visual field projects light from UV bright prey items perfectly to the ventro-temporal part of the retina (“strike zone”) with high density of UV cones. Finally, with my behaviour experiments I demonstrate how prey capture behaviours are strongly driven by UV bright paramecia detected with the strike zone

NASA Tech Briefs, September 2008

Topics covered include: Nanotip Carpets as Antireflection Surfaces; Nano-Engineered Catalysts for Direct Methanol Fuel Cells; Capillography of Mats of Nanofibers; Directed Growth of Carbon Nanotubes Across Gaps; High-Voltage, Asymmetric-Waveform Generator; Magic-T Junction Using Microstrip/Slotline Transitions; On-Wafer Measurement of a Silicon-Based CMOS VCO at 324 GHz; Group-III Nitride Field Emitters; HEMT Amplifiers and Equipment for their On-Wafer Testing; Thermal Spray Formation of Polymer Coatings; Improved Gas Filling and Sealing of an HC-PCF; Making More-Complex Molecules Using Superthermal Atom/Molecule Collisions; Nematic Cells for Digital Light Deflection; Improved Silica Aerogel Composite Materials; Microgravity, Mesh-Crawling Legged Robots; Advanced Active-Magnetic-Bearing Thrust- Measurement System; Thermally Actuated Hydraulic Pumps; A New, Highly Improved Two-Cycle Engine; Flexible Structural-Health-Monitoring Sheets; Alignment Pins for Assembling and Disassembling Structures; Purifying Nucleic Acids from Samples of Extremely Low Biomass; Adjustable-Viewing-Angle Endoscopic Tool for Skull Base and Brain Surgery; UV-Resistant Non-Spore-Forming Bacteria From Spacecraft-Assembly Facilities; Hard-X-Ray/Soft-Gamma-Ray Imaging Sensor Assembly for Astronomy; Simplified Modeling of Oxidation of Hydrocarbons; Near-Field Spectroscopy with Nanoparticles Deposited by AFM; Light Collimator and Monitor for a Spectroradiometer; Hyperspectral Fluorescence and Reflectance Imaging Instrument; Improving the Optical Quality Factor of the WGM Resonator; Ultra-Stable Beacon Source for Laboratory Testing of Optical Tracking; Transmissive Diffractive Optical Element Solar Concentrators; Delaying Trains of Short Light Pulses in WGM Resonators; Toward Better Modeling of Supercritical Turbulent Mixing; JPEG 2000 Encoding with Perceptual Distortion Control; Intelligent Integrated Health Management for a System of Systems; Delay Banking for Managing Air Traffic; and Spline-Based Smoothing of Airfoil Curvatures

Drones and Geographical Information Technologies in Agroecology and Organic Farming

Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies. This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license

Engineering a Low-Cost Remote Sensing Capability for Deep-Space Applications

Systems engineering (SE) has been a useful tool for providing objective processes to breaking down complex technical problems to simpler tasks, while concurrently generating metrics to provide assurance that the solution is fit-for-purpose. Tailored forms of SE have also been used by cubesat mission designers to assist in reducing risk by providing iterative feedback and key artifacts to provide managers with the evidence to adjust resources and tasking for success. Cubesat-sized spacecraft are being planned, built and in some cases, flown to provide a lower-cost entry point for deep-space exploration. This is particularly important for agencies and countries with lower space exploration budgets, where specific mission objectives can be used to develop tailored payloads within tighter constraints, while also returning useful scientific results or engineering data. In this work, a tailored SE tradespace approach was used to help determine how a 6 unit (6U) cubesat could be built from commercial-off-the-shelf (COTS)-based components and undertake remote sensing missions near Mars or near-Earth Asteroids. The primary purpose of these missions is to carry a hyperspectral sensor sensitive to 600-800nm wavelengths (hereafter defined as “red-edge”), that will investigate mineralogy characteristics commonly associated with oxidizing and hydrating environments in red-edge. Minerals of this type remain of high interest for indicators of present or past habitability for life, or active geologic processes. Implications of operating in a deep-space environment were considered as part of engineering constraints of the design, including potential reduction of available solar energy, changes in thermal environment and background radiation, and vastly increased communications distances. The engineering tradespace analysis identified realistic COTS options that could satisfy mission objectives for the 6U cubesat bus while also accommodating a reasonable degree of risk. The exception was the communication subsystem, in which case suitable capability was restricted to one particular option. This analysis was used to support an additional trade investigation into the type of sensors that would be most suitable for building the red-edge hyperspectral payload. This was in part constrained by ensuring not only that readily available COTS sensors were used, but that affordability, particularly during a geopolitical environment that was affecting component supply surety and access to manufacturing facilities, was optimized. It was found that a number of sensor options were available for designing a useful instrument, although the rapid development and life-of-type issues with COTS sensors restricted the ability to obtain useful metrics on their performance in the space environment. Additional engineering testing was conducted by constructing hyperspectral sensors using sensors popular in science, technology, engineering and mathematics (STEM) contexts. Engineering and performance metrics of the payload containing the sensors was conducted; and performance of these sensors in relevant analogous environments. A selection of materials exhibiting spectral phenomenology in the red-edge portion of the spectrum was used to produce metrics on the performance of the sensors. It was found that low-cost cameras were able to distinguish between most minerals, although they required a wider spectral range to do so. Additionally, while Raspberry Pi cameras have been popular with scientific applications, a low-cost camera without a Bayer filter markedly improved spectral sensitivity. Consideration for space-environment testing was also trialed in additional experiments using high-altitude balloons to reach the near-space environment. The sensor payloads experienced conditions approximating the surface of Mars, and results were compared with Landsat 7, a heritage Earth sensing satellite, using a popular vegetation index. The selected Raspberry Pi cameras were able to provide useful results from near-space that could be compared with space imagery. Further testing incorporated comparative analysis of custom-built sensors using readily available Raspberry Pi and astronomy cameras, and results from Mastcam and Mastcam/z instruments currently on the surface of Mars. Two sensor designs were trialed in field settings possessing Mars-analogue materials, and a subset of these materials were analysed using a laboratory-grade spectro-radiometer. Results showed the Raspberry Pi multispectral camera would be best suited for broad-scale indications of mineralogy that could be targeted by the pushbroom sensor. This sensor was found to possess a narrower spectral range than the Mastcam and Mastcam/z but was sensitive to a greater number of bands within this range. The pushbroom sensor returned data on spectral phenomenology associated with attributes of Minerals of the type found on Mars. The actual performance of the payload in appropriate conditions was important to provide critical information used to risk reduce future designs. Additionally, the successful outcomes of the trials reduced risk for their application in a deep space environment. The SE and practical performance testing conducted in this thesis could be developed further to design, build and fly a hyperspectral sensor, sensitive to red-edge wavelengths, on a deep-space cubesat mission. Such a mission could be flown at reasonable cost yet return useful scientific and engineering data

Optical MEMS

Optical microelectromechanical systems (MEMS), microoptoelectromechanical systems (MOEMS), or optical microsystems are devices or systems that interact with light through actuation or sensing at a micro- or millimeter scale. Optical MEMS have had enormous commercial success in projectors, displays, and fiberoptic communications. The best-known example is Texas Instruments’ digital micromirror devices (DMDs). The development of optical MEMS was impeded seriously by the Telecom Bubble in 2000. Fortunately, DMDs grew their market size even in that economy downturn. Meanwhile, in the last one and half decade, the optical MEMS market has been slowly but steadily recovering. During this time, the major technological change was the shift of thin-film polysilicon microstructures to single-crystal–silicon microsructures. Especially in the last few years, cloud data centers are demanding large-port optical cross connects (OXCs) and autonomous driving looks for miniature LiDAR, and virtual reality/augmented reality (VR/AR) demands tiny optical scanners. This is a new wave of opportunities for optical MEMS. Furthermore, several research institutes around the world have been developing MOEMS devices for extreme applications (very fine tailoring of light beam in terms of phase, intensity, or wavelength) and/or extreme environments (vacuum, cryogenic temperatures) for many years. Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles that focus on (1) novel design, fabrication, control, and modeling of optical MEMS devices based on all kinds of actuation/sensing mechanisms; and (2) new developments of applying optical MEMS devices of any kind in consumer electronics, optical communications, industry, biology, medicine, agriculture, physics, astronomy, space, or defense

Development and Evaluation of Unmanned Aerial Vehicles for High Throughput Phenotyping of Field-based Wheat Trials.

Growing demands for increased global yields are driving researchers to develop improved crops, capable of securing higher yields in the face of significant challenges including climate change and competition for resources. However, abilities to measure favourable physical characteristics (phenotypes) of key crops in response to these challenges is limited. For crop breeders and researchers, current abilities to phenotype field-based experiments with sufficient precision, resolution and throughput is restricting any meaningful advances in crop development. This PhD thesis presents work focused on the development and evaluation of Unmanned Aerial Vehicles (UAVs) in combination with remote sensing technologies as a solution for improved phenotyping of field-based crop experiments. Chapter 2 presents first, a review of specific target phenotypic traits within the categories of crop morphology and spectral reflectance, together with critical review of current standard measurement protocols. After reviewing phenotypic traits, focus turns to UAVs and UAV specific technologies suitable for the application of crop phenotyping, including critical evaluation of both the strengths and current limitations associated with UAV methods and technologies, highlighting specific areas for improvement. Chapter 3 presents a published paper successfully developing and evaluating Structure from Motion photogrammetry for accurate (R2 ≥ 0.93, RMSE ≤ 0.077m, and Bias ≤ -0.064m) and temporally consistent 3D reconstructions of wheat plot heights. The superior throughput achieved further facilitated measures of crop growth rate through the season; whilst very high spatial resolutions highlighted both the inter- and intra-plot variability in crop heights, something unachievable with the traditional manual ruler methods. Chapter 4 presents published work developing and evaluating modified Commercial ‘Off the Shelf’ (COTS) cameras for obtaining radiometrically calibrated imagery of canopy spectral reflectance. Specifically, development focussed on improving application of these cameras under variable illumination conditions, via application of camera exposure, vignetting, and irradiance corrections. Validation of UAV derived Normalised Difference Vegetation Index (NDVI) against a ground spectrometer from the COTS cameras (0.94 ≤ R2 ≥ 0.88) indicated successful calibration and correction of the cameras. The higher spatial resolution obtained from the COTS cameras, facilitated the assessment of the impact of background soil reflectance on derived mean Normalised Difference Vegetation Index (NDVI) measures of experimental plots, highlighting the impact of incomplete canopy on derived indices. Chapter 5 utilises the developed methods and cameras from Chapter 4 to assess the impact of nitrogen fertiliser application on the formation and senescence dynamics of canopy traits over multiple growing seasons. Quantification of changes in canopy reflectance, via NDVI, through three select trends in the wheat growth cycle were used to assess any impact of nitrogen on these periods of growth. Results showed consistent impact of zero nitrogen application on crop canopies within all three development phases. Additional results found statistically significant positive correlations between quantified phases and harvest metrics (e.g. final yield), with greatest correlations occurring within the second (Full Canopy) and third (Senescence) phases. Chapter 6 focuses on evaluation of the financial costs and throughput associated with UAVs; with specific focus on comparison to conventional methods in a real-world phenotyping scenario. A ‘cost throughput’ analysis based on real-world experiments at Rothamsted Research, provided quantitative assessment demonstrating both the financial savings (£4.11 per plot savings) and superior throughput obtained (229% faster) from implementing a UAV based phenotyping strategy to long term phenotyping of field-based experiments. Overall the methods and tools developed in this PhD thesis demonstrate UAVs combined with appropriate remote sensing tools can replicate and even surpass the precision, accuracy, cost and throughput of current strategies