How an Open-Source Imager Is Revolutionizing Science
Imagine looking at a forest and seeing not just trees, but their health status, water content, and species composition in vibrant, detailed colors.
This isn't a superpower—it's the revolutionary capability of hyperspectral imaging, a technology that has now been democratized through an ingenious open-source design.
Captures only red, green, and blue wavelengths like human vision.
For decades, scientists have relied on hyperspectral imaging to capture data far beyond human vision. Until recently, this powerful technology came with a formidable price tag, often exceeding £20,000 for commercial systems 4 . That all changed with the development of the Hyperspectral Open-Source Imager (HOSI), a groundbreaking system that costs around £350—less than 2% of traditional equipment costs 4 9 .
Cost Reduction
Every material interacts with light in unique ways, absorbing some wavelengths and reflecting others to create what scientists call a "spectral signature." Where human eyes and conventional cameras see only broad color categories, hyperspectral imaging detects these precise signatures across hundreds of narrow wavelength bands 3 .
Hyperspectral imaging captures data across the full electromagnetic spectrum
This enables identification of materials based on their chemical composition rather than just their visible appearance. Think of it like this: where a standard camera might see a "green leaf," a hyperspectral imager can distinguish between a healthy leaf and a diseased one, detect water stress levels, and even identify specific nutrient deficiencies 1 6 .
The high cost of commercial hyperspectral systems hasn't been the only barrier to widespread adoption. Traditional systems also tend to be bulky, complex to operate, and limited in their ability to handle high-contrast scenes 4 .
The HOSI system addresses these limitations through an ingenious redesign that combines off-the-shelf components with 3D-printed parts 4 . This makes it particularly valuable for researching artificial light at night (ALAN), a growing threat to global biodiversity 4 .
Artificial light at night has increased more than fourfold over the past 18 years, creating novel challenges for ecosystems worldwide 4 . Different animal species have varying visual sensitivities, and the impact of artificial light depends heavily on its spectral composition and spatial distribution.
Traditional satellite-based measurements miss crucial aspects of this light pollution, particularly horizontally propagated light and atmospheric effects like skyglow 4 . Understanding ALAN's true impact requires detailed spectral data collected at ground level across wide areas—exactly the challenge HOSI was designed to address.
Artificial light at night has increased more than fourfold over the past 18 years 4 .
The HOSI system operates on what's known as a "whisk broom" principle, building images point by point with a motorized gimbal that moves a compact Hamamatsu C12880MA micro-spectrometer across a scene 4 .
The portable HOSI unit is positioned on a stable surface, with its operation controlled through a graphical user interface on a connected computer or smartphone 4 .
The researcher defines the scan area and resolution, with the option to capture full panoramic images by combining multiple measurement points 4 .
The motorized gimbal moves the spectrometer through each predetermined point in the scene. At each position, the system captures the complete spectral data from 320–880 nm with a spectral resolution of approximately 9 nm (FWHM) 4 9 .
The raw data is converted to absolute radiance values using calibration factors determined through the system's calibration process 4 .
The individual spectral measurements are compiled into a hyperspectral "data cube"—a three-dimensional representation with two spatial dimensions and one spectral dimension 4 .
In validation tests, HOSI demonstrated impressive accuracy, with mean absolute errors of just 1.99% for radiance measurements and 2.5% for reflectance compared to professional-grade instruments 4 .
| Parameter | Specification | Significance |
|---|---|---|
| Cost | ~£350 | Makes hyperspectral imaging accessible to researchers, educators, and citizen scientists |
| Spectral Range | 320–880 nm | Covers ultraviolet, human-visible, and near-infrared wavelengths |
| Spectral Resolution | ~9 nm (FWHM) | Sufficient to distinguish fine spectral features of different light sources and materials |
| Minimum Light Sensitivity | 0.001 cd.m⁻² | Enables measurement in low-light conditions like moonlit environments |
| Dynamic Range | >50,000:1 | Allows capture of scenes with both very bright and very dark areas |
| Spatial Resolution | ~2 cycles per degree | Detailed enough for environmental mapping and light distribution analysis |
The implications of affordable, accessible hyperspectral imaging extend far beyond ALAN research. The same technology that maps light pollution can also transform fields from agriculture to medicine.
Hyperspectral imaging has proven invaluable for forest classification, soil analysis, and water quality assessment. One study noted that hyperspectral satellites improved forest classification accuracy by up to 50% compared to conventional methods 1 .
The technology can also detect marine plastic waste with 70-80% accuracy and map soil organic matter content, providing crucial data for combating pollution and managing natural resources 1 .
In agriculture, hyperspectral imaging enables early detection of crop diseases before visible symptoms appear. The HSI-TransUNet model achieved 98.09% accuracy in detecting crop diseases and 86.05% in classification, potentially revolutionizing precision farming 1 .
For food quality assessment, the technology has predicted egg freshness with an R² of 0.91 and achieved perfect 100% accuracy in pine nut quality classification 1 .
Perhaps one of the most impactful applications is in healthcare, where hyperspectral imaging can differentiate between healthy and cancerous tissues with high sensitivity and specificity—87% and 88% for skin cancer, and 86% and 95% for colorectal cancer detection 1 .
The non-invasive, label-free nature of the technology makes it particularly suitable for clinical use and real-time decision support during surgeries 1 .
| Field | Application | Reported Effectiveness |
|---|---|---|
| Healthcare | Cancer detection (skin, colorectal) | 87% sensitivity, 88% specificity (skin); 86% sensitivity, 95% specificity (colorectal) |
| Agriculture | Crop disease detection | 98.09% accuracy in detection, 86.05% in classification |
| Food Safety | Egg freshness prediction | R² = 0.91 |
| Environmental Monitoring | Marine plastic detection | 70-80% accuracy |
| Pharmaceutical | Counterfeit drug detection | Accurate identification of fake anti-malarial tablets |
| Waste Management | Material identification for recycling | Robust characterization of complex multi-material objects |
Building a HOSI system requires both hardware components and software tools, all designed to be accessible and modifiable by the research community.
| Component | Function | Specifics in HOSI |
|---|---|---|
| Micro-spectrometer | Captures detailed spectral information at each point | Hamamatsu C12880MA spectrometer |
| Motorized Gimbal | Precisely positions the spectrometer for spatial scanning | Custom system with stepper motors for 2-axis control |
| Control Electronics | Coordinates system operation and data collection | Arduino-based controller with custom firmware |
| Structural Components | Houses and supports all system elements | 3D-printed parts designed for off-the-shelf components |
| Calibration Materials | Converts raw data to absolute radiance values | Spectralon standards for reflectance calibration |
| Software Interface | Controls operation and processes data | Graphical user interface compatible with computers and smartphones |
The complete design, including all code, calibration data, and 3D printing files, is available through a GitHub repository and Zenodo archive, ensuring that researchers worldwide can not only use but also modify and improve the system 4 .
The development of the Hyperspectral Open-Source Imager represents more than just a technical achievement—it demonstrates how open-source principles can democratize powerful technologies that were previously accessible only to well-funded institutions.
By reducing the cost of hyperspectral imaging from tens of thousands to hundreds of pounds, HOSI opens doors for researchers, educators, and even citizen scientists to explore the spectral world in ways previously unimaginable.
As artificial intelligence and machine learning continue to advance, the combination of these technologies with accessible hyperspectral imaging promises even greater breakthroughs. Researchers are already developing AI-driven analysis techniques that can automatically interpret complex hyperspectral data, identifying patterns and making predictions that would challenge human analysts 1 2 .
Open-source tools like HOSI make advanced research accessible to all
The invisible world of spectral information surrounds us every moment, filled with insights about our health, our food, and our environment. Thanks to open-source innovations like HOSI, we're all gaining the ability to see this hidden dimension—and what we're discovering promises to transform our understanding of the world around us.