Hyperspectral Imaging for Biomedical and Agricultural Diagnostics

By Owais AliReviewed by Louis CastelDec 5 2025

Hyperspectral imaging is a spectral analysis technique that captures both spatial and spectral information across narrow wavelength bands, enabling precise identification and mapping based on unique spectral signatures. Its non-invasive diagnostic capability is driving adoption in biomedical and agricultural monitoring, supporting early detection, tissue characterization, and real-time crop health assessment, with the market projected to grow at a CAGR of 19.1% by 2034.¹

Image Credit: 3d_kot/Shutterstock.com

Hyperspectral imaging captures detailed spectral information for every pixel in a scene, producing a spatial map of spectral variations. The system records data as a two-dimensional array of spectra, where each pixel contains a full spectral signature. This spectral-spatial integration generates a three-dimensional dataset, or hypercube, enabling precise identification of materials and analysis of their spatial distribution within the sample.²

How Does Hyperspectral Imaging Work?

Key Components of a Hyperspectral Imaging System

A hyperspectral imaging system consists of optical, dispersive, and detection components to meet specific spatial and spectral requirements. It includes an objective lens for spatial resolution, an imaging spectrograph for wavelength dispersion, and a two-dimensional CCD or CMOS detector to capture spatial and spectral information simultaneously.

The spectrograph functions as the central component because it projects wavelength-dependent slit images onto the detector without moving parts, ensuring precise spectral separation. This configuration functions as a hyperspectral camera, producing two-dimensional images in which each pixel contains detailed spectral information.³

Working Principle

The process begins with illuminating the sample with a broad-spectrum light source. The incident light interacts with the target through absorption, reflection, scattering, and, in some cases, fluorescence or emission. The nature of this interaction depends on the material's molecular composition, structural features, and surface characteristics.

For example, in biological tissues, chromophores such as hemoglobin, water, and lipids absorb and scatter light at specific wavelengths, producing spectral features that reflect tissue oxygenation, hydration, or pathology. Non-biological materials exhibit unique spectral responses that depend on chemical bonds, moisture content, surface texture, or contamination.

After interaction, the light is collected and directed through an optical system that includes lenses to focus the scene and a dispersive element such as a prism, diffraction grating, or tunable filter. This element separates the incoming light into narrow, contiguous spectral bands, effectively resolving the continuous spectrum into discrete channels.

The spectrally separated light is then projected onto a two-dimensional detector array (CCD or CMOS), where one axis encodes spatial information, and the other encodes spectral information. The resulting data forms a three-dimensional hyperspectral cube, with two spatial dimensions (x, y) and one spectral dimension (λ). Each slice of the cube corresponds to a narrow wavelength band, while each pixel along the spatial axes contains a full spectrum.

Once the hyperspectral cube is acquired, advanced computational methods are applied to extract meaningful information. Techniques such as spectral unmixing, classification algorithms, and machine learning models analyze the spectral signatures at each pixel to identify materials, detect anomalies, or quantify chemical and physical properties.

This enables precise mapping, monitoring, and diagnostic assessments across diverse applications, from biomedical imaging and agriculture to environmental monitoring and industrial inspection.^4,5

Biomedical Applications: Non-Invasive, Real-Time Diagnostics

Hyperspectral imaging has enabled non-invasive, label-free analysis of biological tissues by capturing both compositional and structural information across a wide range of wavelengths. This capability allows detailed characterization of physiological and pathological changes, making it valuable for diagnostics, surgical guidance, and biomedical research.

Dermatology

In dermatology, hyperspectral imaging enables detailed assessment of skin composition and lesion characterization by capturing reflectance spectra across visible and near-infrared wavelengths. This approach allows detection of variations in chromophores such as hemoglobin and melanin, facilitating early identification of vascular or pigment-related abnormalities.

A study published in the IEEE Transactions on Medical Imaging applied polarization-enhanced hyperspectral imaging combined with neural networks to assess skin complications in diabetic patients. Using a snapshot hyperspectral camera spanning 510-900 nm with 6-10 nm spectral resolution, two-dimensional maps of blood volume fraction and skin blood oxygenation revealed elevated blood volume and reduced oxygenation, indicative of microcirculation impairments.⁶

Cancer Detection and Monitoring

Hyperspectral imaging also enables non-invasive differentiation between malignant and healthy tissues, supporting cancer diagnosis and intraoperative guidance. In one study, the TIVITA® Tissue system (500-1000?nm, 640 × 480 pixels) combined with a feedforward neural network distinguished cancerous colorectal mucosa from healthy tissue in 54 patients, achieving 86% sensitivity and 95% specificity.

These findings demonstrate this technology’s capability to detect variations in tissue perfusion associated with angiogenesis, highlighting its potential for reliable, non-invasive colorectal cancer diagnosis.⁷

Ophthalmology

Advanced tissue and cellular studies leverage hyperspectral imaging for its combined spectral and spatial resolution. In retinal research, the HERA VNIR hyperspectral camera (NIREOS), paired with a microscope, enabled mapping and quantification of oil droplets in avian retinas.

Spectral data captured from 400-700?nm, analyzed with automated classification algorithms such as the Spectral Angle Mapper, differentiated red, yellow, and pale green droplets corresponding to specific photoreceptor types. This approach provided quantitative insights into retinal structure and visual function, supporting comparative vision research and detailed tissue analysis.⁸

Agricultural Applications: Plant Health, Food Safety, and Precision Farming

Hyperspectral imaging is increasingly applied in agriculture to support plant health monitoring, food safety, and precision farming.

Early Detection of Plant Diseases

Hyperspectral imaging can detect plant diseases before visible symptoms emerge by capturing subtle variations in leaf reflectance across visible and near-infrared wavelengths. Such spectral changes help investigate stress or infection from pathogens, water deficit, or nutrient deficiencies. 

A study by Shi et al. implemented a deep learning-based HSI model, CropdocNet, to detect potato late blight disease in Canada. The model analyzed hyperspectral images to distinguish healthy from infected tissue, achieving 98.09% accuracy on the validation data and demonstrating HSI’s potential for rapid, non-invasive disease diagnosis.⁹

Monitoring Crop Nutrient Levels

Crop health can be quantitatively assessed through spectral signatures associated with chlorophyll content, water status, and nitrogen levels. This enables precision fertilization and early correction of nutrient deficiencies.

A study published in PLOS ONE applied hyperspectral imaging to estimate chlorophyll content in japonica rice canopies using the successive projection algorithm for inversion modeling. The study achieved an R² of 79.1% and RMSE of 8.215 mg/L, enabling accurate assessment of crop nutrient status, early detection of deficiencies, and optimization of fertilization strategies for improved yield and plant health.¹⁰

Assessing Fruit Ripeness and Detecting Contamination

Hyperspectral imaging facilitates non-destructive assessment of fruit ripeness and quality by detecting changes in pigments, sugar content, and surface characteristics across spectral bands. It also helps identify foreign contamination, bruising, or disease on fruits and vegetables, improving post-harvest management and food safety.

A study used ground-based hyperspectral imaging to estimate mango yield and quality parameters in Spain. The researchers achieved high determination coefficients for various yield measures, demonstrating reliable non-destructive evaluation of fruit development, ripeness, and potential contamination.¹¹

Field-Scale and Precision Farming Implementation

Industry leaders such as Cubert, IMEC, and Specim are actively developing hyperspectral solutions for agriculture.

The Cubert ULTRIS X20, a light-field snapshot camera covering 350-1000?nm with 164 spectral bands, offers real-time “video” HSI (~8?Hz) for drone-based crop monitoring and rapid quality inspection in dynamic environments.¹²

IMEC produces compact CMOS-based cameras with integrated spectral filter arrays, enabling snapshot imaging across visible to SWIR wavelengths for water content analysis, nutrient monitoring, and soil assessment in scalable field deployments.¹³

Advantages Over Traditional Imaging or Sensing Methods

Hyperspectral imaging offers non-invasive, non-contact, and non-destructive analysis, preserving sample integrity while avoiding the use of chemical reagents, making it environmentally safe. It reduces processing times for quality assessment and monitoring compared to traditional chemical or visual inspection methods.

Unlike conventional imaging, which is limited to primary colors, hyperspectral imaging captures hundreds to thousands of spectral bands, producing a continuous spectral profile for each pixel and allowing detailed characterization of chemical and physical properties.^2,14

Current Limitations and Barriers to Adoption

Despite its considerable advantages, hyperspectral imaging faces several limitations that hinder widespread adoption. The technology requires complex, expensive equipment, including cameras, spectrographs, light sources, and data-processing platforms, making it largely inaccessible to smaller institutions or low-resource settings.

In addition, the large volume of hyperspectral data requires significant computational power, storage, and memory, while spectral redundancy can complicate feature extraction and analysis.

It is also sensitive to environmental factors such as illumination changes, atmospheric conditions, and motion, often necessitating rigorous calibration and correction to maintain data accuracy.  As a result, achieving high spatial and spectral resolution simultaneously remains challenging, and slow acquisition speeds can lead to motion artifacts in dynamic scenarios, affecting applications in medicine, agriculture, and surveillance.

These factors, combined with the need for skilled personnel, the size and complexity of the system, and integration challenges within existing workflows, currently confine hyperspectral imaging primarily to controlled laboratory or industrial environments.^2,14

Future Outlook and Industry Potential

Recent advances indicate that AI integration with hyperspectral imaging will significantly enhance automated diagnostics and real-time data analysis across multiple sectors.

AI techniques such as lightweight neural networks, model pruning, and deep neural networks will accelerate processing, reduce computational requirements, and maintain high accuracy, enabling actionable insights from large hyperspectral datasets in medical diagnostics, environmental monitoring, and quality control.

Adaptive acquisition and data analysis (AADA) strategies will further improve imaging efficiency in dynamic scenes by prioritizing the most informative regions, minimizing motion artifacts, and optimizing scanning processes.

The development of portable HSI systems, including miniaturized and chip-based sensors, is expected to expand field deployment and accessibility for diverse applications. When combined with AI-driven analytics, these devices will enable remote diagnostics in agriculture, environmental monitoring, and healthcare.

In addition, advances in generative adversarial networks (GANs) and sparse tensor networks will enhance data quality by reducing noise, enhancing images, and enabling high-speed, efficient processing.

Over the next decade, the field is expected to advance toward widespread adoption of portable, real-time systems, enabling scalable, non-invasive, high-throughput imaging solutions and unlocking new opportunities in precision agriculture, healthcare diagnostics, and environmental monitoring.¹⁴

But then, what's multispectral imaging? Find out here

References and Further Reading

1. GMI. (2024). Hyperspectral Imaging Systems Market Size - By Component, By Technology, By Spectrum Range, By Application and Forecast, 2025 - 2034. Global Market Insights Inc. https://www.gminsights.com/industry-analysis/hyperspectral-imaging-systems-market
2. Bhargava, A., Sachdeva, A., Sharma, K., Alsharif, M. H., Uthansakul, P., & Uthansakul, M. (2024). Hyperspectral imaging and its applications: A review. Heliyon, 10(12), e33208. https://doi.org/10.1016/j.heliyon.2024.e33208
3. Kavitha Reddy Gurrala. (2021). Hyperspectral Imaging for Food Quality Assessment. International Research Journal of Engineering Science, Technology and Innovation, 7(1), 1-24. https://www.interesjournals.org/abstract/hyperspectral-imaging-for-food-quality-assessment-65276.html
4. ElMasry, G., & Sun, D.-W. (2010). Principles of Hyperspectral Imaging Technology. Hyperspectral Imaging for Food Quality Analysis and Control, 3-43. https://doi.org/10.1016/b978-0-12-374753-2.10001-2
5. ‌PhotonicScience. (2020). Hyperspectral Imaging: Principles & Applications. https://photonicscience.com/hyperspectral-imaging-principles-applications/
6. Dremin, V., Marcinkevics, Z., Zherebtsov, E., Popov, A., Grabovskis, A., Kronberga, H., Geldnere, K., Doronin, A., Meglinski, I., & Bykov, A. (2021). Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning. IEEE Transactions on Medical Imaging, 40(4), 1207-1216. https://doi.org/10.1109/tmi.2021.3049591
7. Jansen-Winkeln, B., Barberio, M., Chalopin, C., Katrin Schierle, Diana, M., Hannes Köhler, Gockel, I., & Maktabi, M. (2021). Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy. Cancers, 13(5), 967-967. https://doi.org/10.3390/cancers13050967
8. Nireos. (2025). Hyperspectral microscopy for avian retina analysis. https://nireos.com/application/hyperspectral-microscopy-for-avian-retina-analysis/?_gl=1*1hhonf7*_up*MQ..*_ga*OTY2MzkwOTUyLjE3NjQ2NTE5NzA.*_ga_V160CRDDR9*czE3NjQ2NTE5NjckbzEkZzAkdDE3NjQ2NTE5NjckajYwJGwwJGgw
9. Shi, Y., Han, L., Kleerekoper, A., Chang, S., & Hu, T. (2021). Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery. Remote Sensing, 14(2), 396. https://doi.org/10.3390/rs14020396
10. Cao, Y., Jiang, K., Wu, J., Yu, F., Du, W., & Xu, T. (2020). Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing. PLOS ONE, 15(9), e0238530. https://doi.org/10.1371/journal.pone.0238530
11. Gutiérrez, S., Wendel, A., & Underwood, J. (2019). Ground based hyperspectral imaging for extensive mango yield estimation. Computers and Electronics in Agriculture, 157, 126-135. https://doi.org/10.1016/j.compag.2018.12.041
12. Cubert. (2025). Ultris X20 - The World’s First UV-VIS-NIR Hyperspectral Video Camera. https://cubert-hyperspectral.com/en/ultris-x20/
13. IMEC. (2025). Hyperspectral imaging for agriculture - From visible to SWIR, and beyond. https://www.imechyperspectral.com/en/applications/hyperspectral-imaging-agriculture
14. Cheng, M., Mukundan, A., Karmakar, R., Valappil, M. A., Jouhar, J., & Wang, H. (2025). Modern Trends and Recent Applications of Hyperspectral Imaging: A Review. Technologies, 13(5), 170. https://doi.org/10.3390/technologies13050170

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