Combining hyperspectral cameras and artificial intelligence It is completely changing the way we see and analyze the material world. It's no longer just about getting pretty pictures, but about discovering chemical and structural information that is literally invisible to the human eye and traditional cameras.
Thanks to miniaturized optoelectronic sensorsWith integrated-chip spectrometers and increasingly powerful AI algorithms, it is possible detect defects, classify materials, analyze crops or guide surgeries in real time and with a precision that just a few years ago sounded like science fiction. And the best part: many of these systems are starting to leave the laboratory and be integrated into real production lines, farms, or operating rooms.
What is a hyperspectral camera with artificial intelligence?
A hyperspectral camera is, in essence, an imaging system that captures tens or hundreds of spectral bands For each point in the scene, typically from the visible to the near-infrared, with typical resolutions between 4 and 10 nm depending on the sensor design. While an RGB camera assigns three color values ​​per pixel, a hyperspectral system generates a complete spectral vector per pixel, allowing materials to be identified by their unique spectral signature.
When this type of camera is fitted with a module of artificial intelligence capable of selecting, interpreting and classifying This information yields an extremely powerful analytical tool: it is possible to recognize chemical, structural or organoleptic properties without contact and in a non-invasive way, with automated results that can be directly integrated into industrial, agricultural or clinical processes.
Projects like OASYS, developed in centers of reference in photonic microsystems, rely on advanced optoelectronic sensors and integrated spectrometers These devices record the spectral characteristics of each point on the inspected product or material. This reveals chemical properties impossible to perceive with conventional cameras, facilitating the early detection of defects in food, plastics, or textiles.
Until recently, the main obstacles to these technologies were their size, technical complexity, and cost. AI-based compact camerasCombined with very low power on-chip spectrometers, this changes the scenario and opens the door to solutions that can be integrated into production machines, mobile systems, and even handheld devices.
One particularly interesting approach is that proposed by the Fraunhofer Institute for Photonic Microsystems, which combines a conventional 2D camera with a spectral sensor and an AI moduleThe system first acquires a classic image of the scene, identifies the relevant regions using computer vision algorithms, and only applies detailed hyperspectral analysis to those regions. This minimizes the data volume without sacrificing chemical accuracy.
Data reduction and technical advantages of applied AI
One of the biggest barriers to the widespread use of hyperspectral imaging is the enormous amount of data generated by a hypercube (the three-dimensional representation that combines width, height, and wavelengths). Spectrally analyzing each pixel of an entire scene can be prohibitively expensive in terms of storage, processing, and energy consumption if real-time operation is desired.
Hybrid systems that integrate AI overcome this limitation through a intelligent selection of regions of interestIn typical industrial applications, this approach allows for a 70-85% reduction in spectral data volume compared to a full-scan system. In other words, hyperspectral detail is captured only where needed, and irrelevant details are ignored.
This data reduction translates directly into less memory required, less energy consumption and processing times compatible with production lines that can handle several dozen objects per second. AI acts as a pre-filter and decision-making brain, evaluating whether a product meets specifications or whether a material belongs to a particular category.
At a quantitative level, when fine spectral resolutions are combined with classification models based on neural networks or other machine learning algorithms, the following can be achieved success rates exceeding 95% in material identification tasks, provided that the dataset used for training accurately represents the actual variations in the work environment.
Furthermore, the miniaturization of spectrometers, with active surfaces smaller than 1 mm² and spectral resolutions below 10 nm, allows for the design of much more compact cameras. Some AI-assisted on-chip spectrometer prototypes, described in scientific literature published on platforms such as arXiv, are already capable of operating at wavelengths close to 1100 nm. consumption below 100 mW, which makes them suitable for portable devices or battery-powered systems.
This advancement not only reduces size and power consumption but also improves the overall robustness by eliminating bulky optical elements and moving parts. Modular architectures, widely used in industrial machine vision manufacturers' catalogs, facilitate the adaptation of these intelligent hyperspectral modules to different productive sectors with minimal changes.
Industrial quality control: food, plastics and textiles
One of the fields where the potential of these solutions is most clearly seen is the automated quality control in the industryIn the food industry, for example, hyperspectral imaging makes it possible to detect internal damage, alterations in composition, or surface contamination that are not evident in a conventional photograph.
By analyzing the spectral signature of each point on the food, a trained AI algorithm can Identify defective products, foreign bodies, or inadequate ripening levelsThis helps reduce both unnecessary waste and the risk of non-conforming products reaching the consumer, while at the same time maintaining high inspection speeds on the packaging line.
In sectors such as plastics and textiles, compact hyperspectral cameras with AI have the ability to recognize specific types of polymers or mixtures of materials that visually appear identical. This is critical in automated recycling processes, where correctly separating the different materials directly influences the quality of the final product and the profitability of the process.
Spectral discrimination allows for the differentiation of, for example, plastics that share color and texture but belong to different chemical families. Through trained classification models, AI can automatically direct materials to the appropriate recycling stream or reject those that do not meet the requirements, without the need for physical markers or additional labeling.
In the case of textiles, this technology makes it easier to detect unwanted fiber mixtures, surface treatments, or contaminants that can affect the quality, durability, or fire resistance of a fabric. Optoelectronic sensors developed in projects like OASYS, along with integrated spectrometers, allow for the recording of detailed spectral information even during rapid movements, which is crucial in fabric production lines or finishing processes.
The industrial ecosystem is already beginning to incorporate these systems into technical catalogs and commercial solutions. Manufacturers specializing in machine vision and companies focused on connectivity and industrial electronics, such as Phoenix Contact with its portfolio of connectors geared towards automated SMT assembly, are aligning their products so that Hyperspectral cameras are integrated into industrial data networks in a simple and standardized way.
Precision agriculture and projects like SCOUT
Agriculture is another area where the combination of hyperspectral imaging and AI is making a qualitative leap. The spectral signatures of plants allow for the evaluation of their characteristics. physiological state, water stress, nutritional deficiencies or the presence of diseases long before symptoms are visible to the naked eye.
A notable example is the SCOUT project, which focuses on the analysis of table olives using multispectral signaturesFor this application, a prototype handheld device is used, designed to be operated by a single person, equipped with sensors that cover the range of 400 to 1000 nm, encompassing the bands relevant to the study of the characteristics of the fruit.
This portable device focuses, after a preliminary analysis, on those wavelengths that show the greatest correlation with the parameters to be measured, such as degree of maturity or certain organoleptic propertiesBy working only with the most significant bands, the size and cost of the equipment are drastically reduced, while maintaining a very high level of functionality for the farmer.
In addition to the handheld device, SCOUT incorporates a mobile system that collects multispectral images from a onboard camera on a zip line about the plantation. These images allow analysis of the crop context, extracting information about greenness, vegetation density and the overall health of the olive grove using indices such as NDVI (normalized difference vegetation index).
The information collected by both systems—the handheld and the mobile—is processed using artificial intelligence techniques based on deep neural networks (Deep Learning)The models are trained with labeled field data, so that, once deployed, they can offer expert recommendations to the farmer: optimal harvest time, plot zoning, early detection of problems, or yield estimates.
This architecture gives rise to an authentic cyber-physical system oriented towards agronomic decision-makingwhere sensors, AI algorithms, and digital platforms interact continuously. The European Union promotes initiatives of this kind through funds such as Next Generation EU, which finance R&D projects in artificial intelligence and digital technologies integrated into agricultural and agro-industrial value chains.
Digital platforms, web interface and mobile app
For AI-powered hyperspectral cameras to be truly useful outside the lab, hardware and algorithms alone are not enough; it is essential to have digital platforms that present the results clearly to technicians, operators, or farmers. In the case of SCOUT and similar systems, the software architecture relies on a web application and a mobile app that act as the visible face of the system.
The web application works as main control and analysis panelIt is accessed through a web browser and offers everything from data and map visualization to the configuration of business parameters relevant to each user (e.g., quality thresholds, batch definition, or classification criteria). This interface communicates dynamically with a REST API that centralizes information exchange with the backend layer and field devices.
The mobile app, for its part, has a dual function: on the one hand, it allows consult key information on the status of crops or plots at any time and place; on the other hand, it is the tool that mediates between the handheld device and the cloud, using the Bluetooth connectivity of the terminal in which it is installed to synchronize field data.
This type of architecture makes it possible for both technical staff and end users without an advanced technological profile to interact with complex AI models without needing in-depth knowledge in hyperspectral imaging or deep learning algorithms. Simply capture the data following a guided workflow and let the system process, analyze, and present results in an understandable language.
In industrial settings, the same philosophy is applied through integrations with MES, ERP, or SCADA monitoring platforms, where intelligent hyperspectral cameras become another node in the industrial network. Compatibility with common communication standards simplifies the process. classification or measurement results are integrated into automated decisions of rejection, product rerouting or adjustment of process parameters.
Chemical imaging and interpretation of hypercubes
One of the most interesting approaches to translating hyperspectral data into something intuitive for the user is the so-called chemical imageCompanies specializing in hyperspectral vision, such as INSPECTRA with its CHP® (Chemical image Processing) technology, combine machine vision with infrared spectroscopy to generate RGB images where each color represents relevant chemical information.
The concept of chemical imaging starts from a hypercube in which each voxel (the three-dimensional equivalent of the pixel, adding the spectral axis) has an associated unique spectral signatureUsing interpretation software, models are trained to relate these signatures to specific classes of materials, contaminants, or parameters of interest. From there, an RGB image is constructed in which, for example, green tones are assigned to products that are to be accepted and red tones to contaminants or foreign bodies.
The great advantage of this approach is that, starting from the same hypercube, one can generate multiple chemical images in parallelEach one is designed to highlight a different qualitative parameter: type of material, presence of defects, moisture distribution, etc. This multiplies the value of hyperspectral acquisition, since several layers of visually interpretable information are extracted from a single scan.
CHP® technology illustrates well how AI is integrated into the workflow: the models that convert spectral data into chemical images are not static, but are adjusted and retrained to adapt to new products, changes in the process, or variations in lighting conditionsIn this way, the system maintains its reliability over time and can be deployed on different lines without needing to completely redesign the hardware.
This ability to translate complex information into simple visualizations is also crucial in medical settings, where surgeons need to make quick decisions on the fly. In that context, having a tumor area or healthy tissue appear color-coded in real time can make all the difference in the surgical strategy.
Medical applications: hyperspectral imaging in surgery
In the healthcare field, the capture, processing, and visualization of hyperspectral images are becoming established as a non-invasive support tool in surgical proceduresOne particularly relevant case is its use in neurosurgery to delineate brain tumors during the procedure.
Using mobile cameras that cover ranges such as 665-975 nm, with around 25 spectral bands and spatial resolutions on the order of 2045 x 1085 pixels (equivalent to 409 x 217 in specific configurations), it is possible to acquire hypercubes of the operating field in near real time. Each of the bands collects different information about the interaction of light with tissuesThis allows us to distinguish between tumor tissue, healthy tissue, and critical structures.
The analysis algorithms process these hypercubes and generate maps that help the surgeon to to precisely define the tumor marginsThis reduces the volume of healthy tissue removed and minimizes subsequent complications. By improving the definition of tumor margins, both surgery and recovery times can be reduced, and the likelihood of relapse decreased.
In practice, this technology contributes to a tangible improvement in patient quality of lifeby allowing more conservative interventions without sacrificing oncological efficacy. Furthermore, its application is being explored in other medical fields, such as vascular surgery, dermatology, and wound monitoring, where the spectral differences between healthy and altered tissue offer invaluable information.
The convergence of hyperspectral imaging, AI, and surgical guidance systems presents a scenario in which the operating room becomes a highly digitized environment, where the surgeon has access to layers of functional and chemical information superimposed on the conventional image, facilitating difficult decisions that until now were largely based on subjective experience.
Overall, hyperspectral cameras with artificial intelligence are transitioning from niche tools to key players in industries as diverse as food, textiles, recycling, precision agriculture, and advanced medicine. Their reduced size and power consumption, the use of AI models capable of selecting and analyzing only relevant information, and the emergence of user-friendly digital platforms are making their implementation in real-world environments increasingly feasible, offering a level of control, traceability, and efficiency that perfectly aligns with the demands of today's industry and society.
