In agriculture, farmers and agronomists need to recognise crop stress, nutrient shortfalls, and drought damage early in the growing season, often before anything is apparent to the naked eye. Failure to spot early warning signs such as fungal disease and pest infestations can lead to devastating consequences, with compromised harvests, wasted resources and, for smaller operations, entire livelihoods at risk. This is where multispectral imaging in agriculture is transforming the way growers protect crop health.
Some of today’s AI-driven multispectral imaging systems can achieve 81-95% accuracy in identifying infections a full two to three weeks before visible symptoms appear to the naked eye. Not only does this help protect yields, but it also means fewer pesticides are needed, leading to more eco-friendly and safer farming practices.
These systems functions by using different spectral bands – usually red-edge for early distress and near-infrared (NIR) for photosynthetic activity – to detect everything from chlorophyll levels to water stress and nutrient deficiencies. Because plants reflect and absorb light differently depending on their condition, the multispectral camera captures this reflected light data across multiple bands.
The Optical Components Supporting the Data
Optical setups in a multispectral device are often mounted on drones for their coverage, speed, and higher resolution than satellite alternatives, and combine several high-performance optics, each engineered for specific wavelengths to enable simultaneous spectral data capture.
Operationally, narrowband interference bandpass filters are the most important. They enable these cameras to isolate the exact wavebands the system needs, such as red-edge and NIR. However, quality can directly affect the reliability of multispectral readings, and substandard bandpass filters often allow out-of-band leakage, resulting in corrupted data. When combined with operational conditions, like direct sunlight, reflections off wet foliage, and bright blue skies, poor-quality filters can introduce enough noise to render index maps unfit for agricultural decision-making.
The way filters are integrated into a multispectral camera affects both performance and practicality. Filter wheels, which rotate discrete filters in front of a single sensor to capture each band sequentially, are one approach, though their added weight and mechanical complexity make them less suited to drone applications. Meanwhile, mosaic or micropatterned filters (available through our parent company, Torrent Photonics) offer a more compact alternative. With multiple bandpass filters patterned onto a single substrate, they enable simultaneous capture across all spectral bands while remaining lightweight, making them particularly well-suited to weight-sensitive platforms such as drone-based crop monitoring.
Lenses must be capable of focusing both visible and NIR light onto the same sensor plane without causing focus shift. Environmental challenges inthe field, for instance, heat haze, dust particles and humidity, place further demands on focus stability and consistency. Here, high-quality optical lenses corrected for visible and NIR wavelengths maintain their integrity across all bands, which is critical for drones operating at altitude in fluctuating lighting and temperature conditions.
As the front aperture of a multispectral device, windows shield all these optics and the sensor assembly from harsh operating environments, and they must offer broad transmission to ensure the system works as intended. Dust accumulation and condensation are environmental threats these optics face, with window quality and optical coatings affecting practical operation.
Machine Vision & Scale
The findings gathered through the optical assembly are typically processed into an index map that users can interpret, commonly, the normalised difference vegetation index (NDVI). This data – alongside other vegetation indices – feeds into larger-scale platforms that use machine vision algorithms to identify problem areas and generate variable-rate application maps specifying where to apply fertiliser and pesticide, and in what quantities. Automated agricultural equipment such as tractors and sprayers can then analyse these maps, adjusting output in real-time as they move across farmland. Collectively, this precision farming enables season-long growth monitoring and yield prediction, significantly reducing the likelihood of crop loss, minimising waste, and crucially, supporting sustainable farming at scale.
For multispectral imaging in agriculture to deliver reliable results, the optics have to perform consistently in demanding field settings. For more information on optical components for multispectral imaging, contact a member of our team today.