 and operate at high speeds; compact computers provide sufficient computing capability to handle high data volumes; and modern machine learning algorithms can use hyperspectral data to accurately identify materials, even those that appear quite similar. The technology is advancing rapidly in both research and industrial applications. Its usefulness is based on the fact that different materials have different reflectance spectra.
Applications — Agriculture
In agriculture, different plant species have different spectra, as do the same plant species under different conditions. For example, hyperspectral data can be used to alert farmers to early signs of crop stress or noxious weed infestations, as well as to determine ripeness. In precision agriculture and environmental monitoring the technology is increasingly viable for commercial applications, and will expand as additional obstacles are overcome. Determining accurate relationships between spectral features and plant physiologies, for example, is a focus of much research worldwide. This will enable site-specific applications of herbicides, pesticides, and fertilizers. Ultimately, such targeted crop management techniques will allow farmers to use fewer chemicals more effectively and produce greater yields. Thus, there is strong institutional support for hyper-spectral technologies, especially in Asia where food supply and environmental protection are critical issues.
Applications — Industry
Because factory environments can be controlled better than outdoor farmlands and forests, hyperspectral imaging is gaining traction for industrial sorting applications. Conventional vision systems often fail to sort items that have similar colors or appearance. Two examples of difficult applications for traditional color sorting systems, are foods with similar-colored shells or peels, and materials whose composition is unrelated to their visible color, such as recycled plastics. When standard vision systems fail, these sorting tasks fall to humans, processes that are costly, slow, and prone to mistakes.
Not only can hyperspectral data be used to distinguish differences between similarly colored materials, they can even access information outside the visible range — both infrared and ultraviolet. Real-time machine learning algorithms can process the data in seconds and then transmit the information to actuators such as robotic arms, air-jets, and mechanical flippers to complete the sort.
While designing and manufacturing the cameras is technically challenging, producing reliable hyperspectral analysis software for industrial sorting is both a barrier and an opportunity. Because widely used simple algorithms exist alongside a wealth of knowledge about deep machine learning, hyperspectral machine vision software development is an exciting field.
Traditional methods of analyzing spectral imaging data use the concept of “vegetation indices” or more generally just “indices,” colloquially called “band math”. These indices are scalar quantities associated with each pixel, calculated via simple algebraic combinations of measured intensities of a few specific spectral bands. For example, a historically venerable vegetation index is the Normalized Difference Vegetation Index (NDVI), which dates from the early days of multispectral remote sensing. Given a full reflectance spectrum at each pixel, the value of NDVI can be calculated at each pixel as
where “NIR” is the reflectance at an infrared band (around 750 nm), “Red” is the reflectance of a red band (around 650 nm), and NDVI is a number between -1.0 and 1.0. The magnitude of NDVI is a rough indicator of plant health. NDVI was well suited to early multispectral cameras that recorded four broad-band data points at each pixel (usually blue, green, red, and near-infrared). This is what was measured in the early Landsat satellites.
NDVI can give, at best, a rough estimate of plant health. Since the advent of airborne spectrometers and hyperspectral sensors, many more precise indices have been invented, with new indices invented regularly. An example