Multispectral vs Hyperspectral in agriculture
Gamaya is a data analytics company that empowers farmers with unprecedented in-depth understanding of their lands and crops using unique hyperspectral imaging and artificial intelligence. Gamaya is one of the first commercial company that makes hyperspectral imaging accessible for commercial applications, such as precision agriculture, at the cost comparable to multispectral imaging. The hyperspectral camera, developed by Gamaya, is not our primary goal. Our core expertise is in the analysis and interpretation of hyperspectral data using artificial intelligence to produce the information about the plant physiology.
Hyperspectral imagery has been available for 20–30 years, but it’s been an extremely expensive exercise constructing the equipment, hiring developers, and analyzing the terabytes of data, so has mainly been used by large research institutes (Carnegie Airborne Observatory, ETHZ, etc.), space agencies and the military. Below we would like to compare multispectral vs hyperspectral imaging and clarify what are the real benefits of hyperspectral imaging in agriculture.
The spectral resolution is the main factor that distinguishes hyperspectral imagery from multispectral imagery. Based on spectral responses, hyperspectral imagery captures more narrow bands than multispectral in the same portion of the electromagnetic spectrum. The advantage of a higher spectral resolution gives ability to distinguish between different crop characteristics, which in turn provides ability to address more and much more complicated farming issues. The greater/finer detail in a scene, the more likely unique crop characteristics and physiological traits are to be defined.
The table below reveals the main differences between multispectral and hyperspectral imaging, highlighting the ability to address specific agricultural applications.
As seen from the table, multispectral imagery is largely limited to enabling analysis based on the Normalized Difference Vegetation Index (NVDI) and cannot, for instance, distinguish or classify weeds, diseases or pests. Multispectral cameras can measure generic characteristics such as if a plant is healthy or not, but hyperspectral images can go much further, and diagnose the exact reason for that state. In this sense, multispectral imaging is analogue of the blood pressure or heart rate measurements in medicine, while hyperspectral imaging is analogue of MRI scan, used to diagnose specific diseases.
Higher spectral resolution of the hyperspectral imaging predetermines the high information content of the hyperspectral data, which is 10 times higher than NDVI. As hyperspectral imaging allows to identify unique physiological crop traits, it becomes possible to identify crop diseases, pests and nutrient deficiencies, due to ability to correlate spectral signature with changes in the plant physiology. Hyperspectral imaging can identify and classify different type of weeds, wild vegetation and crop varieties, due to the fact that each specie of vegetation and variety of crop has its unique spectral signature.
Another important difference between multispectral and hyperspectral imagery is the ability to use AI and machine learning, facilitated by the high informational content of hyperspectral data. This allows to continually improve existing products and develop new products. Multispectral imaging technology lacks the information richness to enable continual development. Therefore, hyperspectral imaging is positioned to become a main remote sensing technology used on a global scale for various agricultural applications.
To sum up, hyperspectral imaging is the most information-rich source of spectral data and provides multiple benefits over multispectral imagery to address different farming issues, such as detection of diseases, pests, NPK deficiencies, identification of weeds and other. Precision agriculture requires more than RGB information or multispectral imagery, and currently the hyperspectral imaging is the most advanced technology for remote sensing applications.
Gamaya provides the world’s most advanced solution for diagnostics of farmland using a unique constellation of patented hyperspectral imaging technology, drone-based deployment and artificial intelligence. Gamaya farmland analytics solution improves production efficiency and risk management by facilitating optimised usage of chemicals and fertilisers, as well as reducing disease and weed-related losses. Some of the information services that we provide for soybean, corn and sugarcane include the detection and diagnostics of crop diseases, classification of weeds, optimization of fertilization, identification of crop varieties, as well as prediction of yield.