On a direct method to estimate cannabidiolic acid content of Cannabis sativa L. from near-infrared hyperspectral images

This paper investigates the use of a direct reflectance model of Cannabis sativa L. in conjunction with near-infrared hyperspectral imaging to estimate the cannabidiolic acid (CBDA) concentration of plant components (buds and leaves). A plant material model based on the PROSPECT model is used to determine the CBDA content. This model relates the measured pixel reflectance to cannabinoid content as determined by liquid chromatography-mass spectrometry. This work also provides insight into how the refractive index of CBDA varies with increasing wavelength in the NIR region. The direct model was applied to the complete hyperspectral image of the plant to produce CBDA estimation and distribution maps. This work aims to develop a non-destructive method for Cannabis sativa L. assessment to allow for real-time monitoring of plant growth, health, and yield.