Multispectral reflectance database of Cannabis sativa

Our research hypothesis postulates that different strains of Cannabis sativa grown for medicinal purposes exhibit distinctive multispectral reflectance signatures in the 410 nm to 890 nm range that can be used for nondestructive and low-cost classification. The accompanying database contains 800 spectral signatures, each with 2442 lambda reflectance values, providing detailed resolution of the optical data. These data were collected from a medical cannabis crop by randomly sampling 30 plants of two varieties and 20 plants of two other varieties, spanning three distinct phenological stages, from which both high and low leaf measurements were taken for each sample. Notable findings from this data collection will focus on identifying subtle spectral patterns and differences between varieties, as well as within-variety variability as a function of phenological stage and leaf position. The data can be interpreted to develop machine learning models capable of discriminating between medical cannabis varieties efficiently and without damaging the plant, representing a valuable alternative to current identification methods that are often destructive and costly, opening the door to applications in quality control, traceability and crop optimization in the medical cannabis industry.