We are pleased to see our latest co-authored paper published in Industrial Crops and Products:
“Hyperspectral measurements of Cannabis sativa fan leaves during early floral development predict final cannabinoid yield.”
The study shows that spectral-optical measurements of fan leaves, taken early in flowering, can already predict the final cannabinoid yield with high accuracy. Using hand-held hyperspectral sensing and machine learning models, the team demonstrated reliable, non-destructive predictions for CBD, THC, and total cannabinoid content — weeks before harvest.
For us at Compolytics, this work highlights once again the power of data-driven optics: transforming spectral patterns from living plants into actionable insights.
A great collaboration with our research partners from The University of Adelaide — showing what’s possible when spectral sensing meets intelligent modelling.
📖 Read the publication in Industrial Crops and Products
This research project and the connected development of spectral optical sensor technology for cannabis / industrial hemp is part of a whole series on this topic.
📖 Read more:
Spectral Assessment of Cultivar and Sex of Cannabis Plants
Improving Cannabis Farming using Spectral Sensing
Media coverage
The study has also attracted wider attention. Forbes featured the findings in an article titled “New Technique Accurately Predicts THC Content Weeks Early, Study Shows”, highlighting the potential of hyperspectral sensing and machine learning for improved quality control and risk management in cannabis cultivation.
Image credit: Banner illustration by Dr Aaron Phillips, University of Adelaide, created as part of the research collaboration leading to the publication.