# Identification of non‐glandular trichome hairs in cannabis using vision‐based deep learning methods

## TL;DR

This paper introduces a deep learning method to accurately identify cannabis hairs using microscope images, helping forensic labs detect illicit cannabis products more efficiently.

## Contribution

A novel deep learning framework for identifying cannabis non-glandular trichome hairs with over 97% accuracy, reducing reliance on manual forensic analysis.

## Key findings

- The deep learning model achieved over 97% accuracy in distinguishing cannabis from non-cannabis plant material.
- The method uses annotated microscope images validated by forensic tests and expert analysis.
- This approach offers a reliable and efficient alternative to traditional, resource-intensive forensic methods.

## Abstract

The detection of cannabis and cannabis‐related products is a critical task for forensic laboratories and law enforcement agencies, given their harmful effects. Forensic laboratories analyze large quantities of plant material annually to identify genuine cannabis and its illicit substitutes. Ensuring accurate identification is essential for supporting judicial proceedings and combating drug‐related crimes. The naked eye alone cannot distinguish between genuine cannabis and non‐cannabis plant material that has been sprayed with synthetic cannabinoids, especially after distribution into the market. Reliable forensic identification typically requires two colorimetric tests (Duquenois‐Levine and Fast Blue BB), as well as a drug laboratory expert test for affirmation or negation of cannabis hair (non‐glandular trichomes), making the process time‐consuming and resource‐intensive. Here, we propose a novel deep learning‐based computer vision method for identifying non‐glandular trichome hairs in cannabis. A dataset of several thousand annotated microscope images was collected, including genuine cannabis and non‐cannabis plant material apparently sprayed with synthetic cannabinoids. Ground‐truth labels were established using three forensic tests, two chemical assays, and expert microscopic analysis, ensuring reliable classification. The proposed method demonstrated an accuracy exceeding 97% in distinguishing cannabis from non‐cannabis plant material. These results suggest that deep learning can reliably identify non‐glandular trichome hairs in cannabis based on microscopic trichome features, potentially reducing reliance on costly and time‐consuming expert microscopic analysis. This framework provides forensic departments and law enforcement agencies with an efficient and accurate tool for identifying non‐glandular trichome hairs in cannabis, supporting efforts to combat illicit drug trafficking.