Journal:Characterization of trichome phenotypes to assess maturation and flower development in Cannabis sativa L. by automatic trichome gland analysis

Full article title	Characterization of trichome phenotypes to assess maturation and flower development in Cannabis sativa L. (cannabis) by automatic trichome gland analysis
Journal	Smart Agricultural Technology
Author(s)	Sutton, D.B.; Punja, Z.K.; Hamarneh, G.
Author affiliation(s)	Simon Fraser University
Primary contact	Email: darrens at sfu dot ca
Year published	2023
Volume and issue	3
Article #	100111
DOI	10.1016/j.atech.2022.100111
ISSN	2772-3755
Distribution license	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Website	https://www.sciencedirect.com/science/article/pii/S2772375522000764
Download	https://www.sciencedirect.com/science/article/pii/S2772375522000764/pdfft (PDF)

Abstract

Cannabis (Cannabis sativa L.) is cultivated by licensed producers in Canada for medicinal and recreational uses. The recent legalization of this plant in 2018 has resulted in rapid expansion of the industry, with greenhouse production representing the most common method of cultivation. Female Cannabis plants produce inflorescences that contain bracts densely covered by glandular trichomes, which synthesize a range of commercially important cannabinoids (e.g., tetrahydrocannabinol [THC] and cannabidiol [CBD]), as well as terpenes. Cannabinoid content and quality varies over the eight-week flowering period to such an extent that the time of harvest can significantly impact product quality. Cannabis flower maturation is accompanied by a transition in the color of trichome heads that progresses from clear to milky to brown (amber) and can be seen visually using low magnification. However, the importance of this transition as it impacts quality and describes maturity has never been investigated.

To establish a relationship between trichome maturation and trichome head color changes (phenotype), we developed a novel automatic trichome gland analysis pipeline using deep learning. We first collected a macro-photography dataset based on four commercially grown Cannabis strains, namely Afghan Kush, Green Death Bubba, Pink Kush, and White Rhino. Images were obtained in two modalities: conventional macroscopic light photography and macroscopic UV induced fluorescence. We then implemented a pipeline where the clear-milky-brown heuristic was injected into the algorithm to quantify trichome phenotype progression during the eight-week flowering period. A series of clear, milky, and brown phenotype curves were recorded for each strain over the flowering period that were validated as indicators of trichome maturation and corresponded to previously described parameters of trichome development, such as trichome gland head diameter and stalk elongation. We also derived morphological metrics describing trichome gland geometry from deep learning segmentation predictions that profiled trichome maturation over the flowering period.

We observed that mature and senescing trichomes displayed fluorescent properties that were reflected in the clear, milky, and brown phenotypes. Our method was validated by two experiments where factors affecting trichome quality and flower development were imposed, and the effects were then quantified using the deep learning pipeline. Our results indicate the feasibility of automated trichome analysis as a method to evaluate the maturation of female flowers cultivated in a highly variable environment, regardless of strain. These findings have broad applicability in a growing industry in which cannabis flower quality is receiving increased circumspection for medicinal and recreational uses.

Keywords: Cannabis, trichomes, deep learning, phenotype, fluorescence, precision agriculture

Introduction

Cannabis (Cannabis sativa L.) is valued for its medicinal use in every continent except Antarctica, and many countries have established the legal framework for the [Cannabis cultivation|cultivation]] and sale of recreational-cannabis-derived products. [1] Drug-producing Cannabis strains are characterized by large female inflorescences (flowers) that bear a cluster of pistils surrounded by bracts, which produce large numbers of glandular trichomes where cannabinoids and terpenes are synthesized. [2] Cannabis plants require approximately eight weeks to mature prior to harvest, after which inflorescences are dried for sale in their natural form or processed for value-added products (i.e., edibles, cosmetics, extracted oils). In order to ensure optimal Cannabis quality, it is imperative to identify the stage at which the inflorescences are at the point of prime maturation and hence potency. There are currently no scientifically based methods to predict maturation of inflorescences, and consequently harvests are performed on a calendar basis, i.e., when plants have attained seven to eight weeks of growth. Enacting methods to better assess trichome maturation can lead to improvements in quality assurance for the Cannabis industry.

In this work, we study Cannabis trichome gland head phenotypes as flowers mature and visual trichome changes occur, such as a progression in color of the heads of trichomes from clear to milky to brown. Although these phenotypes have been suggested as a visual heuristic for harvest timing, little scientific work describes them during flower development and trichome maturation. Previous work supports the idea that browning of trichome heads is associated with quality degradation in dried Cannabis [3], but the shelf life of dried Cannabis [4] and progressive trichome browning in storage makes extrapolation of results to fresh Cannabis tissue unpredictable. In order to understand the role of trichome phenotypes during trichome maturation, it is necessary to obtain measurements in situ during flower development.

We describe an automatic computational method that was built to extract trichome phenotype and morphology metrics during Cannabis flower development from macroscopic photographs. To reduce uncertainty related to the chronology of observations, our method was implemented in a commercial greenhouse such that the time delay between excision and photography was minimized. By implementing recent advances in computer vision, we show our automatic method can be used to define trichome maturation in multiple Cannabis strains in high-throughput applications without repeated fine tuning.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. Some grammar and punctuation was cleaned up to improve readability. In some cases important information was missing from the references, and that information was added. No other changes were made in accordance with the "NoDerivatives" portion of the license.