Marijuana Breeders Can Use AI To Design New Strains, Study Demonstrates

Marijuana breeders may be able to design new strains and speed up their
growing cycles by utilizing artificial intelligence (AI), a new study
suggests.

Researchers found that by feeding genetic markers, growth measurements,
environmental data and chemical assays into AI models, breeders could
simulate thousands of potential crosses and stimulate “speed breeding”
through machine learning before ever planting a seed.

The authors argue this approach could cut traditional breeding cycles,
which currently last between six to eight years, down to a fraction of that
time, while also improving consistency—a perennial challenge that
commercial cannabis growers grapple with.

“Machine learning allows for iterative simulations of breeding
outcomes…while ensuring chemical consistency,” the authors concluded.

“AI-enabled cannabis breeding represents a paradigm shift in strain
development, enabling precise control over cannabinoid and terpene profiles
while reducing breeding cycle times and resource requirements.”

The paper also highlights the role of metabolomics, an emerging field that
catalogs the vast array of chemicals produced by living organisms.

“AI systems correlate these datasets to predict how specific genetic
combinations will influence chemical composition and growth traits,
enabling precise selection of parental strains for crossbreeding,” they
observed.

Techniques like genomic selection, regression analysis and deep learning
are already used in major agricultural crops. Applied to cannabis, these
tools look for patterns linking genetic variants to chemical traits such as
the proportion of THC or the presence of rare cannabinoids like CBG.

One of the biggest challenges in cannabis cultivation is the complex
interaction between genetics and environment. Light spectrum, humidity,
nutrient availability and subtle temperature shifts can reshape a plant’s
chemical output.

The study, which has not yet been published in a journal but was posted on
the science site ResearchGate, describes how AI systems can incorporate
these variables to predict performance in different growing environments—a
tool that could be particularly valuable as the industry expands into
diverse climatic regions.

Neural networks can track nonlinear interactions among dozens of genes that
influence plant chemistry, in addition to incorporating environmental
conditions.

“These innovations promise accelerated strain development, improved
chemical consistency, and enhanced adaptability to diverse growing
environments,” the authors, who are affiliated with the University of
Saskatchewan and Renaissance Bioscience, noted.

Using instruments like gas chromatography mass spectrometers, researchers
can employ AI to measure cannabinoids and terpenes across a plant’s life
cycle. Combined with imaging tools that assess traits such as trichome
density or stress responses, these data points give AI models the raw
material to make increasingly accurate predictions.

“This capability allows breeders to design strains not only for chemical
profiles but also for resilience and adaptability in diverse growing
environments,” the researchers wrote.

Highlighting the importance of reproducibility, they noted that “the global
cannabis industry demands high-quality, reproducible strains, creating the
need for precision breeding technologies that reduce time-to-market while
maximizing yield and potency.”

The writers cautioned that, for all its advantages, AI-enabled cannabis
breeding faces challenges, including data quality constraints that affect
the accuracy of genotypic and phenotypic predictions. They also note
complications surrounding complex polygenic traits, ethical considerations
and regulatory barriers, observing that “legal restrictions on cannabis
research may limit data access.”

Recent research on cannabis genetics suggests that incentives in the legal
marijuana market—such as the desire for plants to mature faster and produce
more cannabinoids for extraction—may be leading to a decline in
biodiversity of the plant worldwide, prompting a researcher from California
State Polytechnic University Humboldt to describe the problem as “the
bottlenecking of *Cannabis *genetics.”

And in 2022, the California Department of Cannabis Control funded a $20
million study to, as officials wrote, “identify and preserve the history,
value and diversity of California legacy cannabis cultivars and the rich
experience of its legacy cultivation community, and enable, enhance and
guide the understanding and application of cannabis genetics to the greater
body of research and science-based public policy development.”

Interest in cannabis genetics is not confined to state level governments.
In 2018, a powerful U.S. Senate committee directed agriculture authorities
to begin building up the nation’s stockpile of cannabis genetics, setting
aside half a million dollars to support the work.

The post Marijuana Breeders Can Use AI To Design New Strains, Study
Demonstrates appeared first on Marijuana Moment.