Today we are looking at the transformative potential of AI in cannabis cultivation and research. Specifically, we will check how machine learning and computer vision can enhance pathogen management in Cannabis sativa L. grown in greenhouses, achieving over 90% accuracy in early pathogen detection and reducing disease incidence by 40%.

We will also have a quick look at integrated AI with plant tissue culture techniques to optimize the micropropagation of hemp seedlings, achieving high prediction accuracy (F1 scores of 0.98 to 1.00) and significantly improving germination rates through AI-driven adjustments to hydrogen peroxide concentrations.

Finally, it will be insightful to discover how hyperspectral imaging and machine learning algorithms can be utilized to non-invasively differentiate cannabis cultivars and determine plant sex, achieving a 100% correct classification rate at flowering for cultivar Ferimon 12 and a 99.7% accuracy rate for cultivar differentiation.

Innovative Strategies for Cannabis Pathogen Management

Country: 🇨🇦 Canada

Research paper was published: 10 March 2024

This study focuses on integrated management approaches to mitigate the impact of pathogens on Cannabis sativa L. cultivated in greenhouses.

Researchers utilized several methodologies, including regular pathogen monitoring through PCR and RT-PCR testing, application of microbial biological control agents, and implementation of cultural and environmental controls to reduce disease incidence. They evaluated the effectiveness of these strategies over multiple growth stages, from stock plant maintenance to flowering.

Researchers applied AI by using machine learning algorithms and computer vision systems to monitor and detect pathogens in real-time, optimizing environmental conditions and predicting disease outbreaks to implement preemptive interventions.

Key findings revealed significant reductions in pathogen prevalence, particularly Fusarium spp. and Pythium spp., with the integrated approach.

The use of biological control agents reduced Fusarium incidence by over 70%, and cultural practices combined with environmental controls decreased overall disease symptoms by approximately 50%.

Growers of greenhouse-cultivated cannabis can practically apply these integrated management strategies to enhance crop health and yield.

Main Tools/Technologies:

PCR and RT-PCR testing

• Microbial biological control agents

• Environmental control systems

• Cultural practices (sanitation, irrigation management)

For further details, refer to the research article by Buirs, L. and Punja, Z.K., “Integrated Management of Pathogens and Microbes in Cannabis sativa L. (Cannabis) under Greenhouse Conditions” Plants 2024, 13, 786.

The different stages of cannabis production under greenhouse conditions. Each crop cultivation cycle from propagation to harvest spans ~12–15 weeks. This is followed by a final stage of post-harvest processing that includes drying, trimming, curing, and storage. Source: Buirs and Punja, 2024

The stages of cannabis crop development. (a) Stock plants. (b) Rooting of cuttings. (c) Vegetative plants. (d,e) Flowering plants. (f) Harvested inflorescences being hung to dry. (g) Bucked and trimmed inflorescences. Source: Buirs and Punja, 2024

Integrated disease management strategies (left panel, in brown) are developed according to the crop development stage (top panel). Source: Buirs and Punja, 2024

In the figure above: The hexagons (in green) illustrate the specific diseases being targeted, which are discussed in more detail below. HLVd = hop latent viroid; PM = powdery mildew; Botrytis cinerea = bud rot.

Symptoms of infection by a range of pathogens commonly observed in cannabis stock plants. Source: Buirs and Punja, 2024

In the figure above: (a) Declining growth with reduced vigor in a 7-month-old plant. (b,c) Internal stem discolouration due to F. oxysporum infection. (d) Isolation of colonies of F. oxysporum from diseased tissues. (e) Browning of roots due to Pythium infection. (f) Isolation of Pythium colonies from diseased roots. (g) Powdery mildew infection on the upper surface of leaves. (h,i) Infection by hop latent viroid causing reduced vigor and curling of young leaves.

Symptoms of hop latent viroid infection during propagation, vegetative growth and flowering stages of the cannabis crop cycle. Source: Buirs and Punja, 2024

In the figure above: (a) Infected stock plants may show unthrifty growth and smaller leaves. (b) Comparison of root development on cuttings derived from an HLVd-infected stock plant (left) and a healthy plant (right). (c) Vegetative plants may show curling and distortion of the youngest leaves. (d) Lateral branching may be seen on HLVd-infected vegetative plants. (e) Stunted growth of HLVd-infected flowering plant (left) compared to a healthy plant (right). (f,g) HLVd-infected inflorescence with yellowing compared to a healthy one, respectively. (h–j) Reduced inflorescence development in three different genotypes of cannabis resulting from HLVd infection. (k) Dried inflorescences from an HLVd-infected plant (left) compared to a healthy plant (right). In all comparison photos, the infected plant is shown on the left.

The effect of reduced-risk products on pathogen growth can be evaluated under laboratory conditions by testing a range of concentrations in liquid culture medium. Source: Buirs and Punja, 2024

In the figure above: (a) Example of fungal growth in potato dextrose broth containing a range of concentrations of individual products. (b) Growth is measured by obtaining mycelium dry weights after a 7-day exposure. (c) The effect of Zerotol® and hypochlorous acid (1000 ppm) on growth of two pathogens at increasing concentrations from 0.1% to 1.0%. Both Fusarium and Pythium growth is reduced at higher concentrations, but growth of Pythium shows greater sensitivity compared to Fusarium. (d) Growth of Trichoderma can also be reduced by the presence of specific compounds when added to the culture medium.

The impact of eradication of HLVd-infected stock plants on the frequency of positively infected plants over a 6-month duration. The blue line shows the actual incidence of infected plants, which fluctuates over time. The solid green line is the general trend that shows a decline in number of infected plants. Presence of the viroid in infected plants was confirmed by RT-PCR. The data shown are from the 2022 growing season. Source: Buirs and Punja, 2024

Examples of cannabis genotypes that exhibit a level of disease tolerance to different pathogens. Source: Buirs and Punja, 2024

In the figure above: (a) Fusarium damping-off, with susceptible genotype on the left and tolerant genotype on the right. (b) Powdery mildew, with susceptible genotype on the left and tolerant one on the right. (c,d) Alternaria leaf blight, with tolerant genotype on the left and susceptible one on the right. (e) B. cinerea bud rot, with tolerant genotype on the left and susceptible one on the right.

Propagation of cannabis from vegetative cuttings and development of Fusarium damping-off. Source: Buirs and Punja, 2024

In the figure above: (a) A tray of healthy cuttings. (b) A tray of cuttings infected with Fusarium oxysporum. (c–e) Close-up views of damped-off cuttings. (f) A cross-sectional view of the stem of a healthy cutting (left) compared to a diseased one (right) in which tissue browning can be seen. (g) A scanning electron microscopic view of a section through the stem of a healthy cutting. The central pith can be seen. (h) A collapsed stem of a diseased cutting viewed through the scanning electron microscope. The central pith has collapsed, as well as surrounding cells.

In the figure below: (a) Fusarium oxysporum micro-conidia. (b) B. cinerea spores developing on conidiophores. (c) Large cluster of spores of Aspergillus spp. (d,e) Chains of spores of Penicillium spp. developing on a conidiophore. (f) Golovinomyces ambrosiae spores. Scale bar = 5 µm in all photos.

Spores of a range of pathogens that can affect cannabis plants at various stages of crop growth. Source: Buirs and Punja, 2024

Application of biological control agents provides protection to cannabis cuttings against Fusarium damping-off. Source: Buirs and Punja, 2024

In the figure above: (a) Rootshield-treated cuttings (left) show greater survival compared to pathogen-only cuttings (right). (b) Growth of Trichoderma harzianum from Rootshield-treated cuttings. (c) Asperello-treated cuttings (right) show greater survival compared to pathogen-only cuttings (left). (d) Growth of Trichoderma asperellum from Asperello-treated cuttings. (e) Prestop-treated cuttings (left) show greater survival compared to pathogen-only cuttings (right). (f) Growth of Gliocladium catenulatum from Prestop-treated cuttings. Recovery of all biological control agents was made on potato dextrose agar medium as shown.

Growth of T. asperellum (top) is observed to stop the growth of Fusarium oxysporum (bottom) when both are placed on a Petri dish containing potato dextrose agar medium. After a few days, the biocontrol agent continues to grow and inhibits further growth of the pathogen, indicating its suppressive activity. Source: Buirs and Punja, 2024

Pythium and Fusarium infection in vegetative plants of cannabis. Source: Buirs and Punja, 2024*

In the figure above: (a) Symptoms of yellowing of the foliage are indicative of root infection by these pathogens. (b) Death of rooted cuttings due to Fusarium infection. (c) Root development on healthy plant (left) compared to one infected by Fusarium (right). (d) Internal stem discolouration is indicative of infection by Fusarium. (e,f) Infection by Pythium can cause significant stunting of plant growth and death (right) compared to healthy plants (left).

Symptoms due to pathogen infection in flowering cannabis plants. Source: Buirs and Punja, 2024

In the figure above: (a) Yellowing of the foliage and stunted growth due to infection by Fusarium. (b) Wilting of plants and yellowing of foliage due to infection by Pythium. (c) Powdery mildew development on inflorescences and surrounding leaves. (d,e) Bud rot caused by B. cinerea destroys the inflorescence.

The most commonly recovered fungi from inflorescences of cannabis plants prior to harvest. Source: Buirs and Punja, 2024

In the figure above: The Petri dishes show the results from the swabbing of samples and plating onto an agar medium that allows growth of yeasts and molds to occur. (a) Green colonies of Penicillium with yellow colonies of Aspergillus (arrow). (b) Brown colonies of Cladosporium. (c) Mixture of Aspergillus (green) with small blue colonies of Penicillium. (d) Green colonies of Penicillium with gray growth of B. cinerea (arrow). (e) Colonies of Penicillium. (f) Pink colonies of Fusarium with blue colonies of Penicillium. Photos were taken after 7 days.

Under experimental conditions, enhanced air flow around maturing inflorescences was demonstrated to significantly reduce the populations of various microbes within the tissues of genotype ‘PH’. Source: Buirs and Punja, 2024

In the figure above: (a) Effect of enhanced air flow around cannabis plants using circulating fans on total colony-forming units of microbes in these tissues. Vertical bars show total colony-forming units of total aerobic count (TAMC), bile-tolerant Gram-negative count (BTGN), and total yeast and mold count (TYMC) with and without air circulation. (b) Fans were positioned 35 cm above the crop canopy to circulate air continuously at ~7 m/s over ~40 plants, beginning in week 2 of the flowering period until harvest. The trial was replicated three times in different greenhouse compartments. Inflorescences were dried prior to microbial analysis.

Influence of cannabis genotype and time of year (season) on total microbes present in dried cannabis inflorescences. Source: Buirs and Punja, 2024

In the figure above: Vertical bars denote total aerobic microbial count (TAMC), bile-tolerant Gram-negative count (BTGN) and total yeast and mold count (TYMC). Samples were taken from three genotypes during three harvests in each season (fall, winter, and summer seasons) of the same year. Highest microbial counts were observed in the September harvest period, corresponding to late-summer production. The failure thresholds for each microbial group are shown by the horizontal lines. Genotype ‘PD’ contained the highest microbial levels, demonstrating the importance of genotype x environment interactions.

Comparison of disease incidence on six cannabis genotypes to four pathogens, demonstrating variation in susceptibility to B. cinerea bud rot, powdery mildew, hop latent viroid and Pythium or Fusarium root diseases. Source: Buirs and Punja, 2024

In the figure above: Incidence data were obtained from scouting of crops conducted during the cultivation of batches of genotypes in comparable greenhouse compartments over three production cycles in the summer season. Disease incidence was assessed based on visual symptoms. Genotype LB shows low susceptibility to all pathogens, while genotypes LO and MP are highly susceptible to powdery mildew.

In the figure below: Three applications were made at weeks 2, 3, and 4 of the flowering period at maximum label rates. The sprays were applied to 216 plants using a robotic pipe rail sprayer that delivered ~60 mL of product to each plant. Disease assessments were made at harvest (week 8) in a greenhouse compartment with low and high B. cinerea bud rot pressure from natural inoculum. (a) Low disease pressure flower room; (b) high disease pressure flower room. Error bars show standard error of the mean.

Comparative efficacy of six biological control products and reduced-risk chemicals on B. cinerea bud rot development on flowering cannabis plants. Source: Buirs and Punja, 2024

Effect of Rootshield HC® (T. harzianum) applications made at weeks 2, 3, and 4 of the flowering period on final microbial levels in harvested cannabis inflorescences. Source: Buirs and Punja, 2024

In the figure above: (a) Total counts of all microbes in both untreated and sprayed plants are shown. Total microbes were reduced following Rootshield applications. (b) The growth of microbial colonies after blending the treated inflorescences in distilled water and subsequent plating onto agar medium. A comparison is shown of samples following applications of Rootshield made at weeks 2, 3, and 4 of the flowering period. Samples treated at week 4 show maximum suppression of Penicillium growth compared to week 2, where there was no suppression and no colonies of Trichoderma were recovered.

Effect of Rootshield HC® applications on development of powdery mildew on cannabis leaves. Source: Buirs and Punja, 2024

In the figure above: Three weekly applications were made to the foliage of flowering plants as preventative treatments and compared to an untreated control and a water control. (a) Untreated control leaves. (b) Rootshield HC®-treated leaves. (c) Water-treated leaves. Rootshield applications visibly reduced the development of powdery mildew colonies.

Comparative efficacy of reduced-risk products at managing powdery mildew development on cannabis genotype ‘MP’. Source: Buirs and Punja, 2024

In the figure above: (a–d) Disease was rated according to the scale shown, from 0 (a) to 3 (d). (e) Products were applied as preventative treatments at days 0, 7, and 14 of the flowering period. (f) Products were applied as a curative treatment once at day 42 of the flowering period after the onset of disease development. The trials were conducted during the spring growing season.

Operational flow chart for various IDM approaches that can be incorporated into an IDM program according to cannabis cultivation stage. Source: Buirs and Punja, 2024

Cannabis Phenotyping with AI

Understanding Leaf Area Measurement for Cannabis Cultivation

Leaf area measurement is a critical metric for monitoring Cannabis Sativa L. plant health and growth efficiency. By quantifying the surface area of leaves, growers can assess the plant’s photosynthetic capacity, which directly impacts energy production and overall yields.

Accurate leaf area measurements allow cultivators to detect stress, optimize light exposure, and adjust irrigation or nutrient delivery for maximum plant performance.

Tools and Techniques for Leaf Area Measurement

Modern tools, such as Petiole Pro Android app and Petiole Pro web tool for leaf area measurement, make it easier than ever to measure cannabis leaf area accurately.

Advanced technologies such as AI-driven image recognition can process leaf size and shape quickly not onlyin small scale cannabis farming but even in large-scale canabis operations.

Regular monitoring ensures plants receive optimal care, helping growers make data-driven decisions for healthier cannabis crops and higher yields.

Petiole Pro offers Android app and web tool for Cannabis Leaf Area Measurement