How artificial intelligence can benefit cannabis growers

The integration of artificial intelligence tools in cannabis cultivation has the potential to greatly improve the efficiency and productivity of any crop. Whiteboards and Excel spreadsheets are increasingly becoming a thing of the past; and with cloud-based platforms powered by AI, you can achieve the analysis and precision needed to succeed in an industry as competitive as that of cannabis.

According to a recent analysis, the global cannabis market is expected to reach $82.3 billion by 2027. This growth is driven by growing legalisation, the emergence of more medical applications, and the increasing social acceptance of marijuana. And, in turn, this is creating a huge demand for high-quality products. By using AI-driven solutions, growers can ensure that they produce better plants, whilst eliminating many of the challenges traditionally posed by cannabis growing.

There’s no doubt that growing marijuana is a multifaceted skill that requires a high level of precision and attention to detail. In fact, the beauty of this art somehow lies in its complexity. Small adjustments to the growing environment can translate into huge benefits when it comes to harvesting. It is a space for calculated creativity to be able to obtain ideal yields under specific conditions.

However, adjusting this process until it is perfect involves a lot of work in terms of records, calculations, and analysis. These meticulous details are almost impossible to analyse without introducing machine learning into the grow room. And, as these two spheres reach the summit of revolutionary convergence, growers turn to digital tools and artificial intelligence to help them optimise production and increase the yielding potential of their plants.

Amnesia Auto x Amnesia Haze

Amnesia Dream XL Auto

The automatic version of Amnesia Haze is a pest resistant strain that can work as an autoflowering strain or as a fast-flowering photoperiod-dependent seed, which is what we would like to see most in an Amnesia Haze. Just as aromatic and powerful as its progenitor, but without excessive weight from Ruderalis.

Machine learning algorithms based on sensors

For example, some technological platforms use machine learning algorithms to analyse data from environmental sensors placed in a growing facility. These can monitor various metrics, such as temperature, humidity, or light levels, which provide growers with real-time information that can help optimise the growing conditions.

By using these platforms, growers can obtain a detailed understanding of their grow environment and adjust it in real time to ensure optimal conditions. And this helps avoid common problems like excessive irrigation, which can lead to mould and fungi; or insufficient nutrition, which can hinder growth. Not only does automation increase efficiency, but it also reduces the risk of human error, resulting in more consistent and reliable crop production.

Machine vision to replace the eyes of the grower

In addition to environmental sensors, growers are also using machine vision systems to track plant growth and foresee potential problems. Using cameras and machine learning algorithms, growers can rapidly detect anomalies such as pests or nutrient deficiencies, which enables much quicker intervention.

Machine vision technology is particularly useful in cannabis growing because it can help identify subtle changes in plant growth that may not be visible to the naked eye. For instance, a computer vision system can detect minimal differences in the colour or shape of the leaves, which could be an indication of an underlying problem.

Mobile robots equipped with cameras connected to AI can also roam the corridors of cannabis greenhouses at night, scanning plants with spectral images. AI converts these scans into a 3D model of each plant before combining that 3D image with environmental data to generate a progress report that evaluates growth rates, THC levels, cannabinoid compounds, the possible occurrence of disease, the presence of pests, and much more.

Predictive models using AI

AI is also being used to develop predictive models that can foresee the yielding capacity of crops based on various factors, such as weather patterns and soil conditions. These models can help growers predict the best planting times, harvest windows, and even the strains that are most likely to thrive in a specific environment. And this in turn helps optimise the growing schedules, especially during the colder months, which is when producing crops becomes more difficult.

One of the key advantages of using AI in cannabis cultivation is that it allows a more data-based approach for making decisions: by analysing large sets of data, growers can identify patterns and trends that may not be apparent to the naked eye. This can help them optimise their production processes and increase yields.

Gelato x Zkittlez x Ruderalis

New Strain! - Runtz Auto

A resistant and robust strain, all-terrain, which provides about 150gr/plant outdoors and surprises with up to 500gr/m2 indoors of resinous buds and a lush bright green. With a candied and intense flavor, Runtz Auto provides a perfect euphoric effect to combat melancholy, which after a while derives in a sedative experience ideal to lift the mood.

Chatbots for greater knowledge

Domestic self-cultivation of cannabis is neither a mystery nor is it reserved only for experienced growers. Popular artificial intelligence tools like ChatGPT can show you a complete step-by-step guide on how to start growing your own cannabis plants with just a simple message. Try it yourself by asking AI “steps to grow cannabis at home”, and you’ll quickly see what we mean.

The same goes for the search for advice. This information is obtained from the growing database of information that has previously been shared online, but without the need to navigate through countless pages and resources. It is now easier than ever for the average citizen to access information on the latest trends and tips for growing cannabis both indoors and outdoors.

Autonomous domestic systems

There are also brands that offer automated cultivation technologies for domestic use in small spaces. These give growers the freedom to reduce costs whilst generating high cannabis yields with minimal effort.

These AI-driven systems provide anyone, from the average consumer to rookie growers (including parents, executives, athletes, pensioners, etc), with the ability to grow one or more cannabis plants without constant attention or large space requirements. They combine artificial intelligence technologies with automation and hydroponics to create the most efficient and effective growing experience possible.

In addition, micro-cultivation can be controlled and monitored with the help of an app from anywhere using a mobile device. These are called ‘grow boxes’, and they’re small smart grow tents that you can place in your living room or kitchen as if they were just another home appliance. They are gaining much popularity in the U.S. and Canada, and brands include Seedo, Annaboto, Grobo, Leaf, or Linfa.

These devices look like mini fridges. Some are hermetically sealed and feature the following: built-in carbon filters; automated air conditioning units; a full spectrum LED lighting system that adjusts automatically to maintain an optimal distance from the plants; a nutrient dispensing system; built-in cartridges to release CO2 during photosynthesis; and an internal camera to monitor without intervention. Once the plants are fully developed and ready to be cut, some of these systems also become an intelligent post-harvest drying system.

Everything is controlled and managed by artificial intelligence software, which analyses the development of the plant and takes measurements to optimise growth parameters based on performance. You just simply need to set up a growth plan for a particular marijuana strain, sit back, and let it grow completely autonomously.

Genetics on demand

Another interesting AI application in the field of cannabis cultivation is strain development and customisation. Traditionally, breeders have relied on trial-and-error methods to create new strains with specific characteristics. However, AI can accelerate this process by analysing large amounts of data and identifying patterns that humans may not notice.

These engines use patented genetic markers to identify existing cannabis varieties that could be crossed to create even more potent offspring, especially for the medical market. Or to even select new and unique terpenes that can offer flavours and smells that have never been experienced before.

By using AI algorithms, breeders can predict the effects of different genetic combinations and select the most promising candidates for further cultivation. Not only does this save time and resources, but it also enables the development of new strains with unique properties that meet the specific needs and preferences of consumers.

Conclusion

Artificial intelligence could soon become an essential tool for cannabis growers looking to increase yields and improve the efficiency of their facilities. While it cannot replace the knowledge and experience of the most competent growers, it can provide data and insights that lead to more effective decision-making.

Looking ahead, AI in the cannabis sector is poised to continue its growth and evolution. And as technology advances and AI algorithms become more sophisticated, the possibilities for innovation and optimisation will become virtually endless.