#420: Cannabis and Machine Learning, a Joint Venture

Cannabis growers and sellers are rolling in and cashing out on machine learning

Regardless of scale, cannabis growers and sellers are doing business in a notably challenging environment. While they are dealing with ever-changing regulatory measures, they also need to navigate complex labor compliance issues and banking restrictions. On top of the typical business and supply chain operations, this emerging market is still unsettled legally, economically, and facing increasingly severe weather.  As a result, cannabis product companies and the agriculture industry at large, are looking to machine learning’s ability to predict, optimize, and analyze as they embrace the future of agricultural technology.

Challenges in the AgTech and cannabis industry

Cannabis-based producers must tackle complex agricultural issues:

Growers:

• Manage pests and disease
• Design efficient nutritional plans
• Ensure ideal environmental conditions 
• Optimize output while minimizing overhead
• Legal regulatory compliance

Sellers:

• Understand and organize complex distribution processes
• Coordinate manufacturers, farmers, brands, and customer demand
• Make decisions for future growth and expansion
• Multi-state tax structures and regulations

For dealing with the operational side of growing, as well as for tackling the marketing side of selling, cannabis-based product companies can now leverage powerful data. This data fuels machine-learning-capable software that can predict the future by way of modern algorithms and data-processing architectures.

The following characteristics of cloud-based ecosystems are powering machine learning solutions:

• Sensors and hardware for extracting information are cheaper

  • The increased popularity and success of IoT solutions make it possible to deploy, connect, and establish vast networks of smart devices. This localized streaming data is a crucial component for the accuracy of predictive data models.

• Computing and storage resources are increasingly affordable

  • Competition among cloud vendors invites innovation and development at a low cost. Anyone can build and deploy ML solutions in the cloud, given that they have access to enough data. Furthermore, all cloud providers use a pay-as-you-go model allowing customers to only pay for what they use and require.
    

• Algorithms and data processing frameworks are widely available

  • Many data processing tasks (all the way from collection to analysis) can easily be updated and automated with cloud-based tools. Similarly, pre-trained ML models and neural network architectures can be repurposed using old knowledge on new problems.

Such a rich ecosystem of tools, frameworks, and cheap data collecting devices have turned ML in agriculture into a viable, cost-efficient solution for the toughest challenges. No wonder that data-powered optimization is currently reshaping the entire agriculture sector, well beyond cannabis farming.

Below are a few brief ways predictive modeling solutions are being applied by both cannabis growers and sellers.

For Growers: Predictive models for operational improvements

Potency

Accurately understanding the chemical makeup of the cannabis plant is a crucial necessity for respecting regulatory measures. Predictive models can incorporate spectroscopy, x-ray imaging techniques, and machine learning to accurately identify cannabinoids and thus label cannabis varieties. Even in cases when the available data was insufficient, researchers were still able to cluster cannabis strains into distinct categories (medicinal, recreational, combined, industrial) based on their chemical properties. Not only do such models enable a better understanding of cannabis potency at all stages of the supply chain, but they represent a safeguard of quality and health for the end consumers. 

Yield Prediction

Collecting localized, real-time data from crops (humidity, temperature, light) is the first step in understanding both artificial and natural growing environments. However, knowing what to plant and what actions to…