MAR 27, 2019 12:00 PM PDT

Opportunities and Challenges for Applying Machine Learning in the Cannabis Space

C.E. CREDITS: P.A.C.E. CE | Florida CE
Speakers
  • Professor, Department of Management, Laval University
    Biography
      James Eaves received his Ph.D. in Agricultural Economics from the University of California, Davis. Today, he is a management professor at Université Laval, where he is part of a research group that aims to find ways to increase the profitability of growing cannabis indoors and in greenhouses. He is a regular invited speaker at cannabis conferences across North America, writes for Canada's leading cannabis trade journal, and his award winning research has been discussed in numerous media outlets, including The Washington Post, Newsweek, and the CBC.

    Abstract:

    Building and production management systems are creating massive amounts of data related to growing cannabis. This presents the opportunity for growers to use machine learning (ML) tools to substantially increase their profitability and lower risk.  Researchers and entrepreneurs have only recently started applying ML to agriculture, and virtually no research exists regarding cannabis. This session has three goals: 1) Introduce participants to the basic process of developing a ML model; 2) Discuss a few examples of ML being applied to non-cannabis crops that have relevance to cannabis; 3) Discuss some challenges of applying ML in the cannabis space.

    Learning Objectives: 

    1. Learn the process of building a machine learning (ML) model.
    2. Learn some likely ML application in the cannabis space and the general implementation challenges. 


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