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
Speaker
  • Professeur titulaire/Full Professor, Département de management/ Department of Management, Faculté des sciences de l'administration / Business School Pavillon Palasis-Prince, Université Laval
    Biography
      James Eaves received his Ph.D. in Agricultural Economics from the University of California at Davis. He is currently a professor at Université Laval, where his research focuses on methods of improving the profitability of growing cannabis indoors at scale. He has worked with a number of large cannabis producers on various research projects, and he was the Head of R&D for Greenseal Cannabis Company.

    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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