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.
Molecular Biology
Environment
Laboratory Testing
Biotechnology
Research And Development
Cannabis
Research
Genetics
Microbiology
Functional Medicine
Personality
Epigenetics
Animal Behavior
Clinical Oncology
Genetic Engineering
North America33%
Europe33%
South America33%
Website Visitors100%
Executive33%
Forensic Scientist33%
Engineer33%
Private Practice33%
Consultant33%
Government33%