Evaluate effect of chemical compounds on plants using statistics and machine learning

To understand the effects of various substances on plants in terms of yield and disease severity (phytopathology), we need to evaluate both statistical significance and biological relevance when conducting biological experiments. Biological relevance refers to the nature and size of biological changes or differences seen in studies that would be considered relevant, while Statistically significance is the likelihood that a relationship between two or more variables is caused by something other than chance. The primary goal of the project is to apply statistics (experiment design, hypothesis testing, regression analysis) to understand effect of different substances on plants in-vitro, in-planta and in-field. The secondary goal of the project is to develop machine learning / deep learning models that produce estimates of plant health based on imaging and other sensor data generated by Terramera.

Faculty Supervisor:

Tim Swartz

Student:

Matthew Reyers

Partner:

Terramera Inc

Discipline:

Statistics / Actuarial sciences

Sector:

Agriculture

University:

Program: