Algorithms for Predictive Plant Monitoring

2019-2020 Spring
Faculty Department of Project Supervisor: 
Faculty of Engineering and Natural Sciences
Number of Students: 

Researchers develop novel technologies to predict which genes control traits and resulting phenotype. If these genes (quantitative trait loci) can be revealed, we can understand the genetics of adaptational response (resistant (happy), tolerant, susceptible) to the environmental constraints. In the future, these genes can be used as markers (predictive indicators) to select the set of individuals (with desired phenotype) from a population of interest.
In this project, we are specifically interested in development of a quantitative trait analysis methodology. To this end, a root scanner will be used to collect images of seedling root, and then, define unique features of grown roots of seedling with high statistical performance.  Students will participate in
1) Labeling of data into resistant/tolerant/susceptible categories.
2) Development of feature extraction algorithms toward high performance classification of adaptational response of seedlings during growth.

Related Areas of Project: 
Computer Science and Engineering
Molecular Biology, Genetics and Bioengineering
Electronics Engineering

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