Mr Josh Chopin and Professor Stan Miklavcic
Phenomics and bioinformatics are two indispensable disciplines for successful molecular biology and genetics. Applied to plant biology, automated phenotypic analysis based on plant images captured as a function of growth conditions, can help us obtain a large amount of information on the function of genes and the impact of abiotic stress on plants. In this project, we will develop algorithms to extract both statistical and structural features from images of plants at their different stages of growth. This project features the use of machine learning/pattern recognition methods as well as advanced mathematical and statistical techniques to identify cereal plants at their different growth stages. Moreover, as different sensors capture different information, the project aims to integrate the different sensorial image information into the plant phenotypic analysis.
J. Chopin, H. Laga, S.J. Miklavcic “A hybrid approach for improving image segmentation: application to plant phenotyping”, PLoS One, vol. 11, no. 12, pp. 1-18.