Post-Doctoral Fellowship: High-throughput quantitative analysis of flowering dynamics and canopy structure in Canola germplasm using image analysis and deep learning methods
Crop improvement is dependent on accurate measurements of plant traits such as flowering time, leaf and seed number. For canola, a major grain crop in Australia, these traits are currently assessed by human observation, a time-consuming and costly process.
This investment will develop tools and methods to automate and scale up the collection of phenotype data with a focus on flowering time and canopy architecture. The project will employ a variety of imaging techniques and data extraction methods based on recent advances in artificial intelligence and machine learning.
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