Field and quasi-field phenotyping for the quantitative characterisation of wheat yield under stress. The project aims to develop state-of-the-art monitoring and profiling capabilities for the quantitative assessment of plant growth performance in field and quasi-field environments under the abiotic stress conditions of drought and nutrient deficiency. This project involves the design and use of high resolution but low budget imaging stations to capture the growth of cereal plants in competitive ....Field and quasi-field phenotyping for the quantitative characterisation of wheat yield under stress. The project aims to develop state-of-the-art monitoring and profiling capabilities for the quantitative assessment of plant growth performance in field and quasi-field environments under the abiotic stress conditions of drought and nutrient deficiency. This project involves the design and use of high resolution but low budget imaging stations to capture the growth of cereal plants in competitive environments. Novel computer vision and image processing techniques will be applied to the image data to quantitatively characterise the success of genetic varieties to tolerate abiotic stress environments under actual field conditions.Read moreRead less
Co-variant analysis and statistical modelling for improved crop yield. This project plans to develop mathematical tools that will help to identify cereal plant varieties with the highest yield. This is a critical responsibility of plant breeders and many Australian breeders acquire and store important information related to the issue. However, there are as yet no mathematical tools that are able to co-analyse the heterogeneous and high-dimensional data in order to understand how external and int ....Co-variant analysis and statistical modelling for improved crop yield. This project plans to develop mathematical tools that will help to identify cereal plant varieties with the highest yield. This is a critical responsibility of plant breeders and many Australian breeders acquire and store important information related to the issue. However, there are as yet no mathematical tools that are able to co-analyse the heterogeneous and high-dimensional data in order to understand how external and internal factors correlate with the major growth and development stages at the crop level. This project seeks to develop and implement mathematical and statistical tools to analyse genetic, agronomic and phenomic factors that affect plant performance, to deliver advanced yield prediction.Read moreRead less