Harry Taylor


Conventional backcross breeding relies on visual selection of desired phenotypic traits in those plants closest in appearance to the recurrent parent. Consequently, there is an unwanted introduction of human error in addition to lengthy times between sowing plants to the selection stage. Our work aims to develop genetically informed machine learning models for bread wheat (T. aestivum) growth in field conditions to streamline this selection process.

A genomic selection model will allow phenotypic traits to be predicted in unseen genotypes and dramatically reduce the time needed to complete a breeding cycle. Combining this approach with available long-term weather data will also help to rapidly translate the breeding potential of adapted germplasm for deployment to the best environment in an increasingly unstable climate.  

Previous research focuses concerned the molecular events that occur during the gravitropic response, using fluorescently labelled plastid markers and cell type-targeted GCaMP3 calcium reporters in Arabidopsis.

Research interests

Crop genetics, molecular and cell biology, in both fundamental and applied contexts.  


  • MSc Plant Science and Biotechnology from the University of Leeds
  • BSc Biology (International) from the University of Leeds (Queensland University of Technology)