Understanding the genetics of mungbean canopy dynamics using longitudinal UAV data
Abstract
Mungbean is an important pulse crop that is a high-quality plant protein source and a major high-value export crop for many growers globally. While substantial yield improvements have been made, the rate of genetic improvement in mungbean is limited and could be further advanced by overcoming the phenotyping bottleneck associated with ground-based methods. Traditional visual phenotyping approach limits a breeder’s capacity to identify complex traits that could provide a yield benefit in different environments and agricultural systems. Unmanned aerial vehicles (UAV) offer a powerful tool to support efficient, non-invasive, field-based phenotyping, of canopy traits across the growing season. This technology has been successfully applied to major cereal crops, however their potential to advance the breeding of many pulse crops, such as mungbean, is yet to be explored. This study investigated the utility of UAV-based imaging to identify and dissect the genetics of promising canopy traits that underpin yield. A diverse nested association mapping (NAM) population was subjected to weekly UAV flights using a multi-spectral camera. Several vegetative indices (VIs) (i.e. OSAVI, NDRE and thermal) were associated with important canopy traits at specific time-points, such as vigour and canopy cover. Spline curve fitting was used to integrate the extracted VIs from single flights into a continuous time course. A range of canopy parameters were then examined across genotypes, including canopy growth rates and senescence, as well as the identification of key growth stages when these UAV-derived traits are most predictive of yield. Finally, a genome-wide association study identified key genomic regions that could be targeted in breeding programs to modulate canopy development. This study highlights the potential for mungbean improvement programs to scale up phenotyping of canopy traits using UAVs, that could be integrated with genomic selection strategies to accelerate genetic gain.
Mrs Shanice Van Haeften
Shanice is a PhD student at the Queensland Alliance of Agriculture and Food Innovation Institute (University of Queensland). Her PhD research focuses on using new innovative tools to increase the productivity and reliability of mungbean production for Australian growers. Prior to commencing her PhD, Shanice completed a Bachelor of Science and Bachelor of Business with Honours at the Queensland University of Technology. Shanice is passionate about improving global food security and using her diverse background and skillset, she strives to be a part of developing solutions to sustainably feed communities in the future!
Mrs Shanice Van Haeften, PhD Candidate, Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation E: s.vanhaeften@uq.edu.au
For any questions, please contact the QAAFI Science Seminar Committee.
For any questions, please contact the QAAFI Science Seminar Committee.
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