The development of improved varieties relies on identifying the best performing entries from a breeding population. However, high-throughput phenotyping tools beyond the plot harvester are still rare (White et al., 2012) and not used by Australian breeders. Whilst automated, high-throughput phenotyping platforms in greenhouses for the collection of precise phenotypic data are increasingly available, they operate in fully controlled environments, and QTL and candidate genes identified may not show a positive effect under actual field conditions. Unmanned Aerial Vehicles (UAVs) are a promising alternative: they can be easily transported, carry multiple sensors, and are becoming increasingly affordable (Zaman-Allah et al., 2015). In addition, they can characterize large numbers of plots within minutes which is important to avoid the effect of environmental changes and diurnal physiological processes on observed characteristics.
Develop a drone based phenotyping platform
Develop new image based phenotyping tools
Phenotype field trials
Deployment of UAV based phenotyping tools
We are using a range of sensors on a range of UAV platforms to develop new methods for measuring performance of wheat varieties in the field. As well as utilising high end UAVs with advanced sensors (hyperspec, thermal, high resolution RGB) we are also using consumer grade UAVs (DJI Phantom 4).
The UAV platforms have been used for extensive phenotyping of a limited number of field trials, covering breeders’ trials (and their specific objectives) as well as researchers’ trials (e.g., NUE mapping populations, trials for genome wide association studies (GWAS) and near association mapping (NAM)). Aerial imaging during the growing season is accompanied by ground-based phenotyping of selected plant characteristics.
After acquiring the field image data, extraction of quantitative information from these images is the next critical step. Specific image analysis methods for UAV applications are being developed and implemented with the help of the team at the Phenomics and Bioinformatics Research Centre at the University of South Australia. Targeted is also the development of specific algorithms, focusing on the detection and characterisation of e.g., individual plant organs or of specific features related to abiotic stresses or diseases. Application of statistical and bioinformatics tools will then further improve the image analysis and the automation procedures to correlate field image findings with known information of direct relevance to plant biology. One of the tools already developed enables vegetation indices to be derived from RGB camera data (Khan et al., 2018).
The final product is targeted at breeders, developing guidelines, training programs and a software platform enabling them to characterize their breeding materials faster, better, and cheaper. These tools will also enable researchers to better understand germplasm performance in the target environment.
Trevor Garnett (CI and Program 3 leader, UoA)
Stan Miklavcic (CI, UniSA)
Zohaib Khan (Researcher, UniSA)
Ramesh Raja Segaran (UoA, URAF)
Vahid Rahimi-Eichi (PhD student, UoA)
Yuriy Onyskiv (Technician, UoA)
Sanjiv Satjia (Technician, UoA)
AGT, LongReach, Intergrain
White JW, Anrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldman KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorpe KR. (2012). Field-based phenomics for plant genetics research. Field Crops Res 133: 101-112;
Zaman-Allah M., Vergara O., Araus J.L., Tarekegne A., Magorokosho C., Zarco-Tejada P.J., Hornero A., Albà A.H., Das B., Craufurd P., Olsen M., Prasanna B.M. & Cairns J. (2015) Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11, 35.
Khan Z., Rahimi-Eichi V., Haefele S., Garnett T. & Miklavcic S.J. (2018) Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging. Plant Methods, 14, 20. https://doi.org/10.1186/s13007-018-0287-6