Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location
The study highlights the promising potential of UAV technology in biomass prediction for wheat at a small plot scale. It suggests that the accuracy of biomass predictions can be significantly improved through optimizing analytical and modelling protocols (i.e., variable selection, algorithm selection, stage-specific model development). Future work should focus on exploring the applicability of these findings under a wider variety of conditions and from a more diverse set of genotypes.
Authors
- Daniel Smith (ORCID: https://orcid.org/0000-0002-5867-9613)
- Scott Chapman (ORCID: https://orcid.org/0000-0003-4732-8452)
- Andries B. Potgieter (ORCID: https://orcid.org/0000-0002-1671-8266)
- Sean Reynolds Massey-Reed (ORCID: https://orcid.org/0000-0003-3988-1303)
- Qiaomin Chen (ORCID: https://orcid.org/0000-0003-0628-8896)
Institutions
- The University of Queensland (AU)
- Agriculture and Food (AU)
Publication Details
- Journal
- Plant Methods
- Published
- 2024-08-20
- DOI
- https://doi.org/10.1186/s13007-024-01236-w
- Citations
- 11
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 5.05
Funders
- Grains Research and Development Corporation