Overview
Background
I am a Postdoctoral Research Fellow at the University of Queensland, specialising in crop physiology, remote sensing, and high-throughput phenotyping. My work focuses on using drone-based imaging systems, 3D modelling, and machine learning to estimate complex plant traits in the field. I currently lead the UQ node of the Australian Plant Phenomics Network (APPN), where I support a range of research projects focused on improving how we measure crop performance. My recent work has involved developing UAV-based pipelines to estimate biomass and radiation-use efficiency in wheat, and applying image-based methods to improve trait prediction in a range of crops.
My areas of expertise include:
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UAV and sensor-based crop monitoring
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Multispectral and RGB imagery analysis
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Data pipelines for variety trials
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Field-based trait modelling and phenotyping automation
Availability
- Dr Daniel Smith is:
- Available for supervision
Qualifications
- Bachelor (Honours) of Agricultural Science, University of Queensland
Research impacts
My research contributes to more efficient and scalable ways of measuring crop performance in the field, supporting breeders, agronomists, and researchers working in complex environments. Through the use of UAVs, multispectral cameras, and analytical pipelines, I’ve helped reduce the reliance on manual and destructive sampling methods in variety trials. These tools enable earlier and more consistent assessment of traits like biomass and canopy development.
As an early-career researcher, I’m focused on building collaborations and developing tools that support both fundamental discovery and real-world application in crop science.
Works
Search Professor Daniel Smith’s works on UQ eSpace
2021
Journal Article
Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods
David, Etienne, Serouart, Mario, Smith, Daniel, Madec, Simon, Velumani, Kaaviya, Liu, Shouyang, Wang, Xu, Pinto, Francisco, Shafiee, Shahameh, Tahir, Izzat S. A., Tsujimoto, Hisashi, Nasuda, Shuhei, Zheng, Bangyou, Kirchgessner, Norbert, Aasen, Helge, Hund, Andreas, Sadhegi-Tehran, Pouria, Nagasawa, Koichi, Ishikawa, Goro, Dandrifosse, Sébastien, Carlier, Alexis, Dumont, Benjamin, Mercatoris, Benoit, Evers, Byron, Kuroki, Ken, Wang, Haozhou, Ishii, Masanori, Badhon, Minhajul A., Pozniak, Curtis ... Guo, Wei (2021). Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods. Plant Phenomics, 2021 9846158, 1-9. doi: 10.34133/2021/9846158
2021
Journal Article
Scaling up high-throughput phenotyping for abiotic stress selection in the field
Smith, Daniel T., Potgieter, Andries B. and Chapman, Scott C. (2021). Scaling up high-throughput phenotyping for abiotic stress selection in the field. Theoretical and Applied Genetics, 134 (6), 1845-1866. doi: 10.1007/s00122-021-03864-5
2019
Conference Publication
Assessing the use efficiency of Enhanced Efficiency Nitrogen Fertilisers (EENFs) in irrigated maize
Martinez, Cristina, Dang, Yash, Smith, Daniel and Bell, Mike (2019). Assessing the use efficiency of Enhanced Efficiency Nitrogen Fertilisers (EENFs) in irrigated maize. TropAg2019, Brisbane, Australia, 11-13 November 2019.
Supervision
Availability
- Dr Daniel Smith is:
- Available for supervision
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Media
Enquiries
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