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:
-
UAV and sensor-based crop monitoring
-
Multispectral and RGB imagery analysis
-
Data pipelines for variety trials
-
Field-based trait modelling and phenotyping automation
Availability
- Mr 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
2025
Journal Article
Unmanned aerial vehicle phenotyping of agronomic and physiological traits in mungbean
Van Haeften, Shanice, Smith, Daniel, Robinson, Hannah, Dudley, Caitlin, Kang, Yichen, Douglas, Colin A., Hickey, Lee T., Potgieter, Andries, Chapman, Scott and Smith, Millicent R. (2025). Unmanned aerial vehicle phenotyping of agronomic and physiological traits in mungbean. The Plant Phenome Journal, 8 (1) e70016, 1-18. doi: 10.1002/ppj2.70016
2025
Other Outputs
Estimating biomass and radiation-use-efficiency in wheat variety trials using unmanned aerial vehicles
Smith, Daniel (2025). Estimating biomass and radiation-use-efficiency in wheat variety trials using unmanned aerial vehicles. PhD Thesis, School of Agriculture and Food Sustainability, The University of Queensland. doi: 10.14264/e2a7de4
2024
Journal Article
Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location
Smith, Daniel T. L., Chen, Qiaomin, Massey-Reed, Sean Reynolds, Potgieter, Andries B. and Chapman, Scott C. (2024). Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location. Plant Methods, 20 (1) 129, 129. doi: 10.1186/s13007-024-01236-w
2024
Journal Article
GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping
James, Chrisbin, Smith, Daniel, He, Weigao, Chandra, Shekhar S. and Chapman, Scott C. (2024). GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping. Computers and Electronics in Agriculture, 217 108485, 108485. doi: 10.1016/j.compag.2023.108485
2023
Journal Article
Global wheat head detection challenges: winning models and application for head counting
David, Etienne, Ogidi, Franklin, Smith, Daniel, Chapman, Scott, de Solan, Benoit, Guo, Wei, Baret, Frederic and Stavness, Ian (2023). Global wheat head detection challenges: winning models and application for head counting. Plant Phenomics, 5 0059, 1-14. doi: 10.34133/plantphenomics.0059
2023
Journal Article
Building a better Mungbean: breeding for reproductive resilience in a changing climate
Van Haeften, Shanice, Dudley, Caitlin, Kang, Yichen, Smith, Daniel, Nair, Ramakrishnan M., Douglas, Colin A., Potgieter, Andries, Robinson, Hannah, Hickey, Lee T. and Smith, Millicent R. (2023). Building a better Mungbean: breeding for reproductive resilience in a changing climate. Food and Energy Security, 12 (6) e467. doi: 10.1002/fes3.467
2023
Journal Article
VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation
Madec, Simon, Irfan, Kamran, Velumani, Kaaviya, Baret, Frederic, David, Etienne, Daubige, Gaetan, Samatan, Lucas Bernigaud, Serouart, Mario, Smith, Daniel, James, Chrisbin, Camacho, Fernando, Guo, Wei, De Solan, Benoit, Chapman, Scott C. and Weiss, Marie (2023). VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation. Scientific Data, 10 (1) 302, 1-12. doi: 10.1038/s41597-023-02098-y
2023
Other Outputs
INVITA Core site Ground-Based HTP platform Data
Chapman, Scott and Smith, Daniel (2023). INVITA Core site Ground-Based HTP platform Data. The University of Queensland. (Dataset) doi: 10.48610/346651e
2023
Other Outputs
INVITA Core site UAV dataset
Chapman, Scott and Smith, Daniel (2023). INVITA Core site UAV dataset. The University of Queensland. (Dataset) doi: 10.48610/951f13c
2021
Journal Article
Maize production and nitrous oxide emissions from enhanced efficiency nitrogen fertilizers
Dang, Yash P., Martinez, Cristina, Smith, Daniel, Rowlings, David, Grace, Peter and Bell, Mike (2021). Maize production and nitrous oxide emissions from enhanced efficiency nitrogen fertilizers. Nutrient Cycling in Agroecosystems, 121 (2-3), 191-208. doi: 10.1007/s10705-021-10171-4
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
Supervision
Availability
- Mr Daniel Smith is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Media
Enquiries
For media enquiries about Mr Daniel Smith's areas of expertise, story ideas and help finding experts, contact our Media team: