Overview
Background
Professor Andries B. Potgieter is a Principal Research Fellow at the Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland, and an international leader in Digital Agriculture. With a career spanning over 35 years across government, industry, and academia, his research integrates remote sensing, climate forecasting, and crop–climate modelling to support resilient, data-driven decision-making in agriculture. He is currently a key research collaborator in the $36 million GRDC-funded Analytics for the Australian Grains Industry (AAGI) initiative, where he leads digital analytics activities within UQ.
Professor Potgieter’s work focuses on developing predictive tools that combine satellite Earth observation, machine learning, and crop simulation to improve seasonal forecasting, crop monitoring, and risk management. He has pioneered widely adopted innovations such as the CropID tool, now commercialised via Data Farming Pty Ltd, and his models have influenced decision frameworks at Statistics Canada and the FAO. His 114 peer-reviewed publications have accrued over 4,000 citations, and his Field-Weighted Citation Impact (FWCI) places him in the top 5% of researchers globally.
He has built a thriving interdisciplinary research program and mentoring pipeline, supervising PhD, Masters, and MoDS students, and supporting postdoctoral researchers who now work at AWS, Sugar Research Australia, and the Chinese Academy of Agricultural Sciences. His leadership in global partnerships has positioned UQ as a preferred academic collaborator for international institutions tackling climate-smart agriculture.
Current projects
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Analytics for the Australian Grains Industry (AAGI) – Digital analytics for yield forecasting and decision tools for grain growers (GRDC)
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CropVision – Satellite remote sensing and AI for field-scale crop production forecasting (ARC Linkage)
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RiskSSmart – Integration of Earth observation and climate models for sorghum risk mitigation (SmartSat CRC)
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Root Phenomics – Linking above-ground sensing to root system architecture to accelerate phenotyping of drought-tolerant cereals (GRDC; Chief Investigator)
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ARC Training Centre for Predictive Breeding in Agricultural Futures – Developing next-generation tools and training pathways for climate-resilient crop improvement (ARC Industrial Transformation Training Centres; Collaborating Investigator)
Previous research highlights
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Late Maturity Alpha Amylase (LMA) Risk Modelling – National-scale risk prediction framework for wheat quality (GRDC)
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CropPhen – High-throughput phenotyping for crop type and growth stage detection via drone/UAV (GRDC)
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SIMLESA and YieldShield – Groundbreaking work in food insecurity mapping and climate risk insurance across eastern and southern Africa
Availability
- Associate Professor Andries Potgieter is:
- Available for supervision
Fields of research
Qualifications
- Doctor of Philosophy, University of Southern Queensland
Research impacts
Transforming agricultural decision-making through digital tools My research has led to practical innovations that help farmers, government agencies, and agribusinesses make better decisions under climate uncertainty. By combining satellite imagery, climate data, and artificial intelligence, I have created tools that support crop forecasting, risk assessment, and sustainable farming practices at local, national, and global levels.
Real-world outcomes
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CropID, a remote sensing tool developed through my research, has been commercialised by Data Farming Pty Ltd and is now used across Australia to map and monitor crops—helping reduce fertiliser waste and improve land management.
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My seasonal yield forecasting models have been adopted by the Queensland Government and informed policy and logistics planning.
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Statistics Canada built their national crop forecasting system using frameworks I co-developed, showing international uptake.
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I co-developed YieldShield™, Australia’s first climate-based crop insurance product, which helped farmers manage drought risk.
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In 2024–2025, I was contracted by the FAO to adapt these models for use in low-income countries, supporting food security initiatives in Africa and Latin America.
Broader benefits
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Economic impact: My tools improve supply chain planning, reduce crop losses, and inform commodity trading and insurance.
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Environmental impact: Precision monitoring enables more efficient fertiliser use, reducing runoff and environmental harm.
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Social impact: My training and mentoring of students and early-career researchers builds long-term capability in digital agriculture, both in Australia and internationally.
Works
Search Professor Andries Potgieter’s works on UQ eSpace
2023
Other Outputs
Field trial raw and processed datasets for 2020 winter field season
Armstrong, Robert, Potgieter, Andries, Hammer, Graeme, Mortlock, Miranda, Biddulph, Ben, Curry, Jeremy, Height, Nathan, McCallum, Melissa, Porker, Kenton, Harris, Felicity, Bathgate, Jordan, Simpson, Jess, Clarke, Genevieve, Giblot-Ducray, Danièle, Fairlie, William, Hughes, David and Cullis, Brian (2023). Field trial raw and processed datasets for 2020 winter field season. The University of Queensland. (Dataset) doi: 10.48610/721f2e2
2023
Other Outputs
LMA Risk maps at Shire scale
Armstrong, Robert, Potgieter, Andries, Brider, Jason and Hammer, Graeme (2023). LMA Risk maps at Shire scale. The University of Queensland. (Dataset) doi: 10.48610/1774e6f
2023
Other Outputs
Late Maturity alpha-Amylase winter wheat data from field trials conducted at six locations in Australia
Armstrong, Robert, Potgieter, Andries, Hammer, Graeme, Mortlock, Miranda, Biddulph, Ben, Curry, Jeremy, Height, Nathan, McCallum, Melissa, Porker, Kenton, Harris, Felicity, Bathgate, Jordan, Simpson, Jess, Clarke, Genevieve, Giblot-Ducray, Danièle, Fairlie, William, Hughes, David and Cullis, Brian (2023). Late Maturity alpha-Amylase winter wheat data from field trials conducted at six locations in Australia. The University of Queensland. (Dataset) doi: 10.48610/6769356
2023
Journal Article
From prototype to inference: a pipeline to apply deep learning in sorghum panicle detection
James, Chrisbin, Gu, Yanyang, Potgieter, Andries, David, Etienne, Madec, Simon, Guo, Wei, Baret, Frédéric, Eriksson, Anders and Chapman, Scott (2023). From prototype to inference: a pipeline to apply deep learning in sorghum panicle detection. Plant Phenomics, 5 0017, 1-16. doi: 10.34133/plantphenomics.0017
2022
Journal Article
Challenges and opportunities in remote sensing-based crop monitoring: a review
Wu, Bingfang, Zhang, Miao, Zeng, Hongwei, Tian, Fuyou, Potgieter, Andries B., Qin, Xingli, Yan, Nana, Chang, Sheng, Zhao, Yan, Dong, Qinghan, Boken, Vijendra, Plotnikov, Dmitry, Guo, Huadong, Wu, Fangming, Zhao, Hang, Deronde, Bart, Tits, Laurent and Loupian, Evgeny (2022). Challenges and opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10 (4) nwac290, 1-34. doi: 10.1093/nsr/nwac290
2022
Journal Article
Cover Image
Wu, Alex, Brider, Jason, Busch, Florian A., Chen, Min, Chenu, Karine, Clarke, Victoria C., Collins, Brian, Ermakova, Maria, Evans, John R., Farquhar, Graham D., Forster, Britta, Furbank, Robert T., Groszmann, Michael, Hernandez‐Prieto, Miguel A., Long, Benedict M., Mclean, Greg, Potgieter, Andries, Price, G. Dean, Sharwood, Robert E., Stower, Michael, van Oosterom, Erik, von Caemmerer, Susanne, Whitney, Spencer M. and Hammer, Graeme L. (2022). Cover Image. Plant, Cell & Environment, 46 (1). doi: 10.1111/pce.14512
2022
Conference Publication
Exploring the potential of using UAV phenotyping platforms to support breeding for improved root systems
Kang, Y., Alahmad, S., Smith, D., van Haeften, S., Rambla, C., Meer, S.V., Smith, M., Christopher, J., Chenu K, Able, J.A., Voss-Fels, K.P., Jordan, D.R., Borrell, A.K., Chapman, S., Potgieter, A.B., Wasson, A. and Hickey, L.T. (2022). Exploring the potential of using UAV phenotyping platforms to support breeding for improved root systems. 7th International Plant Phenotyping Symposium, Wageningen, Netherlands, 27-30 November 2022.
2022
Conference Publication
Physiological insight to improve mungbean productivity
Smith, Millicent, Van Haeften, Shanice, Dudley, Caitlin, Douglas, Colin, Ryan, Merrill, Potgieter, Andries, Robinson, Hannah and Hickey, Lee (2022). Physiological insight to improve mungbean productivity. TropAg International Agriculture Conference, Brisbane, QLD Australia, 31 October - 2 November 2022.
2022
Conference Publication
Phenotyping innovations to harness genetic diversity for climate resilience
Smith, Millicent, Campbell, Bradley, Restall, Jemma, Dudley, Caitlin, Van Haeften, Shanice, Godwin, Ian, Chapman, Scott, Sukal, Amit, Robinson, Hannah, Ryan, Merrill, Potgieter, Andries and Hickey, Lee (2022). Phenotyping innovations to harness genetic diversity for climate resilience. International Plant Phenotyping Symposium, Wageningen, Netherlands, 27-30 September 2022.
2022
Conference Publication
Understanding the genetics of mungbean canopy development using longitudinal UAV data
Van Haeften, Shanice, Kang, Yichen, Douglas, Colin, Dudley, Caitlin, Ryan, Merrill, Smith, Millicent, Chapman, Scott, Robinson, Hannah, Potgieter, Andries and Hickey, Lee (2022). Understanding the genetics of mungbean canopy development using longitudinal UAV data. International Plant Phenotyping Symposium, Wageningen, Netherlands, 27-30 September 2022.
2022
Conference Publication
From lab to field: a major QTL to modify root system architecture in elite durum wheat
Kang, Y., Alahmad, S., Aldiss, Z., Christopher, J., Chenu, K., Able, J.A., Voss-Fels, K.P., Potgieter, A.B., Jordan, D.R., Borrell, A.K., Wasson, A. and Hickey, L.T. (2022). From lab to field: a major QTL to modify root system architecture in elite durum wheat. International Wheat Congress, Beijing, China, 11-15 September 2022.
2022
Journal Article
Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis
Potgieter, Andries B, Schepen, Andrew, Brider, Jason and Hammer, Graeme L (2022). Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis. Agricultural and Forest Meteorology, 324 109116, 1-12. doi: 10.1016/j.agrformet.2022.109116
2022
Conference Publication
Exploring root-shoot dynamics to enhance yield potential and stability of future wheat cultivars
Alahmad, S., Kang, Y., Rambla, C., Meer, S.V., Smith, M., Christopher, J., Chenu, K., Able, J.A., Voss-Fels, K.P., Jordan, D.R., Borrell, A.K., Smith, D., Chapman, S., Watt, M., Ober, E., Potgieter, A.B., Wasson, A. and Hickey, L.T. (2022). Exploring root-shoot dynamics to enhance yield potential and stability of future wheat cultivars. Wheat Breeding Assembly, Narrabri, NSW Australia, 28-31 August 2022.
2022
Conference Publication
A major QTL to modify root system architecture in elite durum wheat
Kang, Y., Alahmad, S., Aldiss, Z., Christopher, J., Chenu, K., Able, J.A., Voss-Fels, K.P., Potgieter, A.B., Jordan, D.R., Borrell, A.K., Wasson, A. and Hickey, L.T. (2022). A major QTL to modify root system architecture in elite durum wheat. Wheat Breeding Assembly, Narrabri, NSW Australia, 28-31 August 2022.
2022
Journal Article
Genetic basis of sorghum leaf width and its potential as a surrogate for transpiration efficiency
Zhi, Xiaoyu, Hammer, Graeme, Borrell, Andrew, Tao, Yongfu, Wu, Alex, Hunt, Colleen, van Oosterom, Erik, Massey-Reed, Sean Reynolds, Cruickshank, Alan, Potgieter, Andries B., Jordan, David, Mace, Emma and George-Jaeggli, Barbara (2022). Genetic basis of sorghum leaf width and its potential as a surrogate for transpiration efficiency. Theoretical and Applied Genetics, 135 (9), 3057-3071. doi: 10.1007/s00122-022-04167-z
2022
Conference Publication
New technologies to accelerate mungbean improvement
Van Haeften, Shanice, Dudley, Caitlin, Douglas, Colin, Udvardi, Michael, Massel, Karen, Beveridge, Christine, Robinson, Hannah, Hickey, Lee, Potgieter, Andries and Smith, Millicent (2022). New technologies to accelerate mungbean improvement. Queensland Legume Symposium, Brisbane, QLD, Australia, 22 July 2022.
2022
Conference Publication
UAV-based technologies to reveal the secrets of mungbean yield
Van Haeften, Shanice, Hickey, Lee, Douglas, Colin, Smith, Millicent, Robinson, Hannah and Potgieter, Andries (2022). UAV-based technologies to reveal the secrets of mungbean yield. Australasian Plant Breeding Conference, Gold Coast, QLD, Australia, 9-11 May 2022.
2022
Journal Article
Estimating photosynthetic attributes from high-throughput canopy hyperspectral sensing in sorghum
Zhi, Xiaoyu, Massey-Reed, Sean Reynolds, Wu, Alex, Potgieter, Andries, Borrell, Andrew, Hunt, Colleen, Jordan, David, Zhao, Yan, Chapman, Scott, Hammer, Graeme and George-Jaeggli, Barbara (2022). Estimating photosynthetic attributes from high-throughput canopy hyperspectral sensing in sorghum. Plant Phenomics, 2022 9768502, 1-18. doi: 10.34133/2022/9768502
2022
Conference Publication
Frequency analysis of environment types associated with late-maturity alpha-amylase for Australian wheat under current and future climates
Armstrong, Robert, Hammer, Graeme and Potgieter, Andries (2022). Frequency analysis of environment types associated with late-maturity alpha-amylase for Australian wheat under current and future climates. 20th Australian Agronomy Conference , Toowoomba, QLD Australia, 19-22 September 2022. Australian Society of Agronomy.
2022
Conference Publication
A high-throughput phenotyping pipeline for rapid evaluation of morphological and physiological crop traits across large fields
Das, Sumanta, Massey-Reed, Sean Reynolds, Mahuika, Jenny, Watson, James, Cordova, Celso, Otto, Loren, Zhao, Yan, Chapman, Scott, George-Jaeggli, Barbara, Jordan, David, Hammer, Graeme L. and Potgieter, Andries B. (2022). A high-throughput phenotyping pipeline for rapid evaluation of morphological and physiological crop traits across large fields. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17-22 July 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/IGARSS46834.2022.9884530
Funding
Current funding
Supervision
Availability
- Associate Professor Andries Potgieter is:
- Available for supervision
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Supervision history
Current supervision
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Doctor Philosophy
Determining the effects of abiotic stress on crop growth development, and yield under different nitrogen applications using remotely sensed data for cotton and wheat.
Principal Advisor
Other advisors: Professor Scott Chapman, Dr William Woodgate
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Doctor Philosophy
Insights into the associations between functional above ground plant traits and root function for drought adaptation in sorghum.
Principal Advisor
Other advisors: Professor David Jordan, Dr Barbara George-Jaeggli, Dr Samir Alahmad, Dr Dongxue Zhao
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Doctor Philosophy
On-ground management of soil nutrients by integrating proximal and remote sensing platforms in northern Australian savannas grazing lands
Associate Advisor
Other advisors: Dr Yan Zhao, Professor Susanne Schmidt
Completed supervision
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2025
Doctor Philosophy
New insight and tools to increase yield potential and reliability in mungbean
Associate Advisor
Other advisors: Professor Lee Hickey, Dr Hannah Robinson, Dr Millicent Smith
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2025
Doctor Philosophy
Estimating biomass and radiation-use-efficiency in wheat variety trials using unmanned aerial vehicles
Associate Advisor
Other advisors: Professor Scott Chapman
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2024
Doctor Philosophy
New insights into designer root systems for durum wheat improvement
Associate Advisor
Other advisors: Professor David Jordan, Professor Emma Mace, Dr Samir Alahmad, Professor Lee Hickey
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2023
Master Philosophy
Crop sensing as a tool to assist data collection in maize agronomic trials
Associate Advisor
Other advisors: Dr Joe Eyre, Professor Daniel Rodriguez
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2022
Doctor Philosophy
Hyperspectral sensing methods and genome-wide association studies to improve photosynthetic capacity in sorghum
Associate Advisor
Other advisors: Professor Graeme Hammer, Dr Alex Wu, Dr Barbara George-Jaeggli
Media
Enquiries
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