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Associate Professor Andries Potgieter
Associate Professor

Andries Potgieter

Email: 
Phone: 
+61 7 344 33465

Overview

Background

Associate Professor Andries Potgieter is a Principal Research Fellow at the Queensland Alliance for Agriculture and Food Innovation (QAAFI) at the University of Queensland. He currently leads and mentor a team of researchers in the areas of seasonal climate forecasting, remote and proximal sensing with applications in the development of crop production outlooks and less risk prone cropping systems across Australia, producing highly cited publications.

With over 30 years of experience, A/Prof Potgieter’s main research interest is in the complex integration of remote sensing technologies, spatial production modelling, climate forecasting systems at a regional scale. In particular, his interest targets agricultural research that enhances the profitability and sustainability of spatial production systems through a better understanding of the linkages and interactions of such systems across a range of spatial (e.g. field, farm, catchment, national), and temporal (i.e. seasons to decades) scales. He is a leader in the field of quantitative eco-physiological systems modelling and has successfully built up a national and international recognised research profile with strong linkages to industry (farmer groups, insurance, seed companies and bulk handlers of commodities) and domestic and national agencies (State governments, ABARES and ABS) as well as international linkages with Ag-Food Canada, Maryland University, USDA, Chinese Academy of Science (CAS), the Chinese Academy of Agricultural Sciences (CAAS) including the UN and FAO.

Recent research projects

  • Spatial and image analysis modelling specifically, phenotyping of sorghum breeding plots through drones and pheno mobile platforms (funded by ARC Centre of Excellence in Translational Photosynthesis)
  • Regional commodity forecasting and crop area estimates for winter and summer crops across the main broad cropping region of Australia (supported by QLD Government)
  • Development of a model to predict and determine the Genetic by Environment characterization of Late Maturity Alpha Amylase (LMA) risk across Australia (GRDC funded)

Previous research

  • Benchmarking and developing of novel metrics for the Insurance industry for hedging farmer’s risk against crop failures due to water stress within a shire)
  • Determining crop water stress within the thermal – crop canopy space at field scale.
  • Determining of food insecure “hotspots” for the SIMELSA project that provided a baseline analysis to help identify highly vulnerable regions across eastern Africa and listing of relevant and actionable issues of potentially high impact for research, development and increased investment.

Availability

Associate Professor Andries Potgieter is:
Not available for supervision

Qualifications

  • Doctor of Philosophy, University of Southern Queensland

Works

Search Professor Andries Potgieter’s works on UQ eSpace

109 works between 1997 and 2025

41 - 60 of 109 works

2021

Journal Article

Detecting sorghum plant and head features from multispectral UAV imagery

Zhao, Yan, Zheng, Bangyou, Chapman, Scott C., Laws, Kenneth, George-Jaeggli, Barbara, Hammer, Graeme L., Jordan, David R. and Potgieter, Andries B. (2021). Detecting sorghum plant and head features from multispectral UAV imagery. Plant Phenomics, 2021 9874650, 9874650-14. doi: 10.34133/2021/9874650

Detecting sorghum plant and head features from multispectral UAV imagery

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

Scaling up high-throughput phenotyping for abiotic stress selection in the field

2021

Journal Article

Evolution and application of digital technologies to predict crop type and crop phenology in agriculture

Potgieter, A. B., Zhao, Yan, Zarco-Tejada, Pablo J, Chenu, Karine, Zhang, Yifan, Porker, Kenton, Biddulph, Ben, Dang, Yash P., Neale, Tim, Roosta, Fred and Chapman, Scott (2021). Evolution and application of digital technologies to predict crop type and crop phenology in agriculture. In Silico Plants, 3 (1) diab017, 1-23. doi: 10.1093/insilicoplants/diab017

Evolution and application of digital technologies to predict crop type and crop phenology in agriculture

2021

Conference Publication

Domain adaptation for plant organ detection with style transfer

James, Chrisbin, Gu, Yanyang, Chapman, Scott, Guo, Wei, David, Etienne, Madec, Simon, Potgieter, Andries and Eriksson, Anders (2021). Domain adaptation for plant organ detection with style transfer. International Conference on Digital Image Computing - Techniques and Applications (DICTA), Online, 29 November - 1 December 2021. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA52665.2021.9647293

Domain adaptation for plant organ detection with style transfer

2020

Journal Article

High-throughput phenotyping of dynamic canopy traits associated with stay-green in grain sorghum

Liedtke, J. D., Hunt, C. H., George-Jaeggli, B., Laws, K., Watson, J., Potgieter, A. B., Cruickshank, A. and Jordan, D. R. (2020). High-throughput phenotyping of dynamic canopy traits associated with stay-green in grain sorghum. Plant Phenomics, 2020 4635153, 1-10. doi: 10.34133/2020/4635153

High-throughput phenotyping of dynamic canopy traits associated with stay-green in grain sorghum

2020

Journal Article

Predicting wheat yield at the field scale by combining high-resolution sentinel-2 satellite imagery and crop modelling

Zhao, Yan, Potgieter, Andries B., Zhang, Miao, Wu, Bingfang and Hammer, Graeme L. (2020). Predicting wheat yield at the field scale by combining high-resolution sentinel-2 satellite imagery and crop modelling. Remote Sensing, 12 (6) 1024, 1024. doi: 10.3390/rs12061024

Predicting wheat yield at the field scale by combining high-resolution sentinel-2 satellite imagery and crop modelling

2020

Conference Publication

Quantifying drought tolerant crop traits using sensing technologies to enhance selection in sorghum breeding trials

Potgieter, A.B., Laws, Kenneth, George-Jaeggli, Barbara, Hunt, Colleen, Reynolds Massey-Reed, Sean, Lamprecht, Marnie, Liedtke, Jana D., Zhao, Yan, Chapman, Scott, Borrell, Andrew K., Mace, Emma S., Hammer, Graeme L. and Jordan, David R. (2020). Quantifying drought tolerant crop traits using sensing technologies to enhance selection in sorghum breeding trials. Interdrought 2020, Mexico City, Mexico, 9-13 March 2020.

Quantifying drought tolerant crop traits using sensing technologies to enhance selection in sorghum breeding trials

2020

Book Chapter

Sorghum management systems and production technology around the globe

Ciampitti, I. A., Prasad, P. V. Vara, Kumar, S. R., Kubsad, V. S., Adam, M., Eyre, J. X., Potgieter, A. B., Clarke, S. J. and Gambin, B. (2020). Sorghum management systems and production technology around the globe. Sorghum in the 21st Century: Food - Fodder - Feed - Fuel for a Rapidly Changing World. (pp. 251-293) edited by Vilas A. Tonapi, Harvinder Singh Talwar, Ashok Kumar Are, B. Venkatesh Bhat, Ch. Ravinder Reddy and Timothy J. Dalton. Singapore: Springer. doi: 10.1007/978-981-15-8249-3_11

Sorghum management systems and production technology around the globe

2019

Journal Article

An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia

Armstrong, Robert N., Potgieter, Andries B., Mares, Daryl J., Mrva, Kolumbina, Brider, Jason and Hammer, Graeme L. (2019). An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia. Crop and Pasture Science, 71 (1), 1-11. doi: 10.1071/cp19005

An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia

2019

Journal Article

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Furbank, Robert T., Jimenez‐Berni, Jose A., George‐Jaeggli, Barbara, Potgieter, Andries B. and Deery, David M. (2019). Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytologist, 223 (4) nph.15817, 1714-1727. doi: 10.1111/nph.15817

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

2019

Journal Article

Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

Cai, Yaping, Guan, Kaiyu, Lobell, David, Potgieter, Andries B., Wang, Shaowen, Peng, Jian, Xu, Tianfang, Asseng, Senthold, Zhang, Yongguang, You, Liangzhi and Peng, Bin (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and Forest Meteorology, 274, 144-159. doi: 10.1016/j.agrformet.2019.03.010

Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

2019

Journal Article

A weakly supervised deep learning framework for sorghum head detection and counting

Ghosal, Sambuddha, Zheng, Bangyou, Chapman, Scott C., Potgieter, Andries B., Jordan, David R., Wang, Xuemin, Singh, Asheesh K., Singh, Arti, Hirafuji, Masayuki, Ninomiya, Seishi, Ganapathysubramanian, Baskar, Sarkar, Soumik and Guo, Wei (2019). A weakly supervised deep learning framework for sorghum head detection and counting. Plant Phenomics, 2019 1525874, 1525874-14. doi: 10.34133/2019/1525874

A weakly supervised deep learning framework for sorghum head detection and counting

2019

Journal Article

Editorial: Building and delivering real-world, integrated sustainability solutions: Insights, methods and case-study applications

Newlands, Nathaniel K., Porcelli, Tracy A., Potgieter, Andries B., Kouadio, Louis, Huete, Alfredo and Guo, Wei (2019). Editorial: Building and delivering real-world, integrated sustainability solutions: Insights, methods and case-study applications. Frontiers in Environmental Science, 7 (May) 71. doi: 10.3389/fenvs.2019.00071

Editorial: Building and delivering real-world, integrated sustainability solutions: Insights, methods and case-study applications

2019

Conference Publication

High-throughput phenotyping of canopy radiation use efficiency and its component traits

George-Jaeggli, Barbara, Potgieter, Andries, Zhi, Xiaoyu, Reynolds Massey-Reed, Sean, Watson, James, Lamprecht, Marnie, Chapman, Scott, Laws, Kenneth, Hunt, Colleen, Borrell, Andrew, Jordan, David, van Oosterom, Erik, Wu, Alex and Hammer, Graeme (2019). High-throughput phenotyping of canopy radiation use efficiency and its component traits. 6th International Plant Phenotyping Symposium, Nanjing, China, 22-26 October 2019.

High-throughput phenotyping of canopy radiation use efficiency and its component traits

2019

Conference Publication

Determining of targeted crop characteristics utilising sensing technologies to enhance selection of higher yielding varieties in sorghum breeding trials

Potgieter, Andries, Laws, Kenneth, George-Jaeggli, Barbara, Hunt, Colleen, Guo, Wei, Watson, James, Reynolds Massey-Reed, Sean, Chapman, Scott, Hammer, Graeme and Jordan, David (2019). Determining of targeted crop characteristics utilising sensing technologies to enhance selection of higher yielding varieties in sorghum breeding trials. Australian Summer Grains Conference, Gold Coast, QLD, Australia, 8-10 July 2019.

Determining of targeted crop characteristics utilising sensing technologies to enhance selection of higher yielding varieties in sorghum breeding trials

2019

Book Chapter

The use of hyperspectral proximal sensing for phenotyping of plant breeding trials

Potgieter, Andries B., Watson, James, George-Jaeggli, Barbara, McLean, Gregory, Eldridge, Mark, Chapman, Scott C., Laws, Kenneth, Christopher, Jack, Chenu, Karine, Borrell, Andrew, Hammer, Graeme and Jordan, David R. (2019). The use of hyperspectral proximal sensing for phenotyping of plant breeding trials. Fundamentals, sensor systems, spectral libraries, and data mining for vegetation. (pp. 127-148) edited by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete. Boca Raton, FL United States: CRC Press. doi: 10.1201/9781315164151-5

The use of hyperspectral proximal sensing for phenotyping of plant breeding trials

2019

Conference Publication

High-throughput phenotyping tools to test whether leaf-level photosynthesis traits are measurable at the crop level

George-Jaeggli, Barbara, Potgieter, Andries, Zhi, Xiaoyu, Reynolds Massey-Reed, Sean, Watson, James, Lamprecht, Marnie, Chapman, Scott, Laws, Kenneth, Hunt, Colleen, Borrell, Andrew, Jordan, David, van Oosterom, Erik, Wu, Alex and Hammer, Graeme (2019). High-throughput phenotyping tools to test whether leaf-level photosynthesis traits are measurable at the crop level. TropAg 2019, Brisbane, QLD Australia, 10-13 November 2019.

High-throughput phenotyping tools to test whether leaf-level photosynthesis traits are measurable at the crop level

2019

Conference Publication

Seeing canopy photosynthesis through the eyes of a Gecko

George-Jaeggli, Barbara, Zhi, Xiaoyue, Wu, Alex, Potgieter, Andries, Reynolds Massey-Reed, Sean, Watson, James, Hunt, Colleen, Lamprecht, Marnie, Chapman, Scott, Borrell, Andrew, Jordan, David and Hammer, Graeme (2019). Seeing canopy photosynthesis through the eyes of a Gecko. Innovations in Agriculture for Food Security, Brisbane, QLD Australia, 30 June - 3 July 2019.

Seeing canopy photosynthesis through the eyes of a Gecko

2019

Conference Publication

Predicting lodging using sensing technologies to enhance selection in sorghum breeding trials

Potgieter, Andries, Laws, Kenneth, George-Jaeggli, Barbara, Hunt, Colleen, Guo, Wei, Reynolds Massey-Reed, Sean, Chapman, Scott, Borrell, Andrew, Mace, Emma, Hammer, Graeme and Jordan, David (2019). Predicting lodging using sensing technologies to enhance selection in sorghum breeding trials. 6th International Plant Phenotyping Symposium, Nanjing, China, 22-26 October 2019.

Predicting lodging using sensing technologies to enhance selection in sorghum breeding trials

2018

Journal Article

Aerial imagery analysis – quantifying appearance and number of sorghum heads for applications in breeding and agronomy

Guo, Wei, Zheng, Bangyou, Potgieter, Andries B., Diot, Julien, Watanabe, Kakeru, Noshita, Koji, Jordan, David R., Wang, Xuemin, Watson, James, Ninomiya, Seishi and Chapman, Scott C. (2018). Aerial imagery analysis – quantifying appearance and number of sorghum heads for applications in breeding and agronomy. Frontiers in Plant Science, 9 1544, 1544. doi: 10.3389/fpls.2018.01544

Aerial imagery analysis – quantifying appearance and number of sorghum heads for applications in breeding and agronomy

Funding

Current funding

  • 2024 - 2025
    RiskSSmart: Digital tool for de-risking sorghum production decisions (SMART SAT CRC)
    SmartSat CRC
    Open grant
  • 2024 - 2029
    ARC Training Centre in Predictive Breeding for Agricultural Futures
    ARC Industrial Transformation Training Centres
    Open grant
  • 2024 - 2027
    Root structure and function traits: Overcoming the root phenotyping bottleneck in cereals
    PROC-9176895 Phenomics methods and tools to enable improved resource capture efficiency in grain crops
    Open grant
  • 2023 - 2027
    Analytics for the Australian Grains Industry (AAGI)
    Grains Research & Development Corporation
    Open grant
  • 2021 - 2027
    Reducing lodging risk in sorghum to increase grower confidence and profitability
    Grains Research & Development Corporation
    Open grant
  • 2021 - 2025
    CropVision: A next-generation system for predicting crop production
    ARC Linkage Projects
    Open grant

Past funding

  • 2020 - 2024
    CropPhen: Remote mapping of grain crop type and phenology
    Grains Research & Development Corporation
    Open grant
  • 2020 - 2022
    Machine learning to extract maximum value from soil and crop variability (GRDC project administered by The University of Adelaide).
    University of Adelaide
    Open grant
  • 2020 - 2023
    LMA Project C: An improved model of Late Maturity Alpha-Amylase (LMA) field risk in Australian wheat
    Grains Research & Development Corporation
    Open grant
  • 2015 - 2019
    A Genetic x Environment characterization of the risk for Late Maturity Alpha-amylase across the main wheat producing shires of Australia
    Grains Research & Development Corporation
    Open grant
  • 2014 - 2019
    Sustainable intensification of maize-legume cropping systems for food security in Eastern and Southern Africa - Phase II (SIMLESA-2)
    International Maize and Wheat Improvement Centre
    Open grant
  • 2013
    Seeing is believing: The use of thermal sensing in predicting crop yield at field scale
    UQ Early Career Researcher
    Open grant
  • 2012 - 2015
    Integrating crop and livestock production for improved food security and livelihoods in rural Zimbabwe (ZimLESA; ACIAR project led by ILRI)
    International Livestock Research Institute
    Open grant
  • 2012 - 2014
    Sustainable intensification of maize-legume cropping system for food security in Eastern and Southern Africa (SIMLESA) - Ethiopian Extension
    International Maize and Wheat Improvement Centre
    Open grant
  • 2011 - 2012
    Determining the ability of crop water stress indices to capture yield, within the crop canopy/thermal space, through utilising remote sensing at a farm/paddock level
    UQ New Staff Research Start-Up Fund
    Open grant
  • 2011
    An analysis of spatial data for Zimbabwe (ACIAR project)
    Queensland Department of Employment, Economic Development and Innovation
    Open grant
  • 2010 - 2014
    Sustainable intensification of maize-legume cropping systems for enhancing food security in eastern and southern Africa (SIMLESA; CIMMYT-led ACIAR Project novated from DEEDI)
    International Maize and Wheat Improvement Centre
    Open grant
  • 2010
    Strategic Planning for investment based on agro-ecological zones
    Queensland Department of Employment, Economic Development and Innovation
    Open grant

Supervision

Availability

Associate Professor Andries Potgieter is:
Not available for supervision

Supervision history

Current supervision

Completed supervision

Media

Enquiries

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