Rate My Professor Andries Potgieter

Associate Professor Andries B. Potgieter serves as Principal Research Fellow at the Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland, in the agriculture domain. He holds a Doctor of Philosophy from the University of Southern Queensland and possesses over 35 years of experience spanning government, industry, and academia. As an international leader in digital agriculture, Potgieter specializes in integrating remote sensing, climate forecasting, and crop-climate modelling to create predictive tools that enable resilient, data-driven decision-making in agriculture. He leads digital analytics efforts within the $36 million GRDC-funded Analytics for the Australian Grains Industry (AAGI) initiative. His innovations include the CropID tool, commercialized through Data Farming Pty Ltd, alongside seasonal yield forecasting models adopted by the Queensland Government and YieldShield, Australia's inaugural climate-based crop insurance product. Potgieter supervises PhD, Masters, and MoDS students, mentoring postdoctoral researchers now affiliated with AWS, Sugar Research Australia, and the Chinese Academy of Agricultural Sciences.

Potgieter's research has shaped decision frameworks at Statistics Canada and the FAO, supporting food security applications in Africa and Latin America. He boasts 114 peer-reviewed publications amassing over 4,000 citations, with a Field-Weighted Citation Impact ranking him in the global top 5% of researchers. Key publications encompass Wang et al. (2023), Modelling the impact of climate change on agriculture in Australia and Oceania; Ciampitti et al. (2020), Sorghum management systems and production technology around the globe; Potgieter et al. (2019), The use of hyperspectral proximal sensing for phenotyping of plant breeding trials; Chapman et al. (2018), Visible, near infrared, and thermal spectral radiance on-board UAVs for high-throughput phenotyping of plant breeding trials; and recent works such as Ashourloo et al. (2026), Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing, and Van Haeften et al. (2025), Unmanned aerial vehicle phenotyping of agronomic and physiological traits in mungbean. Ongoing projects include CropVision for AI-enhanced field-scale crop production forecasting, RiskSSmart for sorghum risk mitigation via Earth observation and climate models, and Root Phenomics to accelerate phenotyping of drought-tolerant cereals. His contributions advance crop monitoring, high-throughput phenotyping, and risk management for grains including wheat, sorghum, and mungbean.