Rate My Professor Andries Potgieter

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.