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About

G. Polder is a leading researcher in agricultural robotics and precision farming, with a focus on using spectral imaging, computer vision, and autonomous systems for plant phenotyping and disease detection. His work bridges the gap between sensor technology and practical field applications, particularly in weed control and crop health monitoring. Polder’s most cited paper (85 citations) reviews spectral imaging for disease detection using autonomous platforms, while his highly influential work on detecting and controlling broad-leaved dock in grassland (73 citations) demonstrates the real-world impact of his vision-based robotic systems. He has also made significant contributions to automatic phenotyping of tomatoes in production greenhouses (44 citations), advancing high-throughput methods for breeders and growers. Notable achievements include developing a mobile field robot for detecting volunteer potato plants in corn crops (35 citations) and using optical sensors to detect tulip breaking virus (23 citations), a major economic threat in the Netherlands. His work on the PhenoBot system for 3D phenotyping of large tomato plants further showcases his innovation in combining robotics with advanced imaging. With a career spanning over two decades, Polder’s research continues to shape the future of smart agriculture, enabling more efficient, non-chemical weed control and automated crop monitoring.