Rate My Professor Henrik Karstoft

His research centers on signal processing, machine learning, and computer vision, applied to biosystems including plant species identification, insect monitoring, embryo grading, agricultural robotics, and remote sensing. Notable projects include the FLYgene initiative (2022-2026) for sustainable insect production through selective breeding. Karstoft has produced 108 research outputs, including 48 journal articles and 21 conference papers. Key publications encompass 'Automatic grading of human blastocysts from time-lapse imaging' (Kragh et al., 2019), 'Embryo selection with artificial intelligence: how to evaluate and validate a model ready for clinical use' (Kragh and Karstoft, 2021), 'Plant species classification using deep convolutional neural network' (Dyrmann et al., 2016), 'Using Deep Learning to Challenge Safety Standard for Acute Hazard Detection at Swing Sawing' (Steen et al., 2016), and 'A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments' (Bjerge et al., 2024). His work has advanced automated monitoring and prediction in biological and agricultural contexts, with applications in precision farming, wildlife monitoring, and reproductive medicine.