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About

Pouya Sohrabipour is a pioneering researcher at the intersection of agricultural robotics, machine learning, and food safety automation. His work focuses on developing intelligent robotic systems that address critical challenges in food production and processing, from field to factory. Sohrabipour’s most impactful contribution is his comprehensive survey on imitation learning (IL) in agricultural robotics (2024, 24 citations), which provides an exhaustive analysis of how robots can learn complex agricultural tasks by observing human demonstrations—a paradigm shift for autonomous farming. He has also introduced novel solutions for food safety monitoring, including a robotic swabbing system integrated with fluorescent sensing to standardize hygiene verification on food contact surfaces, overcoming the variability of manual methods. In poultry processing, Sohrabipour developed a cost-effective active laser scanning system that enables depth-aware deep-learning-based instance segmentation, automating labor-intensive tasks while improving precision. His work demonstrates a rare ability to bridge theoretical machine learning advances with practical, deployable agricultural technologies. By addressing both efficiency and hygiene in food production, Sohrabipour’s research is poised to reshape how robotics enhance safety and productivity across the agri-food sector.