McGill University | Canada
Dr. Hamed Etezadi is a research-focused scholar in Bioresource Engineering with a Ph.D. from McGill University, Canada, where his thesis focused on developing decision support infrastructure for sustainable crop production under the supervision of Prof. Viacheslav Adamchuk. He holds an M.S. in Biosystems and Agricultural Engineering from the University of Tehran, Iran, and a B.E. in Agricultural Engineering from Urmia University, Iran. With over 15 years of academic and professional experience, Dr. Etezadi has contributed significantly to precision agriculture, data-driven modeling, remote sensing, machine learning, and autonomous agricultural systems. He has published 6 peer-reviewed journal articles, including Q1 and Q2 journals, and presented at leading conferences such as ASABE and IEEE/CS Robotics and Mechatronics, His research explores soil variability, aerial pollination systems, and predictive modeling for sustainable agriculture. He has received multiple honors, including the FRQNT Scholarship, Grad Excellence Award, and global recognition on Kaggle Notebooks. Dr. Etezadi combines academic teaching, research leadership, and industry experience, including directing growth and innovation in agri-tech platforms, fostering data-driven decision-making, and advancing sustainable farming technologies globally.
Profile : Orcid
Featured Publications
Etezadi, H., Adamchuk, V., Bouroubi, Y., Leduc, M., Gasser, M.-O., & Titley-Peloquin, D. (2025). “Quantifying intra-field soil variability using categorical data: A case study of predicting soil organic matter using soil survey maps.”
Etezadi, H., & Eshkabilov, S. (2024). “A comprehensive overview of control algorithms, sensors, actuators, and communication tools of autonomous all-terrain vehicles in agriculture.”
Mazinani, M., Zarafshan, P., Dehghani, M., Vahdati, K., & Etezadi, H. (2023). “Design and analysis of an aerial pollination system for walnut trees.”
Zarafshan, P., Etezadi, H., Javadi, S., Roozbahani, A., Hashemy, S. M., & Zarafshan, P. (2023). “Comparison of machine learning models for predicting groundwater level, case study: Najafabad region.”
Salari, K., Zarafshan, P., Khashehchi, M., Chegini, G., Etezadi, H., Karami, H., Szulżyk-Cieplak, J., & Łagód, G. (2022). “Knowledge and technology used in capacitive deionization of water.”