Mohammad Nadimi

Biography

Dr. Mohammad Nadimi is an Assistant Professor in the Department of Biosystems Engineering at the University of Manitoba. His interdisciplinary research integrates photonics, imaging and spectroscopy, smart electronic sensing, artificial intelligence, data analytics and digital agriculture to develop practical technologies for agricultural and postharvest systems.

He received his PhD in Electrical Engineering from the University of Manitoba in 2018, where his research focused on laser systems for biological applications. He subsequently conducted postdoctoral research in Biosystems Engineering, applying advanced optical, imaging and sensing technologies to agricultural and agri-food applications. Before joining the University of Manitoba faculty in 2025, he also gained industry experience in technical and analytical roles, including Senior Data Analyst and Machine Learning Engineer, working with large-scale datasets, predictive modelling and machine-learning applications.

His current research program focuses broadly on intelligent sensing and digital technologies for agriculture, including smart grain storage and postharvest monitoring, non-destructive quality assessment, AI-enabled sensing systems and laser-based approaches for improving seed performance. His group combines advanced sensing technologies with machine learning and data-driven modelling to develop practical tools for monitoring, prediction and decision-making in agricultural systems.

Dr. Nadimi has secured more than $600,000 in research funding, including more than $460,000 in externally secured funding, through competitive grants, collaborative programs and partnerships. He has authored more than 50 peer-reviewed journal articles and more than 30 conference papers. He was recognized in the Stanford–Elsevier World’s Top 2% Scientists List in 2025 and is a recipient of the 2026 Canadian Society for Agricultural and Biosystems Engineering (CSABE/SCGAB) John Ogilvie Research Innovation Award.

Beyond his research program, Dr. Nadimi founded and chaired the first Manitoba Digital Agriculture Symposium, which brought together more than 160 participants from academia, industry, government and the agricultural community. He is also actively involved in scholarly publishing, serving on the editorial boards of journals including Measurement: Food (2026 IF=6.3) and as a Guest Editor for the prestigious journal of Computers and Electronics in Agriculture (2026 IF=10.1). His broader goal is to advance interdisciplinary engineering and digital technologies that contribute to more efficient, resilient, and sustainable agricultural systems.

Research

Area

  • Application of electromagnetic imaging and spectroscopy techniques for real-time quality monitoring of agri-food products.
  • Development and application of advanced data analytics techniques, including machine learning and artificial intelligence, to optimize computational time and power in big data analysis within the agri-food domain.
  • Microstructural analysis of raw and processed agri-foods to enhance understanding of food quality and safety.
  • Applications of physical treatments, such as laser biostimulation, to improve the viability of crops.
  • Applications of smart electronic sensing technologies for real-time crop quality monitoring.

Expertise

  • Grain quality monitoring
  • Photonics and spectroscopy
  • Advanced imaging techniques
  • Microstructural analysis
  • Electronics and sensor technologies
  • Advanced statistical data analysis
  • Machine learning and deep learning

Research description

To develop industry-aligned, photonics-based technologies integrated with advanced
data analytics that enhance agri-food production and storage efficiency, elevate food
quality, reduce loss and minimize environmental impacts.

Graduate Student Opportunities

Dr. Nadimi is currently seeking motivated M.Sc. and PhD students interested in research related to smart grain storage systems, including intelligent sensing, digital agriculture, imaging and spectroscopy, artificial intelligence, machine learning and data-driven approaches for monitoring grain quality during handling and storage.

Prospective students should include the following information in bullet form in the body of their application email:

  • Degree sought: M.Sc. or PhD
  • Current affiliation: university, institution or employer, including the country
  • Current or most recent degree and field of study
  • Overall GPA: clearly indicate both the GPA and the grading scale (e.g., 4.4/4.5, 19.2/20)
  • Peer-reviewed journal publications (if any): number
  • Expected availability/start date

Applicants should also attach their CV and academic transcripts and briefly describe their research interests, relevant technical experience and how their background aligns with research in smart grain storage and quality monitoring.

Only applicants whose academic background, research experience and interests closely align with current opportunities may be contacted.

Selected Publications

Google Scholar