Dr. Mohsen Yoosefzadeh-Najafabadi is an Assistant Professor of Dry Bean Breeding and Computational Biology at the University of Guelph. His research integrates plant breeding, quantitative genetics, artificial intelligence, multi-omics, digital phenotyping, and climate-scenario modelling to accelerate the development of resilient, high-yielding dry bean cultivars for Ontario and beyond. He earned his PhD at the University of Guelph with a focus on soybean genetics and big-data breeding approaches and has since broadened his work across legumes and other crops. His program operates at the intersection of breeding and computation, developing next-generation tools, AI-based pipelines, and multi-omics frameworks to address complex agricultural challenges related to yield, stress resilience, nutritional quality, and canning performance.

“My program aims to develop dry bean cultivars that deliver meaningful value to growers, processors, and the broader agri-food system. By integrating high-throughput technologies, AI-driven analytics, and multi-omics insights, we try to create cultivars that are resilient, high-performing, and aligned with the future needs of agriculture.”

Areas of Research Interest

Dry Bean Breeding & Genetics

Designing breeding strategies for high-yield, stress-resilient, and quality-optimized dry bean cultivars across multiple market classes.

AI-Driven & Computational Plant Breeding

Integrating hybrid AI, predictive AI, generative AI, rule-based modelling, reasonable learning, and large-scale data fusion to enable next-generation breeding pipelines.

Omics-Based Selection & Multi-Omics Integration

Connecting genomics, phenomics, metabolomics, enviromics, and transcriptomics to accelerate selection accuracy and biological discovery.

Remote Sensing & Phenomics

Deploying UAV, field-based, satellite, and proximal imaging systems to model complex traits and build predictive phenomic tools.

Genome–Phenome Relationships Across Omics Layers

Studying genome–proteome–phenome–envirome to understand trait architecture, stress responses, and epigenetic mechanisms.

Computational Tools & AI Platforms

Creator of BeanGPT, AllInOne Pre-processing, and multi-omics R/Shiny platforms for plant breeding, phenotyping, and predictive agriculture.

Courses

Graduate

• PLNT*6520 – Applied Computational Biology in Agricultural Science

Winter Semester – A graduate-level course integrating AI, multi-omics, and computational tools for modern crop improvement.

Undergraduate

• MBG*2400 – Fundamentals of Plant & Animal Genetics

Fall Semester - This course introduces students to the foundational principles of heredity in plants and animals.