L2M Validate / Qc Winter 2026 / Grow Guard

The integration of machine learning models and the extraction of meaningful data into industrial workflows continues to accelerate, driving innovation and new business opportunities. Leveraging our expertise in mathematical and statistical modeling, we aim to apply these technologies to address critical challenges in agriculture, where up to 40% of crop yields are lost due to suboptimal resource management.
Our vision originated from observing a relatable problem: the loss of household plants, which costs owners both financially and emotionally. We recognized that a customized care program tailored to each plant’s specific conditions could improve survival rates beyond conventional methods. We propose to scale this approach to commercial agriculture. By modeling the unique conditions of individual farms, we will provide customized maintenance programsincluding precision irrigation and fertilizationaligned with each crop’s specific requirements.
Our solution will employ digital twin technology to simulate and monitor plant growth within a controlled virtual environment, enabling proactive management. This data-driven approach will transform reactive farming into predictive precision agriculture. By continuously analyzing environmental variables, soil conditions, and plant health indicators, our proposed system will provide actionable insights that optimize resource utilization while minimizing environmental impact and reducing operational costs.

Faculty Supervisor:

Hanan Anis

Student:

Partner:

V1 Studio

Discipline:

Engineering

Sector:

Education

University:

University of Ottawa

Program: