Projects

The project introduces a groundbreaking solution to combat Fusarium dry rot, a major issue for potato producers in Alberta, causing millions in annual losses. The solution combines two advanced technologies: eDNA monitoring to track Fusarium spore populations and AI-powered wounding detection to identify mechanical damage during harvest. With industry partnerships, the project aims to develop species-specific Fusarium detection kits, integrate real-time monitoring, and reduce storage waste. By providing predictive insights, the project seeks to improve storage yield retention and offer proactive disease management tools to producers.

Saiwa Inc., in collaboration with Mohawk College, built the Follomotion back-end services—a suite enabling real-time motion capture, camera calibration, and pose estimation. Delivered within one month and supported for six months, the project provided scalable, API-first infrastructure that enhanced precision, adaptability, and reliability. The solution empowers industries such as sports, fitness, healthcare, and rehabilitation with cutting-edge motion tracking technology.

Hemaa.ai, in partnership with Tarmeez, delivers an AI-powered solution for date palm plantations that enables early health monitoring, precise tree counting, and targeted weed detection using drone and satellite imagery. Leveraging advanced deep learning models, this platform enhances operational efficiency, reduces costs, and promotes sustainable agriculture through timely alerts and accurate spatial data mapping.

In partnership with Vineland Research and Innovation Centre and with funding support from the Ontario Centre of Innovation (OCI), Saiwa is developing an advanced computer vision and AI-powered platform for early stress detection in greenhouse-grown sweet peppers. This project replaces an earlier cucumber-focused initiative and targets sweet pepper crops—another economically significant greenhouse product in Ontario. By leveraging drone and robotic imagery acquisition, alongside real-time deep learning models, this initiative aims to reduce losses from disease, optimize intervention timing, and improve crop yield and quality.