L2M- Potatoleaf Doctor( AI model for potato leaf detection)

Agriculture supports food security for 60% of the global population, with potatoes, the 4th most consumed crop, feeding around 1.5 billion people daily (Afakh et al. and LeCun et al.). Canada produces about 5.7 million tonnes of potatoes each year, making it the 12th-largest producer in the world. The country’s potato exports generate around $1.6 billion annually. However, Canadian potato producers lose a significant amount of yield each year due to preventable leaf diseases such as Late Blight and Early Blight. Most small and mid-scale farmers still rely on manual scouting, which is slow, subjective, and often too late to prevent damage. To solve this problem, we plan to design a lightweight AI framework (using computer vision technology) that identifies leaf diseases directly from smartphone/drone images, which also works offline, providing instant and affordable diagnostics. We believe that this model will work at the early stage of detection and contribute to potato growth.

Faculty Supervisor:

JingTao Yao

Student:

Partner:

North Forge

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Regina

Program: