Fuelled by a changing climate, plant pathogens encounter increasingly favourable conditions to spread and wreak havoc on global crop yields. But could artificial intelligence help predict the spread of disease, buying farmers valuable time to take preventative measures?
As part of an effort that took top honours and a $20,000 prize in the recent ProjectX global undergraduate research competition, a team of University of Toronto students proposed the use of a new machine learning architecture to forecast infections of black Sigatoka, a fungal disease that blackens bananas from the inside out.
ProjectX, the brainchild of the U of T Artificial Intelligence student group, challenged teams of undergraduate students from universities around the world to use machine learning to address the impacts of climate change. The competition, which concluded in December, was divided into three categories: infectious disease; weather and natural disaster prediction; and emissions and energy efficiency.
Left to righ (top row): Ziyad Edher, Yuchen Wang, Sornnujah Kathirgamanathan, (bottom row) Matthieu Chan Chee, Minh Duc Hoang and Shion Fujimori.
ProjectX organizers maintained a strict firewall between themselves and U of T student competitors to ensure fairness in the competition.
The team from U of T that emerged victorious in the infectious disease category included computer science students Yuchen Wang, Matthieu Chan Chee, Ziyad Edher, Minh Duc Hoang and Shion Fujimori; as well as Sornnujah Kathirgamanathan, a molecular genetics and microbiology student in the Temerty Faculty of Medicine.
Operating remotely from Toronto, Vancouver, Japan, and Vietnam, the team members focused on devising a neural network to forecast the infection risk of black Sigatoka.The fungal disease can have devastating consequences for farmers, decreasing yields and driving up costs. The Food and Agriculture Organization of the United Nations reported that, between 2007 and 2009, St. Vincent and the Grenadines faced a 90 per cent decline in banana crop production due to the disease.
For more information: utoronto.ca