Precision Agriculture to Improve Crop Health

Using computer power to prevent pests and protect food crops.

Partner Co-investment - $4.2M

Supercluster Co-investment - $2.7M

Project Budget - $6.9M

Project Partners

Project Overview

The global population is rising, generating a need to produce more food to feed the world. Along with climate change, the food crops of Canada and the world are facing growing challenges from pests, pathogens and viruses that attack and destroy crops.

The threat is large. In Canada, we export more than $7 billion worth of wheat every year. As the climate warms, diseases such as wheat rust spread further north, overwinter more often, and create an increasing threat to production. At the same time, increasing the use of pesticides brings risks to the environment.

The Terramera-led Precision Agriculture to Improve Crop Health project is tackling this crop loss with a research consortium bringing together Sightline Innovation, Compression.ai, BC Cancer Research, Trent University, Simon Fraser University, the University of Saskatchewan, Agriculture and Agri-Food Canada, Genome British Columbia (Genome BC), and Canada’s Michael Smith Genome Sciences Centre.

Together, the team is working to develop new pest and pathogen controls through the use of computational biochemistry, genomics, machine learning, and robotics. The research will start with changing the way product development happens. Large amounts of data will be brought together in new ways. Machine learning and robotics will be used to quickly identify and test new pest management formulations and determine their ability to attack specific fungi on specific crops. It will show how crops and pests interact with different pesticides, pesticide enhancers, and alternatives.  

The Precision Agriculture to Improve Crop Health project will initially focus on leaf rust disease, which is caused by fungal infections and attacks some of Canada’s most important crops including wheat and barley. The computational platform being developed will help design antifungal formulations and predict their effectiveness, starting with wheat leaf rust and moving on to other crop diseases in Canada.

Constant improvement is built into the project. New formulations will be tested, and their analysis will further improve the machine learning and analysis to design and test the next formulations.

Worldwide, more than $85 billion is spent on crop protection. New pest-control products can tap into this market while protecting the world’s food supply and lessening impacts on the environment. The computing tools developed by the project will be applicable in other fields outside of agriculture, including medicine, biotechnology, chemistry, and computer science.

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