Precision Agriculture

Precision Agriculture to Improve Crop Health

Using computer power to prevent pests and protect food crops.

Project Overview

Updated March 31, 2023.

The Problem

The global population is rising, generating a need to produce more food to feed the world.

The food crops of Canada and the world are facing growing challenges from climate change in addition to pests, pathogens and viruses that attack and destroy crops.

The threats are large. For example, Canada exports more than $7 billion worth of wheat every year. As the climate warms, diseases such as wheat rust spread further north and create an increasing threat to production. At the same time, increasing the use of pesticides brings risks to the environment.

How We Are Solving It

The Terramera-led Precision Agriculture to Improve Crop Health project is tackling this crop loss in partnership with Sightline Innovation, Metaspectral, BC Cancer Research, Trent University, Simon Fraser University, the University of Saskatchewan, Agriculture and Agri-Food Canada, 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. Large amounts of data are being brought together in new ways. Machine learning and robotics are being 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 project is initially focusing 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.

The Result

• Developed a computational biochemistry machine learning platform to inform agricultural product development.
• Created a computational algorithm that generates predictions for agricultural formulation development.
• Established an automated growth chamber for formulation testing and data collection.
• Identified wheat and fungal rust genomes and transcriptomes for rust infected wheat plants.
• Developed potential new commercial products for agricultural fungicides. Several leads for fungicidal formulations containing Actigate were identified during the course of this project. These are currently undergoing field trials and may lead to future licensing deals with agricultural companies for the reformulation of fungicides with Actigate.

Project Lead

  • terramera@2x e1632697291643

Project Partners

  • canada e1632697311226
  • Metaspectral e1632697328900
  • genome@2x e1632647383283
  • sightline e1632697365501
  • sfu@2x e1632697382895

Get involved now!

We’re not waiting for the future. We’re creating it.

Become a Member

Are you ready to solve society's toughest challenges?