Seeding Food Innovation - Awarded Project 2018

Enabling the next revolution in global food production through automatically labelled data sets and machine learning

Project Description

We envision a future where it will be possible to lavish the same attention on individual plants in a large prairie crop farm as one might on those in a backyard garden. As camera sensors shrink in size, and self-driving vehicles continue to improve, such an idea is no longer the realm of science fiction. The remaining piece of the puzzle, however, is the need for a very large number of pre-identified images of crop plants and weeds with which to train a computer to recognize one from the other. Our research project is to develop the means to automatically generate and label such images and to make the resulting data sets openly available for use by Canadian researchers and companies. This approach puts the missing puzzle piece into the hands of more innovators more quickly, leading to new solutions for global food production sooner.

Relevance to the field of food innovation

By tackling the fundamental crux of this problem, our work will help enable the many new innovations in food production needed for a growing population in a world with diminishing resources. For example, autonomous vehicles fitted with imaging sensors and plant recognition software could one day operate around the clock, inspecting hundreds of hectares of farmland plant-by-plant, monitoring crop health, identifying weeds, pests and disease, and taking action to improve growing conditions and eliminate threats. This would greatly increase crop yields and help reduce herbicide and pesticide use.

Anticipated outcome

Our anticipated outcome is five-fold: (1) the development of methods to automatically generate and label images of crop plants and weeds; (2) the development of plant recognition software using these image datasets; (3) the open access sharing of our methods, datasets, and software; (4) the exploration of other sensor technologies; and (5) the testing of our software in the field using autonomous vehicles.

Grantees:

 

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