Uncategorized News Weeds
UWinnipeg lands $250,000 Weston Seeding Food Innovation grant

A University of Winnipeg research project taking advantage of machine learning for weed identification receives a Weston Seeding Food Innovation grant worth $250,000.

The project aims to “transform the way we produce food, allowing farmers in Canada and beyond to care for large prairie crops as efficiently as a backyard garden.”

University of Winnipeg physics professor, Dr. Christopher Bidinosti is leading the project along with applied computer science professor, Dr. Christopher Henry. Their research team includes experts from the University of Winnipeg, Red River College, the University of Saskatchewan, Northstar Robotics, Sightline Innovation, the Canola Council of Canada, and Manitoba Pulse and Soybean Growers.

“Most gardeners, because their gardens are small, can pick every weed by hand or snip off every leaf that has rust on it, to give it that intimate care,” said Bidinosti. “If we can do that on the scale of the farm or the Canadian Prairies, imagine how much food you could grow?”

“There’s been a revolutionary change in computing hardware that has opened the door for many cool, real-world applications of machine learning,” said Henry. “Digital agriculture is the next big industry to benefit immensely from this technology.”

But in order to train a computer to recognize (and tend for) a prairie crop, it needs to access extensive examples of plants and weeds. This comes in the form of extremely large collections of pre-identified images of crop plants and weeds, from many different angles. Creating such a dataset by hand would take far too many people and an unrealistic amount of time.

“The main goal of our research project is to develop the means to automatically generate and label these images through a computer controlled camera system. We will then make the images publicly available for use by Canadian researchers and companies, because the fastest way to innovation is to get this data into the hands of more innovators,” said Bidinosti.

Before moving the technology outdoors, they will test their approach in a controlled environment at the University of Winnipeg and use the images they collect to develop software at the Dr. Ezzat A. Ibrahim GPU Education Lab. Post-doctoral fellow Dr. Michael Beck, along with three master’s degree students, will be working on the camera system and software.

Researchers in the departments of biology, chemistry, and geography will also contribute to the project, especially Drs. Rafael Otfinowski and Ed Cloutis, who offer expertise with greenhouse-based experiments and optical imaging, respectively. Collaborators outside University of Winnipeg bring further expertise in agriculture, botany, computer science, plant science, remote sensing, and robotics.

Former University of Winnipeg postdoctoral fellow in physics, Jonathan Ziprick, will be leading the effort at Red River College.

With the capacity of machine learning to crunch massive amounts of data, Bidinosti and Henry expect to create an unprecedented number of labelled images, planting the seed for new solutions in global food production.

“Machine learning is disrupting a lot of fields, but, in this case, it is only going to enhance the capabilities of farmers,” said Henry. “Machine learning will become just another instrument farmers add to their toolbox.”

The Weston Seeding Food Innovation Grant provides seed funding for interdisciplinary research or technology development to help accelerate solutions to sustainable food challenges, with a focus on food production, distribution and consumption initiatives that primarily impact Canadians, but also deliver key learning toward issues of global concern.