According to the Environmental Protection Agency, more than 85% of greenhouse gas emissions from landfilled food waste can be attributed to missteps that occur before the food ever reaches a consumer’s plate—from production to processing to distribution. FloWaste, an Indiana-based startup, is addressing food system inefficiency at the processing stage using a proprietary machine learning system.
FloWaste announced today that it has raised a $1.1 pre-seed funding round, which it will use to scale up and improve its technology. Last week, The Spoon got on Zoom with company founder and CEO Rian Mc Donnell to find out how FloWaste can help food producers send less food to landfills.
Mc Donnell had the idea for FloWaste while studying mechanical and manufacturing engineering at Trinity College Dublin. “I gravitated toward the topic of food waste because by that point, I knew that whatever I did, my life was going to be sustainability-focused,” says Mc Donnell.
Here’s how FloWaste works: Customers identify 20 foods that they’d like the system to track, and the team trains their machine learning system to recognize those foods. Then the team installs cameras above customers’ workstations, production lines, and trash cans. The cameras monitor the food production process, automatically classifying food items and quantifying how much gets thrown away.
Video: FloWaste food identification and qualification. Source: FloWaste
“We gather a ton of data,” says Mc Donnell. “And we can chop and change that data based on ingredient usage, yield, shift performance, or daily performance. We present those insights to the management, and then they can make procedural changes.”
The technology can be used in both the industrial and the commercial food sectors. One of FloWaste’s current customers, a European protein producer, is using the system to monitor waste on a beef production line.
“There’s this huge financial return because proteins are expensive,” says Mc Donnell, “but also this huge environmental return because any increase in yield means that you’re effectively killing less cows in the long term.”
According to Mc Donnell, the task of training the machine learning system to recognize different foods has been time-consuming. But he hopes that as the system builds knowledge, it’ll become easier and easier to expand its use. “If we go in with someone and they’re doing fries, it means the next time we go and search for fries, we’ve already got a head start,” he says. “We’re slowly getting more and more robots to the point where eventually we’ll be able to just do a general use case of food as a whole.”
This pre-seed funding round will help FloWaste to build up scale with its technology: The company has signed agreements to launch the system at over 100 locations with its pilot customers in the next nine months. The funding will also help the team contend with the challenges of creating a hardware system from scratch using off-the-shelf cameras. In the near future, Mc Donnell is planning to bring on a full-time IoT engineer to make the system simpler and more reliable.
FloWaste is participating in the current cohort of Europe’s Rockstart accelerator. The company has also received funding from U.S. venture funds, including Underdog Labs and Flywheel Fund.
In the longer term, the team hopes to expand through new partnerships. “We’re working on installing in cafeteria kitchens and doing post-consumer analytics for customers who want that,” says Mc Donnell. “And we’re looking at quick-service restaurants because they have such an emphasis on optimizing their processes and their yield of food in the kitchen.”
Ultimately, the company is on a mission to help food producers discover how more environmentally friendly processes can also boost margins. “The best way to see a sustainability benefit is to tie it to the financials of business,” says Mc Donnell, “and actually teach businesses how they can be making more money by being more sustainable.”
Originally published by at ideacenter.nd.edu on November 09, 2021.