Patrick Donnelly, assistant professor of computer science in the Oregon State University-Cascades College of Engineering is leading the project, which has received $640,000 in funding.
“At every other step of the agricultural supply chain, food waste is tracked, measured and quantified,” Donnelly said in a statement. “However, approaches to measuring post-consumer food waste are costly, time-intensive, prone to human error and infeasible at a large scale.”
Donnelly and OSU colleagues Jason Clark of the College of Engineering and Quincy Clark of the colleges of Agricultural Sciences and Education are aiming to create a kitchen compost container that automatically measures household food waste.
“We’re adapting our design to accommodate consumers’ current behaviour, using compost bins commonly distributed by waste utilities as a template,” said Donnelly. “When a user disposes of edible and non-edible food waste in the bin, our device prompts the user to describe the deposited items. The user’s note is then transcribed with automatic speech recognition and associated with a weight measurement of the items.”
The device will also collect 3D images and sensor measurements of the food waste, resulting in an “entirely novel dataset to enable and encourage future researchers to tackle the problem of food waste measurement with computer vision.”
This research is supported by the Foundation for Food & Agriculture Research and the Kroger Co. Zero Hunger | Zero Waste Foundation. Roughly 37 per cent of US food waste occurs in homes, according to the FFAR.
In an average year, the United States wastes over $400bn worth of food. Aside from economic inefficiency, Donnelly said waste generates significant amounts of carbon dioxide and methane.
“There’s a familiar adage: You can’t manage what you don’t measure,” said Donnelly. “Our goal is to inspire future waste reduction by specifically quantifying, measuring and tracking the amount of food that home consumers send to compost.”
As part of the project, researchers will run a small pilot study, which is likely to start in spring 2024.
“After we design the device, we will distribute them to study participants,” said Donnelly. “The purpose of the preliminary study is to test the technology and collect measurements and images for a dataset.”
At present this type of food waste research is performed by tasking small groups of participants to manually weigh their food waste and record the measurements in a journal, Donnelly said.
“Our solution would fully automate this process, enabling researchers to collect this data more accurately and efficiently,” he said. “Our work is a first step towards developing a fully autonomous, computer vision solution that would enable households to track the amounts and types of food compost waste they generate. With these personalised and data-driven interventions, we hope to inspire consumers to reflect upon and change their behaviours with respect to food waste over time.”