A smart compost bin that tracks household food waste with 3D images and sensor measurements could improve understanding of why so much food goes uneaten.
Researchers from Oregon State University-Cascades have received $640,000 (£513,000) in funding to develop the bin as part of efforts to cut the estimated one-third of food in the US that goes to waste.
“At every other step of the agricultural supply chain, food waste is tracked, measured and quantified,” said project leader Patrick Donnelly. “However, approaches to measuring post-consumer food waste are costly, time-intensive, prone to human error and infeasible at a large scale.
“We’re adapting our design to accommodate consumers’ current behaviour, using compost bins commonly distributed by waste utilities as a template.
“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 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.”
In an average year, the US wastes more than $400bn-worth of food despite the fact that an estimated 10 per cent of households are food-insecure. Aside from economic inefficiency, that waste also generates significant amounts of greenhouse gases carbon dioxide and methane.
“There’s a familiar adage: You can’t manage what you don’t measure,” Donnelly said. “Our goal is to inspire future waste reduction by specifically quantifying, measuring and tracking the amount of food that home consumers send to compost.”
The researchers are planning to run a small pilot study in spring 2024 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.
“Our solution would fully automate this process, enabling researchers to collect this data more accurately and efficiently,” Donnelly 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.”
In 2019, a bin powered by AI was launched to help chefs understand what food they are wasting most often.
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