Deep Learning for Classifying Food Waste
This work addresses food waste reduction for consumers and waste management systems, but it is incremental as it applies existing deep learning methods to a new dataset.
The researchers tackled the problem of classifying food waste in images to reduce waste by using a deep neural network on half a million images from waste bins, achieving results that demonstrate effective learning from training data.
One third of food produced in the world for human consumption -- approximately 1.3 billion tons -- is lost or wasted every year. By classifying food waste of individual consumers and raising awareness of the measures, avoidable food waste can be significantly reduced. In this research, we use deep learning to classify food waste in half a million images captured by cameras installed on top of food waste bins. We specifically designed a deep neural network that classifies food waste for every time food waste is thrown in the waste bins. Our method presents how deep learning networks can be tailored to best learn from available training data.