Deep Learning for Classifying Food Waste

TL;DR

This paper presents a deep learning approach to classify food waste from images captured by cameras on waste bins, aiming to reduce food waste through better classification and awareness.

Contribution

It introduces a tailored deep neural network for classifying food waste in images, demonstrating how deep learning can be adapted for waste management applications.

Findings

Classified food waste in over 500,000 images

Demonstrated the effectiveness of tailored deep learning models

Potential for reducing food waste through improved classification

Abstract

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.