Garbage Content Estimation Using Internet of Things and Machine Learning

Abstract

Much garbage is produced daily in homes due to living activities, including cooking and eating. The garbage must be adequately managed for human well-being and environmental protection. Although the existing IoT-based smart garbage systems have gained high garbage classification accuracy, they still have a problem that they provide a small number of garbage categories, not enough for reasonable practices of household garbage separation. This study presents a new smart garbage bin system, SGBS, embedded with multiple sensors to solve the problem. We deployed temperature, humidity, and gas sensors to know the condition and identify the garbage content disposed of. Then, we introduce a new garbage content estimation method by training a machine learning model using daily collected fuse sensor readings combined with detailed household garbage contents annotations to perform garbage classification tasks. For evaluation, we deployed the designed SGBS in five households over one month. As a result, we confirmed that the leave-one-house cross-validation results showed an accuracy of 91% in 5 kitchen waste contents, also, 89% in 5 paper/softbox contents, and 85% in the 8 garbage categories for the classification tasks.