Waste Segregation using CNN & IoT

Abstract

Due to rapid urbanization and ever-increasing population, India requires a sophisticated waste management system. Only one-fifth of the generated waste is treated and the rest is dumped in landfill sites. Segregation of household waste reduces a lot of complexity in waste treatment plants. Usually, segregation of household waste into dry and wet categories is done manually by the workers collecting the waste. This paper describes a waste management system to segregate waste into dry waste and wet waste through automation. The automation process saves a lot of time and effort and makes it easier for waste treatment plants. The system includes a camera module, which captures the image of the waste based on input from a sensor. The image, that is stored on Raspberry Pi, is then classified used the deep learning classification model. The proposed model is a miniaturized waste management system uses IoT and Deep Learning which can be implemented in the waste treatment plants or large sized community garbage containers on a large scale. The project consists of sensors, Raspberry Pi board, Deep Learning model, model training tool—Lobe, IoT dashboard, camera module, deep learning libraries which include lobe, TensorFlow Lite and open CV and servo motor. The CNN learning model uses ResNet and MobileNet algorithms for speed, accuracy and compatibility. The model, trained using a total of 12000 image samples, is efficient in segregating waste into dry waste and wet waste.