An Efficient Deep Learning Based Waste Management System for Sustainable Environment

Efficient Management of solid and liquid waste is crucial for maintaining environmental sustainability and public health. This work presents a novel approach for the detection and segregation of solid and liquid waste using deep learning techniques. By leveraging Convolutional Neural Networks (CNNs), a system is designed which is capable of automatically classifying waste into solid or liquid categories based on real-time image data from waste disposal units. The system also integrates Internet of Things (IoT) devices to transmit the classified waste data to a remote central collection centre for optimized disposal processes. The model was trained on a diverse dataset of waste images and achieved high accuracy in distinguishing between solid and liquid waste. Once classified, the waste information is communicated via a secure wireless network to the central collection centre, which can coordinate timely collection and disposal efforts. This approach not only automates the waste management process but also reduces manual errors and delays in waste segregation and disposal. This work also discusses the potential for scalability and implementation in smart city infrastructures. Compared to the state of art method existing in the literature, this proposed method achieves an accuracy of 98.31 %, Precision of 97.93%, recall of 98.17% with a F1- score of 0.98. This system demonstrates the effectiveness of deep learning in waste classification and proposes a scalable solution for enhancing waste management practices, ultimately contributing to a more sustainable environment.