Deep Learning in Waste Management and Recycling in Digital Smart City
Sushil Kumar · 2025
For waste management and recycling in smart cities, the fast growth of urban populations and the subsequent rise in garbage creation have posed considerable issues. For cities to be sustainable and ecologically friendly, good waste management and the promotion of recycling practises are crucial. Deep learning techniques have become a potent tool for solving complicated issues and streamlining numerous procedures in a variety of fields in recent years. In the framework of smart cities, this chapter proposes improved Deep learning model with IOT Architecture for recycling and garbage management. The first section gives an introduction of the trash management process, Role of Technology in Waste Management and Recycling, difficulties associated with trash collection, segregation, and disposal in smart cities. It emphasises the demand for cutting-edge solutions to simplify these procedures and boost overall effectiveness. By utilising massive datasets and sophisticated neural networks, has demonstrated promising outcomes in solving challenging issues. It is the perfect contender for waste management applications due to its capacity to extract significant patterns and generate precise forecasts. The next section of the chapter discusses the various model proposed for particular use cases for deep learning in trash management. Sorting and segregating garbage is one example of such an application. To reliably identify various sorts of garbage, deep learning systems can analyse photos or sensor data. Deep learning may greatly increase recycling efficiency by automating the sorting process while lowering human error. The further section discusses the deep Learning model and looks in depth at how deep learning can be used to improve waste classification. Traditional waste collection techniques frequently adhere to set schedules, which results in inefficiencies and higher carbon emissions. To dynamically optimise waste collection routes, deep learning algorithms can learn from past data, including waste classification and management. The chapter also covers deep learning's potential for use in analysing waste composition. Data on the composition of waste must be accurate in order to develop recycling policies and programmes that work. Deep learning algorithms can examine several aspects of garbage, such its material makeup, to offer insightful data. This knowledge can aid in decision-making and facilitate the creation of focused recycling programmes. The next section of the chapter discusses the experiment conducted for waste classification and Result Analysis for the proposed model. Deep learning has the potential to transform recycling and waste management procedures in smart cities. Deep learning can increase productivity, lessen its negative effects on the environment, and aid in the creation of sustainable and intelligent cities by utilising its talents in garbage sorting, collection route optimisation, and waste composition analysis. Hence to address the current issues and make sure that deep learning techniques are successfully incorporated into waste management and recycling systems, more research and collaborations needed will also be discussed in the conclusion section.