Article
Smart Waste Classification Using Deep Learning
Waste management has become a significant global concern, as improper disposal methods pose serious risks to both the environment and public health. Conventional waste sorting techniques are often labor-intensive and inefficient, highlighting the need for smarter and more effective solutions. This study presents an AI-based garbage classification system integrated with IoT, combining realtime IoT-enabled sorting with deep learning-driven image classification. The system utilizes a convolutional neural network (CNN) trained on diverse categories of waste to achieve high classification accuracy. Once the waste is identified, an IoT-enabled actuator mechanism automatically segregates it, reducing human effort and improving operational efficiency. To ensure real-time performance and seamless integration with smart city systems, the proposed solution is deployed on edge computing devices. Experimental results demonstrate high accuracy in classification along with efficient waste segregation, indicating the practicality of the system for large-scale implementation. This study highlights the transformative potential of combining AI and IoT in modern waste management, contributing to a more sustainable and circular economy.
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