Abstract: Effective waste management is crucial for environmental sustainability and public health. This research presents the development of a Smart Waste Segregation System using image processing and deep learning to automate the classification and sorting of waste. The system classifies waste into five categories: paper, glass, metal, plastic, and organic waste. It integrates both hardware and software components to enhance the accuracy and efficiency of waste segregation. The hardware consists of an ESP32-CAM module to capture waste images and an ESP32 development board to control the mechanical sorting system. Captured images are processed on a laptop using the MobileNetV2 deep learning model for real-time classification. Upon identification, the waste is sorted into the appropriate bin using a conveyor belt and servo motors. To ensure optimal performance, the system was tested using five deep learning models: MobileNetV2, VGG16, ResNet50, InceptionV3, and Xception. Experimental analysis revealed that MobileNetV2 offers the best balance of accuracy and computational efficiency, making it ideal for real-time waste classification. Key features of the system include automated image-based waste identification, real-time sorting, and LED indicators to display the detected waste category. This automated approach reduces human intervention, improves sorting accuracy, and increases operational efficiency. The proposed system is scalable, cost-effective, and suitable for applications in smart cities and industrial waste management, offering a sustainable and efficient solution for modern waste handling challenges.
keywords: Smart Waste Segregation, Image Processing, Deep Learning, Automated Waste Classification, MobileNetV2, VGG16, ResNet50, InceptionV3, Xception, ESP32-CAM, Waste Management, Real-Time Sorting, Environmental Sustainability, Mechanical Automation, Smart Cities.
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DOI:
10.17148/IJARCCE.2025.14374
