The swift rise in urbanization has resulted in growing difficulties with garbage management, calling for creative approaches to lessen its negative effects on the environment. The system accomplishes real-time garbage recognition and segregation into biodegradable and non-biodegradable categories on a Raspberry Pi 4. It is built using the OpenCV library and the MobileNetV3 algorithm. The system uses a mix of sensors, load cells, and cameras to measure garbage, weigh, and keep an eye on levels in different bins. The utilization of OpenCV improves computational performance and makes it appropriate for deployment on edge devices, while the inclusion of MobileNetV3 guarantees high accuracy of 96.32 % in waste classification. A deodorizing capability is integrated for biodegradable trash to reduce offensive odours, improving user experience, and promoting environmentally friendly waste management practices. The non-biodegradable garbage bin also has a fire detection sensor built in, which allows for early fire detection and notifies authorities of the situation. Thorough statistics illustrate the usefulness and effectiveness of the waste segregation system, including accuracy measurements, real-world testing results, and computational efficiency studies. Through its scalable and flexible approach to supporting sustainable urban environments, this research advances smart waste management systems.
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Vinnarasu et al. (2024) studied this question.
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