Learning with Smart Waste Management

Abstract

Thispaper,introducesaninnovativesystem thatleveragesadvancedimagerecognition technology to classify waste into biodegradable and non-biodegradable categories. The smart dustbin, integrated with a camera module and ESP-32 microcontroller, uses pre-trained machinelearning models to automatewastesegregation processes. Designedasbothan educational tool and asustainable environmental solution, thesystemtargetsyounglearnersand communitymembers, raisingawarenessaboutthe importance of waste management. By automating the process, it minimizes human error, reduces environmental pollution,and supportscleanerandgreener practicesacrossvarious applications such as schools households, and public spaces.

Introduction

Waste management is a major environmental challenge due to increasing waste generation from urbanization and industrialization. Improper disposal and manual segregation lead to pollution, inefficient recycling, and health hazards. Traditional methods are labor-intensive, error-prone, and mix biodegradable with non-biodegradable waste, reducing recycling effectiveness.

This research proposes a Smart Waste Management System using IoT, AI, and machine learning to automate waste segregation. The system employs a camera, ESP-32 microcontroller, and AI-based image recognition to classify waste as biodegradable or non-biodegradable in real time. A servo motor then directs the waste into the correct bin, improving accuracy, reducing human labor, and minimizing contamination.

The system achieved over 90% classification accuracy with a response time under two seconds. Challenges included low-light image quality and difficulty distinguishing composite waste materials. The smart bin was also tested in an educational setting, effectively raising awareness of proper waste disposal among children.

Overall, this IoT- and AI-driven approach enhances waste segregation efficiency, promotes sustainability, and offers potential for scalable environmental impact through automation and education.

Conclusion

The SmartWaste Management System representsa significant steptowardautomatingwaste segregation through the integration of IoT andAI-driven imagerecognition. Byleveraging a camera module,ESP-32 microcontroller,cloud-based Edge Impulse processing, andservomotors,thesystem effectivelyclassifies waste into biodegradable and non-biodegradable categories, reducing human intervention and improving the efficiency of waste disposal. The implementation of this smart bin has the potential to minimize improper waste disposal, streamlinetherecycling process, and contribute to a cleaner environment.With an accuracy rate of approximately 90%, the system has demonstrated its reliability, though certain challenges such as misclassification in complex waste materials and performance limitations under poor lighting conditions highlight areas for future enhancement. Beyondautomation,thesystemservesasaneducationaltool,fosteringawarenessaboutsustainablewaste management practices. Its interactive nature encourages users, particularly students, to engage in responsible disposal habits. Looking ahead, the project can be further developed by expanding its waste classification capabilities, integrating mobile applications for real-time monitoring, and optimizing its power consumption using renewable energy sources. This system provides a foundation for smart city waste management initiatives, offering a scalable and sustainable approach to addressing the growing challenge of waste segregation and environmental conservation.

References

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Copyright

Copyright © 2025 Shikhar Gupta, Sankalp , Dr. Balwant Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.