Ecobin: Smart Garbage Segregator
Nilesh Kundan Mahajan Electronics and Telecommunication MVP’s KBTCOE
Nashik,India
nileshmahajan560@gmail.com
Puja Shivaji Suryawanshi Electronics and Telecommunication MVP’s KBTCOE
Nashik,India
suryawanshipuja9@gmail.com
Shrushti Nitin Tejale Electronics and Telecommunication MVP’s KBTCOE
Nashik,India
1002sntejale@gmail.com
Abstract - With the rapid increase in urbanization and waste production, effective waste management has become a crucial environmental challenge. Traditional waste segregation methods are inefficient, leading to contamination of recyclables and excessive landfill use. The lack of proper waste classification not only affects recycling rates but also contributes to environmental pollution and public health concerns.
This paper presents EcoBin, an automated smart waste segregation system that efficiently classifies waste into dry, wet, mixed, and metallic categories using ultrasonic and moisture sensors. The system incorporates an ESP32 microcontroller for real-time processing, servo and stepper motors for automated sorting, and a mobile application for user notifications. By integrating automation and IoT technologies, EcoBin reduces human effort, enhances recycling efficiency, and promotes sustainable waste disposal.
EcoBin operates through a contactless mechanism, ensuring hygiene by minimizing direct human interaction with waste. The integration of an LCD display and LED indicators provides real-time feedback on waste classification and system status. The bin is also equipped with an alert system that notifies users when it reaches full capacity, enabling timely waste collection and preventing overflow. Additionally, an in-depth analysis of power consumption, waste segregation
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accuracy, and system performance is conducted to evaluate EcoBin’s effectiveness.
The results of this study indicate a significant improvement in waste segregation efficiency, making EcoBin a promising solution for smart cities, residential complexes, and commercial spaces. Future enhancements include AI-based waste recognition for more precise classification, solar-powered operation for energy efficiency, and integration with municipal waste management systems to further optimize waste collection and disposal processes.
Key Words: Smart Waste Management, IoT, Waste Segregation, Recycling, Automation, Smart Cities, AI- based Waste Recognition, Sustainability
