Research on Automatic Waste Segregation

Abstract

With advancing urbanization, industrialization, and population rise, the global world is in the grip of a terrifying boom in municipal solid waste (MSW). Estimated global waste output is set to grow from 2.01 billion tonnes during 2016 to 3.40 billion tonnes by the year 2050, with an increase of nearly 70%, according to the World Bank. Of this, over 33% of waste is open dumped or uncontrolled landfilled, and this has a critical effect on air quality, soil fertility, and health. Landfills have grown now and are responsible for nearly 8–10% of the world\'s greenhouse gas emissions, and the situation is further worsened by the lack of segregation at the source. In addition to overloading city systems, it also exposes sanitation workers and stray animals to harmful substances. To counteract this growing issue, this paper proposes an Automatic Waste Segregation System to categorize domestic or industrial waste into metal, wet, and dry categories. The system is based on an Arduino Uno microcontroller, along with an IR sensor for dry waste, an inductive sensor for metals, and a moisture sensor for the detection of wet waste. A servo motor-controlled gate and rotor disc mechanism directs waste into respective bins based on sensor input. It is housed in a wooden box with PVC channels for ease of movement and protection. Through minimized human contact and promoting effective, source-level waste separation, the system enables better safety in waste handling, reduced landfill burden, and enhanced recycling, delivering a low-cost, scalable solution for eco-friendly waste management in residential and institutional settings.

Introduction

India is facing a critical waste management challenge due to rapid urbanization and population growth, generating over 62 million metric tonnes of municipal solid waste annually. A significant portion of this waste remains unsegregated, accumulating in poorly managed landfills and contributing to severe health hazards, environmental pollution, and unsustainable land use. The lack of proper waste segregation at the source—especially at the household level—worsens the burden on centralized waste management systems.

Manual segregation by ragpickers, while crucial to India’s informal recycling sector, is hazardous, inefficient, and low in material recovery. In contrast, automatic waste segregation using sensor-based systems offers a smarter, safer, and more scalable solution. The project proposes a low-cost, sensor-driven Automatic Waste Segregation and Monitoring System, using IR sensors, moisture sensors, and metal detectors integrated with Arduino Uno. It classifies waste in real-time into wet, dry, and metallic categories, displaying results on an OLED screen and guiding waste to the appropriate bin using servo motors.

This system supports key goals:

• Reducing human exposure to toxic waste.

• Enhancing recycling efficiency.

• Supporting environmental initiatives like Swachh Bharat Abhiyan.

• Promoting source-level segregation as a routine part of domestic waste handling.

Problem Statement

Improper domestic waste segregation leads to missed opportunities for resource recovery (e.g., compost, biogas, recycling). Industrial segregation is costly, inefficient, and often harmful to workers. Environmental damage from improper disposal includes water contamination, toxic gas emissions, and threats to public health. This project aims to segregate waste at the household level, reducing downstream burden and making waste a valuable resource.

Project Objectives

1. Automate the segregation of wet, dry, and metallic waste.

2. Create an environmentally sustainable solution to support recycling and resource recovery.

3. Minimize human involvement and improve accuracy.

4. Deliver a cost-effective, scalable, smart waste management system.

Literature Review Highlights

• Several smart bin systems have been developed using IoT, AI, and cloud-based solutions.

• Previous studies show bins capable of compacting waste, detecting fill levels, and classifying waste using neural networks.

• Many systems integrate with mobile or web apps to monitor waste status, send alerts, and optimize collection.

• Technologies used include ultrasonic sensors, IR sensors, metal detectors, Wi-Fi modules, and Arduino/NodeMCU platforms.

Methodology & Working

• Waste passes through sensors: IR (dry), metal detector, and moisture sensor (wet).

• Based on sensor input, the Arduino Uno activates servo motors to open the correct bin lid.

• Waste is guided via PVC pipes to three bins.

• An OLED display shows real-time identification like “Metal Waste Detected” or “Wet Waste Identified.”

• System simulations are done using Proteus software before physical deployment.

Results

• The system demonstrated over 95% accuracy in detecting and classifying waste.

• Sensor-to-action latency was less than 1 second.

• Successfully sorted waste into the three intended categories with real-time feedback via OLED display.

Discussion

This automated waste segregation system is effective for residential, educational, and small industrial applications. It can drastically improve recycling rates, reduce landfill dependency, and promote sustainable practices. With minimal cost and high efficiency, it has the potential to revolutionize waste handling culture in Indian cities.

Conclusion

The Automatic Waste Segregation System presents an economical solution which is both scalable and environmentally friendly for addressing present-day waste management issues. The system uses Arduino control and servo-actuated technology with inductive sensors and infrared and moisture sensors to automate the identification and segregation of dry and wet and metallic waste types. The system provides high accuracy and responsiveness through its OLED screen feedback and sensor-actuator communication system which cuts down human involvement and recyclable contamination risks. The system\'s modular design using PVC pipes and wooden frames enables its deployment at homes and businesses and community waste collection sites. The system demonstrates an advanced intelligent waste management technology which shows promise for supporting sustainable waste management and source-level segregation.

References

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Copyright

Copyright © 2025 Gouri Anand Ranade, Yash Mohan Uke, Kunika Rajiv Ronghe, Rutuja Santosh Bajare, Adarsh Sudhir Kale, Dr. Atul Sudhakar Joshi. 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.