Automatic Waste Segregator using Arduino Uno with Wet, Metal, and IR Sensors

Abstract

Waste management is an optimal problem in most cities because of the rapidly growing population and inadequate waste disposal techniques. Poor waste segregation causes a large percentage of wastes7 to go to landfills, along with pollution and even more significant problems regarding recycling. This research develops an automatic waste segregator with Arduino Uno microcontroller using wet, metal, and infrared (IR) sensors to differentiate wastes as wet, dry, and metal. The system can detect the properties of various types of waste materials, and segregates them into appropriate bins, which increases the efficiency of waste sorting and minimizes environmental problems. This proposed design intends to reduce manual effort, improve the recycling percentage, and aid in smart waste management. It is designed to be low cost, of a reasonable size, and can be used in residential, commercial, and industrial places. It is green in nature because it uses embedded systems and sensors for automated waste classification, which can further incorporate IoT and artificial intelligence for a greener approach. In this manner, the waste disposal process can become fully automated, minimizing health risks and promoting sustainable urbanization.

Introduction

I. Introduction

Manual waste segregation is inefficient, unsafe, and unhealthy. An Automatic Waste Segregation System (AWS) addresses these issues by automating the sorting process, increasing accuracy and safety. Waste is categorized into:

• Wet Waste (organic/biodegradable)

• Dry Waste (plastic, paper, glass)

• Metal Waste (cans, foil, aluminum)

Various methods like trommel separators, infrared sensors, and X-ray technology assist in mechanical separation. The AWS is the core of automated systems.

II. Literature Review

Several technologies and approaches enhance automated waste segregation:

• Sensor-Based Sorting: High accuracy achieved using wet, infrared (IR), and metal sensors (Sharma, 2023).

• IoT Integration: Enables remote monitoring, data storage, and analytics for smarter waste management (Patel, 2022).

• AI & Machine Learning: Boosts sorting accuracy by training models to recognize waste types (Gupta, 2021).

• Embedded Systems: Use of Arduino or Raspberry Pi for cost-effective, energy-efficient automation (Roy, 2020).

• Environmental Impact: Automation improves recycling rates and reduces landfill usage (Lee, 2019).

III. Novelty of the Work

Key innovations of the proposed system:

1. Real-time waste classification via integrated sensors.

2. Low-cost automation using Arduino Uno.

3. Efficient sorting to boost recycling and reduce landfill waste.

4. Compact, scalable design for homes and industries.

5. Optional real-time monitoring and data logging.

6. Energy-efficient and eco-friendly system.

IV. System Components

• Arduino Uno: Central controller processing sensor data and activating actuators.

• Wet Sensor: Measures moisture to detect organic waste.

• Metal Sensor: Detects metal via electromagnetic induction.

• IR Sensor: Identifies dry waste based on reflectivity.

• Servo Motor: Moves bins to sort waste accordingly.

• Conveyor Belt (optional): Automates waste feed to sensors.

V. Methodology

1. Waste Detection: Item placed on sensor-equipped platform.

2. Metal Detection: Metal sensor sorts into metal bin if detected.

3. Wet Waste Check: If no metal, wet sensor checks for moisture.

4. Dry Waste Classification: If not metal or wet, it is sorted as dry.

5. Servo Motor Sorting: Actuates bins based on sensor results.

6. Optional Data Logging: Sensor data stored for analysis.

VI. Working Prototype

The system includes a functional model implementing the above methodology. It demonstrates accurate, automated sorting using affordable and scalable technologies. Further enhancements include real-time data tracking and conveyor automation.

Conclusion

An elaborate examination has been successfully conducted on a novel affordable, effective and mechanized waste separation system employing Arduino Uno and multi-sensors. This system effectively diminishes the manual sorting effort, thus increasing the rate of recycling activities and protecting the environment consequently. Future upgrades consist of more accurate deep learning machine algorithms and the possibility of remote monitoring of waste collection via wireless communication. This suggested solution moves a bit forward to smart waste management by minimizing landfill obstruction and enhancing cleanliness in cities.

References

[1] Sharma, S., et al. (2023). “Automated Waste Segregation System Using Sensors.” Journal Of Environmental Engineering. [2] Patel, K. (2022). “Smart Waste Management Systems.” International Conference on IoT in Waste Management. [3] Gupta, M. (2021). “Optimization of Waste Sorting with AI and Sensors.” IEEE Transactions on Automation. [4] Roy, A. (2020). “Application of Embedded Systems in Waste Classification.” International Journal of Smart Cities. [5] Lee, T. (2019). “IoT Based Smart Waste Management Systems: A Review.” Springer

Copyright

Copyright © 2025 Er. Praveen Agrawal, Mansvi Yadav, Hemant Singh Rathore, Rahul Kumar Meena, Dilraj Meena. 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.