A Vision-Based AI Model for Efficient Waste Sorting and Recycling

Abstract

The increase in the rate of urbanization and industrialization has tremendously increased the generation of solid wastes that is creating serious environmental hazards and management problems. Segregation processes in developing countries are manually operated, which leads to poor performances of recycling due to failure and health risks to humans. This paper presents a vision-based intelligent model for waste sorting, utilizing artificial intelligence with image processing for the categorization of wastes automatically. This contains a camera module for capturing images of wastes that further feeds into a Convolutional Neural Network, which characterizes material as plastic, paper, metal, and organic wastes. Depending on its categorization, the wastes will be routed to respective containers with the help of a motor mechanism. It reduces human effort. Internet of Things also can be integrated into this design for real-time monitoring of bin levels and notifications to the waste collector for disposal on time, making the resources more efficient. This proposed design integrates three key reasons: simplicity, affordability, and accuracy for smart city applications and public collection systems. Experimental testing describes the real character of this system and proves that this technology is able to find its place in giving reliable results in waste management.

Introduction

Rapid urbanization and changing consumption patterns have significantly increased solid waste generation, making waste management a major environmental challenge. Traditional waste disposal methods are inefficient, hazardous, and lead to poor recycling outcomes. To address this, automated waste management systems using Artificial Intelligence (AI), computer vision, and the Internet of Things (IoT) are increasingly necessary.

The proposed system introduces a vision-based AI waste segregation model that uses a Convolutional Neural Network (CNN) to classify waste into categories such as plastic, paper, metal, and organic matter. A camera captures waste images, the CNN performs classification, and servo motors mechanically sort the waste into appropriate bins. IoT-enabled ultrasonic sensors continuously monitor bin fill levels and send real-time alerts for timely waste collection.

The literature review shows a progression from traditional machine learning methods (like SVM) to deep learning models (CNNs), along with IoT-based monitoring systems. While earlier approaches faced limitations such as low accuracy, high computational cost, or lack of intelligent classification, the proposed system integrates these technologies into a low-cost, real-time, and scalable solution.

Conclusion

It proposes a vision-based AI model for efficient waste sorting and recycling with the consideration of computer vision integrated with deep learning and IoT, which can segregate wastes automatically. In this proposed system, different types of wastes such as plastic, paper, metal, and organic materials are detected through a CNN trained on real-world datasets. The experimental evaluation yields an average accuracy of about 93%, proving the feasibility and reliability of this system for practical implementation. IoT-based monitoring further adds to the functionality by effectively availing real-time updates on the status of the bin to the waste management authorities for them to optimize the collection schedule and reduce incidences of overflow. This system allows contactless handling of the waste, improving hygiene and safety for sanitation workers. The prototype is economically feasible, compact, and scalable; it is appropriate for applications in homes, institutions, and smart city infrastructures. The integration of automation and intelligence within the prototype makes this solution one step toward attaining sustainable and intelligent handling of urban waste in contemporary cities.

References

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Copyright

Copyright © 2025 Mrs. Poonam Babasaheb Bhopale , Mr. Sarthak Amol Jadhav, Mr. Vivek Vikas Chougule, Mr. Rajvardhan Ramesh Chavan, Mr. Aditya Mahesh Chougule. 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.