A Review of Smart AI Garbage Management System

Abstract

As cities grow, managing municipal solid waste (MSW) efficiently has become a critical challenge. Traditional systems are plagued with inefficiencies and lack real-time responsiveness. Recent technological advances, particularly in artificial intelligence (AI) and machine learning (ML), offer promising solutions. This paper reviews current literature on AI-based waste management systems, emphasizing image classification, real-time tracking, municipal integration, and citizen engagement. Our review includes an analysis of the Smart AI Garbage Management System developed in Satara, India, which incorporates real-time image uploads, automatic classification, and municipal dashboards with personalized user feedback. The findings highlight the transformative role of AI in sustainable waste management and outline the challenges and future opportunities for smart cities.

Introduction

As urban areas expand, traditional waste management systems face growing inefficiencies such as missed pickups, overflowing bins, and poor route optimization. Artificial Intelligence (AI) and Internet of Things (IoT) technologies are emerging as transformative solutions, enabling real-time monitoring, smart waste classification, optimized collection routes, and citizen engagement—leading to cleaner and more sustainable cities.

Methodology and Literature Review:

This review synthesized research from 2013–2024 using academic databases like Scopus, IEEE Xplore, and Google Scholar. It examined AI and IoT applications in areas such as:

• Waste classification (e.g., smart bins using CNNs, edge computing),

• Route optimization (IoT-based systems reducing cost and time),

• Citizen interfaces (mobile apps and real-time alerts),

• Automation (robots, drones, and smart sensors).

Key innovations include:

• ConvoWaste: DCNN-based waste segregation with 98% accuracy.

• WasteNet & Smart Bins: Real-time, low-power waste classification.

• Swarm Robotics & UAVs: Autonomous, large-area waste detection and collection.

• GA-FIS & IoT: Intelligent systems for bin monitoring and waste type segregation.

• Citizen-focused systems: Empower public involvement in waste tracking.

While results show high efficiency (up to 36.8% transport savings, 97–98% classification accuracy), challenges like limited scalability, hardware constraints, and data privacy persist.

Smart AI Garbage Management System (Case Study):

A practical system was developed with key features:

1. Citizen Reporting: Image uploads with GPS/time tags.

2. AI Classification: Segregates waste as dry/wet.

3. Municipal Dashboard: Task assignment and monitoring.

4. Before/After Verification: Ensures task completion.

5. User Feedback: Cleanup confirmation sent to users.

This system enhances transparency, accountability, and collaboration between citizens and municipalities.

Discussion:

Unlike many standalone AI systems, this integrated solution includes the entire waste management cycle—from citizen detection to municipal verification. However, it still faces obstacles:

• Scalability in large cities,

• Hardware variability (camera/GPS quality),

• Privacy concerns with location-tagged images,

• Infrastructure gaps in cloud platform adoption.

Future Opportunities:

1. Edge AI: Real-time analysis on local devices.

2. Federated Learning: Decentralized model training for privacy and efficiency.

3. Behavioral Analytics: Track user habits for targeted interventions.

Conclusion

AI-based urban waste management systems have the potential to revolutionize how cities handle garbage. By integrating ML models, real-time reporting, and citizen engagement, systems like the Smart AI Garbage Management System can provide efficient, transparent, and scalable solutions. However, to realize their full potential, future implementations must address infrastructure disparities, privacy concerns, and ensure community adoption.

References

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Copyright

Copyright © 2025 Rizwan Baraskar, Sufiyan Khan, Atharv Sabale, Sanket Bavdhane, Asst. Mrs. Deepa Rathod. 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.