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Paper ID:IJSREM64263

EcoTrack AI: Smart Environmental Management System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64263

Volume:

10 |

5 |

May 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64263

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Vinod Kangude

Vinod Kangude

Department of Information Technology

NBN Sinhgad School of Engineering, Pune - 411041

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Aniket Pawar

Aniket Pawar

Department of Information Technology

NBN Sinhgad School of Engineering, Pune - 411041

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Aditya Lagad

Aditya Lagad

Department of Information Technology

NBN Sinhgad School of Engineering, Pune - 411041

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Mentors

PRS

Prof. R.M. Samant

Department of Information Technology

NBN Sinhgad School of Engineering, Pune - 411041

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Abstract

In the modern era of rapid urbanization and industrial expansion, the deterioration of environmental quality has become a global challenge. Managing pollution, monitoring waste, and fostering eco-consciousness among citizens require a unified, intelligent, and accessible platform. EcoTrack AI addresses these challenges by integrating artificial intelligence, cloud computing, and citizen engagement into a single smart environmental management system. Developed using Flutter for cross-platform mobile accessibility and Firebase for real-time cloud data handling, the system empowers citizens to report environmental issues, track local air quality, calculate their carbon footprint, and engage in sustainable practices. The platform incorporates AI-based pollution detection using image analysis, intelligent waste disposal suggestions, and a carbon footprint calculator based on lifestyle patterns such as energy consumption, transportation, and food habits. A gamified ecosystem motivates users to take environmentally friendly actions through rewards and leaderboards. For authorities, an admin web dashboard provides analytics, complaint management, and worker assignment tools, ensuring transparency and accountability in municipal operations. The inclusion of real-time heatmaps, multilingual support, and eco-marketplace integration enhances user inclusivity and environmental awareness. EcoTrack AI not only fosters collaboration between citizens, workers, and government entities but also provides data-driven insights for sustainable decision-making. By combining AI, cloud, and community participation, the project aims to build a scalable model for future smart cities, driving behavioral change toward environmental preservation and collective responsibility.

KEYWORDS

EcoTrack AI Environmental Management Flutter Firebase TensorFlow Lite Carbon Footprint

Cite This Article

Vinod Kangude, Aniket Pawar, Aditya Lagad, Prof. R.M. Samant, "EcoTrack AI: Smart Environmental Management System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-8, May 2026. https://doi.org/10.55041/IJSREM64263

Full Article/Conclusion

EcoTrack AI proves to be a robust, scalable, and user-centric system that not only addresses current environmental challenges but also lays the foundation for future smart city solutions. With its ability to integrate advanced technologies like AI, IoT (in future scope), and predictive analytics, the platform has the potential to evolve into a comprehensive decision-support ecosystem. It contributes significantly to sustainable urban development by enhancing environmental monitoring, promoting community participation, and encouraging long-term behavioral change toward a greener and more sustainable future. EcoTrack AI creates a strong foundation for long-term environmental sustainability by transforming passive citizens into active contributors in urban governance. The system's ability to combine technology with community engagement ensures that environmental protection becomes a shared responsibility rather than a government-only task.

References

[1]  M. R. Hossain, S. A. Rahman, and M. S. Hossain, "Smart Waste Management System for Sustainable Environment Using IoT and Machine Learning," IEEE Access, vol. 9, pp. 114769-114783, 2021, doi: 10.1109/ACCESS.2021.3105073. [2]  A. S. Jadhav and R. R. Deshmukh, "IoT-Based Garbage Monitoring and Management System for Smart Cities," in Proc. IEEE Int. Conf. on Communication and Electronics Systems (ICCES), Coimbatore, India, 2022, pp. 145-150, doi: 10.1109/ICCES54183.2022.9784562. [3]  P. K. Gupta and S. S. Rana, "AI-Based Image Recognition System for Waste Classification and Recycling," IEEE Trans. Artificial Intelligence, vol. 4, no. 2, pp. 190-202, 2023, doi: 10.1109/TAI.2023.3256901. [4]  N. K. Sahu, R. Das, and S. Mishra, "Design and Implementation of a Smart Environmental Monitoring System Using Cloud and IoT," IEEE Sensors Journal, vol. 22, no. 18, pp. 17892-17903, Sept. 2022, doi: 10.1109/JSEN.2022.3187096. [5]  R. Kumar and V. Singh, "Integrating Artificial Intelligence with IoT for Smart City Waste and Pollution Management," IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11723-11736, July 2021, doi: 10.1109/JIOT.2021.3057621. [6]  M. Bhattacharya, P. Hossain, and S. S. Das, "Sustainable Smart City Framework Using AI, IoT, and Cloud: A Review," IEEE Access, vol. 10, pp. 65023-65037, 2022, doi: 10.1109/ACCESS.2022.3184569. [7]  S. R. Patil and K. R. Wagh, "Development of Real-Time AQI Monitoring and Alerting System Using Firebase and Mobile Applications," in Proc. IEEE Int. Conf. on Computational Intelligence and Data Science (ICCIDS), 2023, pp. 310-316, doi: 10.1109/ICCIDS.2023.10047829. [8]  V. Sharma, P. Jain, and R. Garg, "Eco-Centric Urban Data Platform Using AI and Cloud Computing for Pollution Analysis," IEEE Smart Cities Journal, vol. 7, no. 1, pp. 45-58, Jan. 2024, doi: 10.1109/JSC.2024.1009821. [9]  K. J. Thomas, "Gamification and User Engagement in Environmental Applications: A Case Study Approach," IEEE Trans. Human-Machine Systems, vol. 53, no. 3, pp. 480-492, 2023, doi: 10.1109/THMS.2023.3245642. [10] S. S. Mehta and A. K. Verma, "Integration of AI-Powered Chatbots for Citizen-Centric Smart Governance," IEEE Access, vol. 11, pp. 12721-12734, 2023, doi: 10.1109/ACCESS.2023.3249827.

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Publication Date

5/29/2026

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