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@article{172802,
        author = {Bhuvan Girhepunje and Ajay Mehta and Niketan Murodiye and Vaibhav Shikhare and Prof. Harshad Kubade},
        title = {Computer Vision Based Garbage Classification System: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {916-920},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172802},
        abstract = {The increase in global waste requires effective and efficient management. Manual waste identification is labor - intensive, error-prone, and can lead to worker injuries. In this project, automatic waste separation was implemented using an embedded microcontroller, USB camera, motor driver, and waste container. This process uses imaging tools to separate waste into categories such as plastic, metal, and organic materials. The USB camera captures real-time images of waste on the conveyor belt, which are then analysed using algorithms developed by the machine. According to the classification, the microcontroller controls the motor by sending signals to deliver the waste to the appropriate storage location. This setup provides instant and accurate identification of waste while minimizing human intervention. The system is cost-effective and scalable, designed for use in municipal waste management facilities, recycling facilities and industrial waste treatment plants. The system increases traceability and reduces environmental impact, encourages recycling and reduces the use of waste materials. Its user-friendly design and low maintenance requirements make it an innovative solution to the complex problems of waste management.},
        keywords = {Computer Vision, Garbage Classification, Waste Management, Machine Learning, Deep Learning, Image Processing, Convolutional Neural Networks (CNN), Smart Waste Segregation, Embedded Systems, Automation, Sustainable Waste Management.},
        month = {February},
        }