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@article{190484,
        author = {VISHNU KK and Sreerag V S and Jibin Joy and Ananthakrishnan S and Ms. Sheeja G and Ms. Priya K P},
        title = {A Comprehensive Literature Review on Intelligent Waste Segregation Systems Using AI, Deep Learning, IoT and Smart Bin Technologies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4182-4186},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190484},
        abstract = {Growing urban populations and increasing waste generation have made conventional waste management systems insufficient, labor-intensive, and environmentally taxing. Recent technological advancements such as artificial intelligence (AI), deep learning, computer vision, and Internet of Things (IoT) have enabled the development of intelligent waste segregation systems and smart trash bins. This paper presents a comprehensive literature review of twelve research works focusing on deep learn- ing–based waste classification, IoT-enabled monitoring systems, automated sorting mechanisms, optical sensor–based industrial systems, and artificial intelligence integrated waste profiling. The reviewed works present promising results with classification accuracies ranging from 70 to 98 percentage, along side the development of smart bin prototypes equipped with fill-level monitoring, automatic segregation, and cloud-based reporting. Despite notable progress, challenges remain in dataset limitations, scalability, real-world deployment, and mechanical reliability. This review synthesizes methodologies, highlights research gaps, compares performance trends, and outlines future directions for building robust, scalable, and cost-effective intelligent waste management systems.},
        keywords = {component, formatting, style, styling, insert},
        month = {January},
        }