Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{170173,
        author = {Zoyafatema khan and Alfiya Sheikh and Bhavika Makode and Vanshika Makode and Dr.Dhananjay Dumbere},
        title = {Garbage sorting in waste management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3212-3215},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170173},
        abstract = {Using an ESP32 camera, the device captures multiple images of objects placed against a clear background, generating a dataset categorized into three classes: empty, metal, and plastic. These images are then processed and labeled using the Edge Impulse platform.The model employs a three-layer object detection framework with two output labels—plastic bottle and metal can—and undergoes 60 iterations of training. The code leverages Edge Impulse’s FOMO (Faster Objects, More Objects) model for lightweight and real-time object detection on the ESP32 platform. Additional hardware includes LEDs and a servo motor for physical object sorting. The servo motor, controlled via the ESP32, enables mechanical separation based on the detected object type. This integrated system provides an efficient and low-cost solution for automated garbage sorting.},
        keywords = {},
        month = {November},
        }