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@article{159092,
        author = {Rohan Jairam and Gunadeep MB and Praveen Ajakkanavar and Bivas Bhattacharya},
        title = {Fruit and Vegetable Detection Using Machine Learning on Embedded Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {308-312},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159092},
        abstract = {This paper presents the results of a deep learning system developed to detect fruits and vegetables using the YOLO v5 model on a Raspberry Pi. The system was tested in a simulated environment, with the goal of detecting objects in real-time and with high accuracy. The model was trained on a dataset of images of different fruits and vegetables, then evaluated using precision-recall metrics. The results show that the YOLO v5 model was able to detect fruits and vegetables with high accuracy with a mean average precision of 99.9%. This system can be used in various applications such as tracking produce in supermarkets, agricultural monitoring, and robotic harvesting. Furthermore, the Raspberry Pi platform provides an economic, energy-efficient, and low-power solution for low-cost, portable, and energy-efficient fruit and vegetable detection.},
        keywords = {Keywords: Raspberry Pi; Fruit and Vegetable Detection; YOLO v5; Deep Learning},
        month = {},
        }