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Fruit and Vegetable Detection Using Machine Learning on Embedded Systems

  • Unique Paper ID: 159092
  • Volume: 9
  • Issue: 11
  • PageNo: 308-312
  • 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.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 11
  • PageNo: 308-312

Fruit and Vegetable Detection Using Machine Learning on Embedded Systems

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