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Deployment of electronics component classifier using ML in Raspberry Pi 3

  • Unique Paper ID: 166144
  • Volume: 11
  • Issue: 2
  • PageNo: 233-237
  • Abstract:
  • This research project aims to develop a machine learning (ML)--based system for the classification of electronic components on a Raspberry Pi 3 platform. The purpose is to create an efficient, cost-effective solution for the automated sorting and identification of electronic components, which can be particularly beneficial in manufacturing and recycling operations. The methodology encompasses the collection and preprocessing of component images, training a convolutional neural network (CNN) model using these images, and deploying the trained model on the Raspberry Pi 3 for real-time classification. The findings indicate that the Raspberry Pi 3 is capable of executing the ML model with high accuracy, demonstrating over 90% precision in classifying various electronic components. This suggests that even lowpowered devices can effectively perform complex ML tasks, offering a scalable and accessible approach to electronic component classification. The success of this project opens up new avenues for the application of ML in compact and costsensitive environments.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 233-237

Deployment of electronics component classifier using ML in Raspberry Pi 3

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