Agriculture industries contributed $25.1 billion or 51% of GDP in Canada and this market is expected to grow up to $85 billion by 2025. This project will provide a small incremental benefit of doing pest detection on low resource device to the end users.

Technical Insipiration

  • Tensorflow has recently released TFLite, a library that allows developers to build on-device models that provides personalized, low-latency and high-quality recommendations, while preserving users’ privacy.

What it does

Identify pests, insects efficiently with EfficientNet, an optimized, low-latency solution that can be used on low resources devices like RaspberryPi, Smartphones.

How I built it

  • Tensorflow TF Lite library
  • Jupyter notebooks & Google Colab
  • Web scrapping
  • HTML, CSS, JS for the looks

Challenges I ran into

  • Video Production :-)
  • Curating dataset

What I learned

  • DMADV Methodology by Dr. Amy. Planning to use it more frequently.
  • learned a lot about the different problems that arise at different stages in agriculture.
  • Got introduced to Carbon sequestration, soil erosion.

What's next for Gotta Catch them all! (The Pests)

  • Integrate with
    • Android devices
    • Intel openVino platform

Built With

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