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.
- 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