I remember reading about the 300 elephants found dead. Their tusks were intact and there was no evidence of poison. The mystery was solved and the water source containing toxic bloom of cyanobacterium.
My project has two software components. Edge Pulse Machine Learning Model and Balena to track environmental variables i.e Temperature, Humidity and Pressure. The hardware used is the Raspberry Pi4 and the Raspberry Pi Sense Hat.
Creating a Model
1. Connect a Device
I was able to connect my Android phone by scanning a QR code with my phone. The project updates when I make changes on my laptop.
2. Data Acquisition
I decided to use images or datasets to train the model. I checked to make sure there was no copyright or permission required to use the images from Unsplash. I created two folders on my Windows 10 laptop called Training and Testing. This would be for uploading to Impulse Edge.
Uploading is simple. You just want to navigate to the folders and select the images to upload to the cloud.
Create Impulse
Image
Transfer learning
I set the minimum confidence rate at 80%. If the confidence rate is below 80% the image will be tagged “uncertain”
Live Classification
Climate Change
There is a question of Climate Change causing water contaminated by toxic blue-green algae. So I decided to include Balena open source and cloud application to measure Temperature, Humidity and Pressure.
The image can be burned to the Raspberry Pi and can be found here: https://github.com/balenalabs/balena-sense
Burn the image to a SD card. I love using Etcher for this. Place the SD card and add power.
Create and deploy the application
