Firover - Forest Fire Detection Rover

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  Fireover: Forest Fire Detecting Rover

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  Model successfully detecting fire.

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  Model producing negative result after detecting Snehal's face.

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  Building the Rover

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  Team working on the project.

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  Team working on the project.

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  Team Picture (Parthiv missing)

Inspiration

In June 2023, Southern Quebec faced an unprecedented surge in forest fires, consuming more land in 25 days than the past two decades combined. Among the consequences was the largest recorded single fire, devouring 460,000 hectares. These wildfires, beyond polluting the air, released massive amounts of carbon dioxide, exacerbating climate change.

Many such recent forest fire tragedies inspired our project Firover - Forest Fire Detection Rover. Imagine small rovers patrolling high-risk areas in forests, powered by machine learning to detect early fire outbreaks. Firover rovers send early warnings to prevent disasters. This project is our response to the urgent need for innovative solutions in forest fire prevention, addressing immediate risks and contributing to broader environmental preservation efforts.

What it does

Our rovers patrol fire-prone high-risk areas in forests. They detect early fires using machine learning and send distress signals, so the fire can be stopped before it spreads out of control.

How we built it

1. We built an autonomous rover using Arduino Nano.
2. We trained a machine learning model to detect fire.
3. We uploaded the model onto the Arduino using edge impulse.

Challenges we ran into

1. controlling the speed of the rover
2. understanding the mapping
3. uploading the ML model onto the chip

Accomplishments that we're proud of

This was the first hardware project for the majority of our team. We learned to build a rover from scratch in less than 24 hours.

What we learned

1. How to train ML models to detect fire with 93% accuracy.
2. How to upload ML models onto Arduino using Arduino TinyML kit.

What's next for Firover - Forest Fire Detection Rover

Next, we want to deploy patrolling drones along with the rovers to detect fires.

Built With

• arduino-nano
• c++
• edgeimpulse
• kaggle
• python
• tensorflow
• tensorflow-lite