ShawnHymel / perfect-toast-machine Public

perfect-toast-machine

About this project

The Perfect Toast Machine

This project attempts to toast bread using odor and temperature data (rather than relying on a simple timer). Using this data, we attempt to predict the "time remaining before burnt" using regression. From this, we can estimate a level of "doneness" e.g. by saying that "toast will be perfect 40 seconds before being burned." By hacking a toaster to cancel the toasting process at this point, we should, in theory, be able to perfectly make toast regardless of starting temperature and bread thickness or composition.

Gas and odor data collected from various types of bread over a Black and Decker simple two-slot toaster. Data was standardized before being uploaded to Edge Impulse. The original dataset, curation script, and inference code can be found here: https://github.com/ShawnHymel/perfect-toast-machine.

A full tutorial showing how to build this AI-powered toaster can be found here: https://www.digikey.com/en/maker/projects/how-to-build-an-ai-powered-toaster/2268be5548e74ceca6830bf35f0f0f9e.

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8.86965986d1774531876340b32cb7fc49
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-21.ef87a7799bb840c989741ce8e9cf27f0

Run this model

On any device

Dataset summary

Data collected
18h 15m 50s
Sensors
temp, humd, co2, voc1, voc2, no2, eth, co, nh3 @ 2Hz
Labels
-68 .. 202

Project info

Project ID 129477
Project version 2
License Apache 2.0
No. of views 309,899
No. of clones 16