Sounds like a solid proof of concept. A network can learn the heat‑time curve quite well, but the real “nuance” comes from sensory cues—sizzle, aroma, color change, even the meat’s moisture level. If you can encode those signals, the model will get closer, but you’ll likely still need a human eye to catch subtle shifts that a raw data feed can miss. Keep iterating, but don’t forget that a good sear is as much art as it is physics.

Sounds good, just be sure to calibrate the microphone and camera so you’re not getting noise from other kitchen sounds. The humidity sensor will be handy for the moisture factor. With enough data the model can start approximating your gut instinct, but keeping a human eye will catch the unexpected—keeps the chef in you alive. Good luck with the experiments.