Kutluhan Aktar / IoT AI-driven Yogurt Processing
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Maintenance window planned next Monday, 04/29 at 5:00AM UTC (more)
About this project
Project Description
This model detects yogurt consistency (texture) classes before fermentation based on temperature, humidity, pressure, milk temperature, and culture weight measurements:
- Thinner
- Optimum
- Curdling
After building my neural network model, I deployed my model as an Arduino library and uploaded it to XIAO ESP32C3. Also, I employed XIAO ESP32C3 to communicate with the Blynk application I designed to run the neural network model remotely and transmit the collected data.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Raw data training data | NPY file | 105 windows | |
| Raw data training labels | NPY file | 105 windows | |
| Raw data testing data | NPY file | 15 windows | |
| Raw data testing labels | NPY file | 15 windows | |
| NN Classifier model | TensorFlow Lite (float32) | 3 KB | |
| NN Classifier model | TensorFlow Lite (int8 quantized) | 2 KB | |
| NN Classifier model | TensorFlow SavedModel | 9 KB | |
| NN Classifier model | Keras h5 model | 3 KB |
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Summary
Data collected
2m 0sProject info
| Project ID | 159184 |
| Project version | 1 |
| License | Apache 2.0 |
