Kutluhan Aktar / IoT AI-driven Yogurt Processing
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
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.
Creating your first impulse (100% complete)
Acquire data
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Deploy
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
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 |
