Kutluhan Aktar / IoT AI-driven Yogurt Processing Public

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

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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.


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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Data collected
2m 0s

Project info

Project ID 159184
Project version 1
License Apache 2.0