Kutluhan Aktar / IoT AI-driven Yogurt Processing Public

Target: Cortex-M4F 80MHz Clone this project

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IoT AI-driven Yogurt Processing

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.

Data (120 samples)

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Optimum.sample_22

ID: 156145814

Label: Optimum

Curdling.sample_5

ID: 156145855

Label: Curdling

Thinner.sample_31

ID: 156145766

Label: Thinner

Curdling.sample_16

ID: 156145759

Label: Curdling

Curdling.sample_9

ID: 156145812

Label: Curdling

Thinner.sample_35

ID: 156145762

Label: Thinner

Curdling.sample_18

ID: 156145760

Label: Curdling

Curdling.sample_20

ID: 156145758

Label: Curdling

Model accuracy (Unoptimized (float32))

Validation set

100.0%

Test set

100.0%

On-device performance (Cortex-M4F 80MHz, Quantized (int8))

Latency

1 ms.

Peak RAM usage

1.7K

Flash usage

16.2K

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Dataset summary

Data collected

2m 0s

Sensors

temperature, humidity, pressure, milk_temperature, starter_weight @ 1Hz

Labels

Curdling, Optimum, Thinner

Project info

Project ID	159184
Project version	1
License	Apache 2.0
No. of views	29,189
No. of clones	14