Kutluhan Aktar / IoT AI-driven Food Irradiation Classifier Public

Target: Cortex-M4F 80MHz Clone this project

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IoT AI-driven Food Irradiation Classifier

About this project

Project Description

This artificial neural network model (ANN) makes predictions on food irradiation dose levels (classes) based on ionizing radiation, weight, and visible light (color) measurements:

Regulated
Unsafe
Hazardous

After building my neural network model, I deployed my model as an Arduino library and uploaded it to Beetle ESP32-C3 so as to run inferences.

Data (540 samples)

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Hazardous.training.sample_529

ID: 108390110

Label: Hazardous

Unsafe.training.sample_31

ID: 108389940

Label: Unsafe

Hazardous.training.sample_534

ID: 108390104

Label: Hazardous

Unsafe.training.sample_214

ID: 108389878

Label: Unsafe

Unsafe.training.sample_142

ID: 108389893

Label: Unsafe

Regulated.training.sample_455

ID: 108389962

Label: Regulated

Hazardous.training.sample_528

ID: 108390114

Label: Hazardous

Unsafe.training.sample_324

ID: 108389833

Label: Unsafe

Model accuracy (Unoptimized (float32))

Validation set

100.0%

Test set

96.3%

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

Latency

4 ms.

Peak RAM usage

1.7K

Flash usage

21.4K

Run this model

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Dataset summary

Data collected

9m 0s

Sensors

weight, f1, f2, f3, f4, f5, f6, f7, f8, cpm, nsv, usv @ 1Hz

Labels

Hazardous, Regulated, Unsafe

Project info

Project ID	109647
Project version	1
License	No license attached
No. of views	54,169
No. of clones	4