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_262

ID: 108390187

Label: Hazardous

Sample length: 1s

Regulated.training.sample_15

ID: 108390084

Label: Regulated

Sample length: 1s

Unsafe.training.sample_144

ID: 108389890

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_254

ID: 108390194

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_169

ID: 108390215

Label: Hazardous

Sample length: 1s

Regulated.training.sample_105

ID: 108390058

Label: Regulated

Sample length: 1s

Unsafe.training.sample_32

ID: 108389941

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_394

ID: 108389823

Label: Unsafe

Sample length: 1s

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

On any device

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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	45,783
No. of clones	3