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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Regulated.training.sample_17

ID: 108390082

Label: Regulated

Sample length: 1s

Regulated.training.sample_375

ID: 108389978

Label: Regulated

Sample length: 1s

Regulated.training.sample_279

ID: 108390009

Label: Regulated

Sample length: 1s

Hazardous.training.sample_254

ID: 108390194

Label: Hazardous

Sample length: 1s

Unsafe.training.sample_35

ID: 108389945

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_260

ID: 108390188

Label: Hazardous

Sample length: 1s

Regulated.training.sample_371

ID: 108389980

Label: Regulated

Sample length: 1s

Regulated.training.sample_18

ID: 108390083

Label: Regulated

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	49,496
No. of clones	4