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_265

ID: 108390183

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_511

ID: 108390124

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_433

ID: 108390139

Label: Hazardous

Sample length: 1s

Regulated.training.sample_462

ID: 108389956

Label: Regulated

Sample length: 1s

Regulated.training.sample_192

ID: 108390033

Label: Regulated

Sample length: 1s

Unsafe.training.sample_417

ID: 108389808

Label: Unsafe

Sample length: 1s

Regulated.training.sample_461

ID: 108389954

Label: Regulated

Sample length: 1s

Regulated.training.sample_1

ID: 108390095

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	48,116
No. of clones	3