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)

View all

Regulated.training.sample_276

ID: 108390014

Label: Regulated

Sample length: 1s

Unsafe.training.sample_235

ID: 108389860

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_350

ID: 108390159

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_428

ID: 108390147

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_71

ID: 108390250

Label: Hazardous

Sample length: 1s

Regulated.training.sample_454

ID: 108389959

Label: Regulated

Sample length: 1s

Unsafe.training.sample_35

ID: 108389945

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_502

ID: 108389782

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	38,519
No. of clones	3