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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Unsafe.training.sample_145

ID: 108389888

Label: Unsafe

Regulated.training.sample_378

ID: 108389973

Label: Regulated

Unsafe.training.sample_36

ID: 108389934

Label: Unsafe

Unsafe.training.sample_486

ID: 108389799

Label: Unsafe

Unsafe.training.sample_230

ID: 108389865

Label: Unsafe

Hazardous.training.sample_77

ID: 108390245

Label: Hazardous

Unsafe.training.sample_306

ID: 108389848

Label: Unsafe

Regulated.training.sample_376

ID: 108389975

Label: Regulated

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	55,810
No. of clones	4