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_428

ID: 108390147

Label: Hazardous

Unsafe.training.sample_324

ID: 108389833

Label: Unsafe

Hazardous.training.sample_263

ID: 108390184

Label: Hazardous

Hazardous.training.sample_526

ID: 108390112

Label: Hazardous

Unsafe.training.sample_214

ID: 108389878

Label: Unsafe

Hazardous.training.sample_333

ID: 108390180

Label: Hazardous

Hazardous.training.sample_441

ID: 108390133

Label: Hazardous

Regulated.training.sample_274

ID: 108390017

Label: Regulated

Model accuracy (Unoptimized (float32))

Validation set

100.0%

Test set

96.3%

On-device performance (Cortex-M4F 80MHz, Quantized (int8))

Latency

1 ms.

Peak RAM usage

1.7K

Flash usage

21.4K

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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	62,754
No. of clones	4