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_203

ID: 108390023

Label: Regulated

Unsafe.training.sample_43

ID: 108389925

Label: Unsafe

Unsafe.training.sample_129

ID: 108389902

Label: Unsafe

Unsafe.training.sample_410

ID: 108389809

Label: Unsafe

Hazardous.training.sample_80

ID: 108390241

Label: Hazardous

Unsafe.training.sample_44

ID: 108389924

Label: Unsafe

Unsafe.training.sample_501

ID: 108389781

Label: Unsafe

Unsafe.training.sample_130

ID: 108389903

Label: Unsafe

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	60,127
No. of clones	4