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_96

ID: 108390066

Label: Regulated

Sample length: 1s

Hazardous.training.sample_430

ID: 108390144

Label: Hazardous

Sample length: 1s

Unsafe.training.sample_396

ID: 108389821

Label: Unsafe

Sample length: 1s

Regulated.training.sample_107

ID: 108390057

Label: Regulated

Sample length: 1s

Unsafe.training.sample_224

ID: 108389872

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_146

ID: 108389885

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_143

ID: 108389894

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_303

ID: 108389854

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

Clone project

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	42,589
No. of clones	3