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

Hazardous.training.sample_429

ID: 108390146

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_75

ID: 108390246

Label: Hazardous

Sample length: 1s

Regulated.training.sample_110

ID: 108390052

Label: Regulated

Sample length: 1s

Regulated.training.sample_195

ID: 108390032

Label: Regulated

Sample length: 1s

Hazardous.training.sample_77

ID: 108390245

Label: Hazardous

Sample length: 1s

Unsafe.training.sample_493

ID: 108389789

Label: Unsafe

Sample length: 1s

Regulated.training.sample_9

ID: 108390090

Label: Regulated

Sample length: 1s

Unsafe.training.sample_312

ID: 108389842

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	32,168
No. of clones	3