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_243

ID: 108390203

Label: Hazardous

Regulated.training.sample_19

ID: 108390078

Label: Regulated

Regulated.training.sample_203

ID: 108390023

Label: Regulated

Unsafe.training.sample_506

ID: 108389778

Label: Unsafe

Hazardous.training.sample_424

ID: 108390149

Label: Hazardous

Regulated.training.sample_475

ID: 108389932

Label: Regulated

Unsafe.training.sample_131

ID: 108389900

Label: Unsafe

Unsafe.training.sample_227

ID: 108389868

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

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	61,109
No. of clones	4