TinyML and IoT-enabled system for automated chicken egg quality analysis and monitoring
The poultry industry grapples with challenges in maintaining optimal egg quality during incubation, with environmental factors such as temperature, humidity, and lighting playing crucial roles. Traditional methods of egg quality assessment often lack precision and can be time-consuming and costly. T...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003946 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The poultry industry grapples with challenges in maintaining optimal egg quality during incubation, with environmental factors such as temperature, humidity, and lighting playing crucial roles. Traditional methods of egg quality assessment often lack precision and can be time-consuming and costly. This study addresses these challenges by introducing an innovative solution that combines Artificial Intelligence (AI) and Internet of Things (IoT) technologies, offering a transformative approach to automating the egg mirage process and improving overall egg quality analysis. The web-based program provides real-time feedback on egg quality, utilizing a Convolutional Neural Network (CNN) algorithm. Our system, implemented on Arduino Nano 33 BLE Sense, demonstrated remarkable performance with a TinyML classification F1-Score of 97.4 % and an accuracy rate of 95.79 %, paving the way for a more precise and efficient method of egg quality monitoring. The success of this research not only revolutionizes egg quality monitoring in the poultry industry but also sets a precedent for the integration of AI and IoT in addressing complex challenges in agricultural practices, including power management. |
|---|---|
| ISSN: | 2772-3755 |