Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
In medium- and large-scale poultry farms, automated systems optimize key processes, from egg production and grading to environmental control, reducing manual labor and ensuring an optimal environment for the birds. However, these technologies remain largely inaccessible to small family farms due to...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/9/4856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In medium- and large-scale poultry farms, automated systems optimize key processes, from egg production and grading to environmental control, reducing manual labor and ensuring an optimal environment for the birds. However, these technologies remain largely inaccessible to small family farms due to high implementation costs. In particular, the selection of laying hens, an essential process for productivity, is still performed manually and requires considerable time and effort. This study presents the development of a modular, low-cost, and minimally invasive IoT system for the automatic detection of laying hens in family-run poultry farms. Additionally, the system enables environmental monitoring and utilizes LoRaWAN networks for efficient long-range data transmission. The collected data are stored on a centralized platform and integrated with web, mobile, and messaging applications to provide real-time access to information. The modular system architecture, developed using open-source software, ensures replicability, scalability, and adaptability to different production environments. The feasibility of the system has been validated through field trials in a real-world environment, demonstrating effective performance, low implementation costs, and high farmer satisfaction, with the user highlighting its positive impact on poultry farm management. |
|---|---|
| ISSN: | 2076-3417 |