# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 5 Conclusions  
The present study has demonstrated the technical and economic feasibility of the system developed and presented in this work, which is based on Internet of Things (IoT) technologies, such as Near-Field Communication (NFC), and a LoRaWAN network, for the automatic identification of laying hens in family poultry farms. The system's functionality has been validated under real-world conditions, confirming its ability to optimize poultry farm management by significantly reducing the need for manual intervention in comparison with the traditional method described. In particular, the system simplified daily routines by reducing the number of visits required to the coop and eliminating the time-consuming manual identification process. Furthermore, by automating the identification process, the system reduces the risk of human error and ensures more consistent, traceable, and objective data collection over time.  
The implementation of message reception tools, such as Telegram and the mobile application, in conjunction with data visualization platforms like Grafana in a web application, has enabled detailed real-time analysis of the collected data, facilitating informed decision making.  
The system's cost-effectiveness makes it accessible to small-scale poultry farmers, allowing family farms to adopt advanced technologies that were previously exclusive to large-scale poultry industries. The use of shared LoRaWAN infrastructure provides a scalable and sustainable solution, encouraging collaboration among multiple producers in rural areas.  
The successful implementation of the proof of concept (PoC) and the positive feedback from the participating farmer indicate that the system has significant potential for technology transfer and could evolve into a commercially viable product. Incorporating training programs for poultry farmers could facilitate adoption and maximize the system's impact on the sector.  
Field tests provide real-time data on hen activity. The analysis of this data, facilitated by Grafana, could support future studies on behavioral patterns, including the frequency and duration of nesting box usage and the influence of environmental factors (such as temperature and humidity) on egg laying. These insights could significantly contribute to the optimization of poultry production.  
In conclusion, this study demonstrates that the implementation of IoT technologies in conjunction with LoRaWAN networks in family poultry farms can substantially enhance productivity and animal welfare, contributing to the sustainable development of organic poultry farming in rural environments.  
Conceptualization and methodology, R.F., C.M. and A.P.d.M.; software, formal analysis, and data curation, R.F., C.M. and A.P.d.M.; validation, R.F., C.M. and A.P.d.M.; investigation, R.F., C.M., A.P.d.M. and M.R.; writing--original draft preparation, R.F., C.M., A.P.d.M. and M.R.; writing--review, editing and visualization, C.M. and M.R.; supervision and project administration, C.M. and A.P.d.M.; resources and funding acquisition, C.M., A.P.d.M. and M.R. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Control and Communication Systems Department of the Universidad Nacional de Educacion a Distancia (UNED), Spain.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors would like to express their gratitude to [PERSON], the poultry farmer, for his valuable collaboration in the implementation and testing of the system in a real-world environment. His collaboration was indispensable for the validation of this study and for gaining a more profound understanding of the needs of the family poultry farming sector.  
The authors declare no conflicts of interest.