Open Access
| Issue |
E3S Web Conf.
Volume 658, 2025
Third International Conference of Applied Industrial Engineering: Intelligent Models and Data Engineering (CIIA 2025)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 12 | |
| Section | Intelligent Connectivity | |
| DOI | https://doi.org/10.1051/e3sconf/202565803005 | |
| Published online | 13 November 2025 | |
- EmpresasEcuador.com, La ganadería en el Ecuador: un vistazo detallado a una industria pilar de la economía nacional (2023). https://empresasecuador.com/como-es-la-ganaderia-del-ecuador/ [Google Scholar]
- G.K. Viteri Guzmán, I.H. Monserrate Sánchez, A.E. Arrese Vilche, Tecnología IoT para el monitoreo de salud animal en el sector ganadero, La técnica 14(1), 60–68 (2024). [Google Scholar]
- Vistazo, Investigación examina enfermedad que acecha al ganado vacuno en varias provincias del Ecuador (2021). https://www.vistazo.com/enfoque/investigacion-examina-enfermedad-que-acecha-al-ganado-vacuno-en-varias-provincias-del-ecuador-LY1032937 [Google Scholar]
- WOAH, Terrestrial Animal Health Code (n.d.). https://www.woah.org/en/what-we-do/animal-health-and-welfare/disease-data-collection [Google Scholar]
- D. Fraser, Understanding animal welfare, Acta Vet. Scand. 50, S1 (2008). https://doi.org/10.1186/1751-0147-50-S1-S1 [Google Scholar]
- S. Li, L. Da Xu, S. Zhao, The internet of things: a survey, Inf. Syst. Front. 17(2), 243–259 (2015). https://doi.org/10.1007/s10796-014-9492-7 [Google Scholar]
- D. Nilay, IoT-enabled livestock management: revolutionizing animal tracking and monitoring, Intuz (2024). https://www.intuz.com/blog/iot-enabled-livestock-management [Google Scholar]
- Terence, S., Immaculate, J., Raj, A., & Nadarajan, J. (2024). Systematic Review on Internet of Things in Smart Livestock Management Systems. Sustainability, 16(10), 4073. https://doi.org/10.3390/su16104073 [Google Scholar]
- B. Smith, A. Jones, K. Patel, Advances in IoT applications for animal welfare and sustainable livestock production, J. Anim. Sci. 98(4), 1234–1248 (2020). https://doi.org/10.1093/jas/skaa045 [Google Scholar]
- C.C. Garnica, Diseño de un sistema de escritura y dibujo virtuales, basado en un acelerómetro (2021). https://www.vlsilab.cinvestav.mx/files/Munoz-Garnica-MC-ppt-marb.pdf [Google Scholar]
- J.D.C. Medina, L.J.R. López, Utilización de IoT en el diagnóstico temprano del Covid-19, Des. Innov. Ing., 429 (2021). [Google Scholar]
- L.A.E. Ponce, Y.R. García, J.M.M. Alonso, A.L. González, H.A.F. Vargas, Dispositivo de rastreo GPS para ganado bovino, Pistas Educativas 39(127) (2018). [Google Scholar]
- Mendes Junior, J. J. A., Campos, D. P., Biassio, L. C. D. A. V. D., Passos, P. C., Júnior, P. B., Lazzaretti, A. E., & Krueger, E. (2023). AD8232 to biopotentials sensors: Open source project and benchmark. Electronics, 12(4), 833. [Google Scholar]
- Arduino, IoT Remote App (n.d.). https://docs.arduino.cc/arduino-cloud/ [Google Scholar]
- F. Pedregosa, V. Varoquaux, G. Gramfort, V. Michel, B. Thirion, O. Grisel, et al., Scikit-learn: machine learning in Python, J. Mach. Learn. Res. 12, 2825–2830 (2011). [Google Scholar]
- Django Software Foundation, Django documentation (n.d.). https://docs.djangoproject.com/ [Google Scholar]
- H. Bastos, python-decouple documentation (2023). https://pypi.org/project/python-decouple/ [Google Scholar]
- E. Gazoni, C. Clark, openpyxl documentation (n.d.). https://openpyxl.readthedocs.io/ [Google Scholar]
- Tivix Inc., django-cron documentation (n.d.). https://django-cron.readthedocs.io/ [Google Scholar]
- G. Hofstra, J. Roelofs, S.M. Rutter, E. van Erp-van der Kooij, J. de Vlieg, Potential of Google Geolocation in managing cattle welfare, J. Agric. Technol. (2022). https://doi.org/10.3390/dairy3040053 [Google Scholar]
- Montalván, S., Arcos, P., Sarzosa, P., Rocha, R. A., Yoo, S. G., & Kim, Y. (2024). Technologies and solutions for cattle tracking: A review of the state of the art. Sensors, 24(19), 6486. https://doi.org/10.3390/s24196486 [Google Scholar]
- J. Tamilselvan, M. Naveenkumar, K. Periyapandi, B. Premkumar, Cattle health monitoring system using Arduino and IoT, IJIRT 7(11) (2021). https://www.ijirt.org/master/publishedpaper/IJIRT150992_PAPER.pdf [Google Scholar]
- A.A. Chaudhry, R. Mumtaz, S.M.H. Zaidi, M.A. Tahir, S.H. Muzammil, Internet of Things (IoT) and Machine Learning (ML) enabled livestock monitoring, in Proc. 2020 IEEE 17th Int. Conf. Smart Communities (HONET), 151–155 (IEEE, 2020). https://doi.org/10.1109/HONET50430.2020.9322666 [Google Scholar]
- Engineers Garage, Arduino-based heartbeat and body-temperature monitoring IoT device (2018). https://www.engineersgarage.com/arduino-based-heartbeat-and-body-temperature-monitoring-iot-device/ [Google Scholar]
- Microcontrollers Lab, Measuring heart rate using pulse sensor and Arduino (2022). https://microcontrollerslab.com/pulse-sensor-arduino-tutorial/ [Google Scholar]
- I. Shabani, T. Biba, B. Çiço, Design of a cattle-health-monitoring system using microservices and IoT devices, Computers 11(5), 79 (2022). https://doi.org/10.3390/computers11050079 [Google Scholar]
- Hernández, G., López, R., Martínez, J., & Torres, F. (2024). Machine learning-based prediction of cattle activity using low-cost sensors. Sensors, 24(10), 3157. https://doi.org/10.3390/s24103157 [Google Scholar]
- A. Batla, Y. Kikani, D. Joshi, R. Jain, K. Patel, Real time cattle health monitoring using IoT, ThingSpeak, and a mobile application, J. Ethol. Anim. Sci. (JEASc) 5(1) (2023). [Google Scholar]
- O. Unold, M. Nikodem, M. Piasecki, K. Szyc, H. Maciejewski, M.A. Bawiec, P. Dobrowolski, M. Zdunek, IoT-based cow health monitoring system, Comput. Sci. – ICCS 2020, 12141, 344–356 (2020). https://doi.org/10.1007/978-3-030-50426-7_26 [Google Scholar]
- R. Aunindita, M. Misbah, S. Joy, M.A. Rahman, S. Mahabub, J. Noor, Use of machine learning and IoT for monitoring and tracking of livestock, 815–820 (2022) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

