eSwine Farming: A QR Code-Driven Monitoring System for Improve Efficiency and Profitability
DOI:
https://doi.org/10.48017/dj.v9iSpecial1.2866Palabras clave:
efficiency, profitability, QR code-driven, swine farming, systeResumen
A medida que avanza la tecnología, muchas actividades tradicionales corren el riesgo de desaparecer, incluida la práctica de la cría de cerdos. La cría de cerdos es una práctica antigua que se remonta al 4900 a. C., pero su eficacia puede disminuir a medida que las personas dependen cada vez más de soluciones tecnológicas más nuevas. Sin embargo, con la llegada de los códigos QR, la cría de cerdos ha adquirido una nueva dimensión, permitiendo a los ganaderos recopilar datos en tiempo real sobre el crecimiento, la salud y la producción de los cerdos. Este estudio está diseñado específicamente para mejorar la eficiencia y rentabilidad de la cría de cerdos, brindando a los agricultores información precisa y oportuna sobre el estado de sus cerdos. El sistema permite a los ganaderos recopilar datos rápida y fácilmente sobre cerdos individuales, que luego pueden analizarse para identificar cualquier problema. Esta información se puede utilizar para ayudar a los agricultores a tomar decisiones informadas sobre cómo gestionar sus operaciones de cría de cerdos, expandirse hacia prácticas nuevas y más efectivas y aumentar la rentabilidad. El sistema eSwine es una innovación significativa en la tecnología de la cría de cerdos, ya que proporciona una herramienta funcional, utilizable y confiable para que los ganaderos gestionen sus operaciones de manera más efectiva. Con una media ponderada promedio de 3,65, eSwine es un activo esencial para cualquier ganadero que busque maximizar sus ganancias manteniendo la salud y el bienestar de sus cerdos.
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Moeller, S., & Crespo, F. L. (2009). Overview of world swine and pork production. Agricultural sciences, 1, 195-208. https://books.google.com.ph/books?hl=en&lr=&id=TxGuCwAAQBAJ&oi=fnd&pg=PA 95&dq=Overview+of+World+Swine+and+Pork+Production+Steven+J.+Moeller,+ Fracisco+Le%C3%B3n+Crespo+&ots=zsk4wsVcIt&sig=T611wPdpPjc82wefaF4N0Y 4A&redir_esc=y#v=onepage&q=Overview%20of%20World%20Swine%20and%20 Pork%20Production%20%20Steven%20J.%20Moeller%2C%20Francisco%20Le%C3 %B3n%20Crespo&f=false.
Philippine Star. (2016). Philippine beefs up competitiveness of swine industry https://www.philstar.com/business/agriculture/2016/03/12/1562207/philippine- beefs-competitiveness-swine-industry#:~:text=The%20Philippine%20swine%20industry%20is,kept%20by%20s mallhold%20pig%20raisers.
Acosta, A. (2022, March 29) Pronutrients, Swine Philippine Swine Update. https://www.veterinariadigital.com/en/articulos/philippine-swine-update/
Catelo, M.A., (2017) Sustainable Productivity Growth in Philippine Swine Production As an Journal of Agricultural Extension, Economics & Sociology Retrieved from https://zenodo.org
Dipay, J. M. B., Fabregas, A. C., & Ado, R. G. (2018). Animal Identification and Records Monitoring Tool using RFID (AIRMTR). KnE Social Sciences, 721-730. https://doi.org/10.18502/kss.v3i6.2415.
Frost, A. R., Schofield, C. P., Beaulah, S. A., Mottram, T. T., Lines, J. A., & Wathes, C. M. (2016). A review of livestock monitoring and the need for integrated systems. Computers and electronics in agriculture, 17(2), 139-159. https://www.sciencedirect.com/science/article/abs/pii/S0168169996013014.
Dogan, H., et.al, (2016) Use of Radio Frequency Identification Systems on Animal Monitoring. Suleyman Dimirel University. Isparta, Turkey Retrieved from: https://www.researchgate.net/publication/308167938_Use_of_Radio_Frequency _Identification_Systems_on_Animal_Monitoring Retrieved date: March 26, 2021
Tiwari, S. (2016, December). An introduction to QR code technology. In 2016 international conference on information technology (ICIT) (pp. 39-44). IEEE. https://ieeexplore.ieee.org/abstract/document/7966807.
Focardi, R., Luccio, F. L., & Wahsheh, H. A. (2019). Usable security for QR code. Journal of Information Security and Applications, 48, 102369. https://www.sciencedirect.com/science/article/abs/pii/S2214212619301693,
Khandal, D., & Somwanshi, D. (2017, August). A novel cost-effective access control and auto filling form system using QR code. In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1-5). IEEE. https://ieeexplore.ieee.org/abstract/document/7275575
Tarjan, L., Šenk, I., Tegeltija, S., Stankovski, S., & Ostojic, G. (2016). A readability analysis for QR code application in a traceability system. Computers and Electronics in Agriculture, 109, 1–11. doi:10.1016/j.compag.2014.08.015 https://www.sciencedirect.com/science/article/abs/pii/S0168169914002142
Yang, Feng & Wang, Kaiyi & Han, Hanyun & Qiao, Zhong. (2018). A Cloud-Based Digital Farm Management System for Vegetable Production Process Management and Quality Traceability. Sustainability. 10. 4007. 10.3390/su10114007. https://www.researchgate.net/publication/328682719_A_CloudCloudBased _Digital_Farm_Management_System_for_Vegetable_Production_Process_ Management_and_Quality_Traceability/citation/download
Neethirajan, S. (2020). Digitalization of Animal Farming. https://www.preprints.org/manuscript/202007.0040/v1
Lyons, C.; Bruce, J.; Fowler, V.; English, P. A comparison of Productivity and Welfare of Growing Pigs in Four Intensive Systems. Livest. Prod. Sci. 1995, 43, 265–274
Sahin, C.; Bolat, E.D. Development of Remote Control and Monitoring of Web-based Distributed OPC system. Comput. Stand. Interfaces 2009, 31, 984–993.
Li, L.H.; Huang, R.L.; Huo, L.M.; Li, J.X.; Chen, H. Design and Experiment on Monitoring Device for Layers Individual Production Performance Parameters. Trans. Chin. Soc. Agric. Eng. 2012, 28, 160–164.
Mungroo, N.; Neethirajan, S. Biosensors for the Detection of Antibiotics in Poultry Industry—A Review. Biosensors 2014, 4, 472–493.
Godfray, H.C.J.; Garnett, T. Food security and sustainable intensification. Philos. Trans. R. Soc. B Biol. Sci. 2014, 369, 20120273.
Pan, L.; Xu, M.; Xi, L.; Hao, Y. Research of Livestock Farming IoT System Based on RESTful Web Services. In Proceedings of the 5th International Conference on Computer Science Network Technology, Changchun, China, 10–11 December 2016; pp. 113–116.
Ahmed, S.T.; Mun, H.S.; Islam, M.M.; Yoe, H.; Yang, C.J. Monitoring Activity for Recognition of Illness in Experimentally Infected Weaned Piglets Using Received Signal Strength Indication ZigBee-based Wireless Acceleration Sensor. Asian-Austral. J. Anim. Sci. 2016, 29, 149–156.
Vranken, E.; Berckmans, D. Precision Livestock Farming for Pigs. Anim. Front. 2017, 7, 32–37.
Neethirajan, S. Recent Advances in Wearable Sensors for Animal Health Management. Sens. Bio-Sensing Resh. 2017, 12, 15–29.
Racewicz, P.; Sobek, J.; Majewski, M.; Rozanska-Zawieja, J. The Use of Thermal Imaging Measurements in Dairy Cow Herds. Anim. Sci. Genet. 2018, 14, 55–69.
Benjamin, M.; Yik, S. Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners. Animals 2019, 9, 133.
Choi, H.; Mayakrishnan, V.; Kim, T.; Lim, D.; Park, S. Livestock Production in Korea: Recent Trend and Future Prospects of ICT Technology. FFTC Agric. Policy Platf. 2019. Available online: https://ap.fftc.org.tw/article/1616 (accessed on 10 January 2021)
Trendov, N.M.; Varas, S.; Zeng, M. Digital Technologies in Agriculture and Rural Areas; FAO: Rome, Italy, 2019; p. 26.
Van der Burg, S.; Bogaardt, M.J.; Wolfert, S. Ethics of Smart Farming: Current Questions and Directions for Responsible Innovation Towards the Future. NJAS Wagening J. Life Sci. 2019, 90, 100289.
Bacco, M.; Barsocchi, P.; Ferro, E.; Gotta, A.; Ruggeri, M. The Digitisation of Agriculture: A Survey of Research Activities on Smart Farming. Array 2019, 3–4, 100009.
FAO. The State of Food and Agriculture: Livestock in Balance; FAO: Rome, Italy, 2009; Volume 180, pp. 492–496.
Lekagul, A.; Tangcharoensathien, V.; Liverani, M.; Mills, A.; Rushton, J.; Yeung, S. Understanding antibiotic use for pig farming in Thailand: A qualitative study. Antimicrob. Resist. Infect. Control 2021, 10, 3.
Pandey, S.; Kalwa, U.; Kong, T.; Guo, B.; Gauger, P.; Peters, D.; Yoon, K. Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap. Animals 2021, 11, 2665.
Bailey, D.; Trotter, M.; Tobin, C.; Thomas, M. Opportunities to Apply Precision Livestock Management on Rangelands. Agric. Spat. Anal. Model. 2021, 5, 1–13.
Hashem, N.M.; Hassanein, E.M.; Hocquette, J.-F.; Gonzalez-Bulnes, A.; Ahmed, F.A.; Attia.
Y.A.; Asiry, K.A. Agro-Livestock Farming System Sustainability during the COVID-19 Era: A Cross-Sectional Study on the Role of Information and Communication Technologies. Sustainability 2021, 13, 6521.
Schillings, J.; Bennett, R.; Rose, D.C. Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare. Front. Anim. Sci. 2021, 2, 639678.
Micle, D.; Deiac, F.; Olar, A.; Drența, R.F.; Florean, C.; Coman, I.G.; Arion, F.H. Research on Innovative Business Plan. Smart Cattle Farming Using Artificial Intelligent Robotic Process Automation. Agriculture 2021, 11, 430.
Long, S.; He, T.; Kim, S.W.; Shang, Q.; Kiros, T.; Mahfuz, S.U.; Wang, C.; Piao, X. Live Yeast or Live Yeast Combined with Zinc Oxide Enhanced Growth Performance, Antioxidative Capacity, Immunoglobulins and Gut Health in Nursery Pigs. Animals 2021, 11, 1626.
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Derechos de autor 2024 Glenda Binay, Chelsey, Ma. Angela Manayon, Jake Robles
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