Monitoring of Livestock on Large-Scale Areas using Aerospace Data and IoT Technologies
Main Article Content
Abstract
This article explores innovative solutions for developing a remote-control system to monitor cattle in vast, remote areas that are inaccessible to humans. The system, utilizing Internet of Things and geofencing technologies, enables farmers to reduce manual labor, track lost livestock, and effectively utilize pasture resources. Advanced technologies, including GPS, ultrasonic sensors, databases, antennas, and Arduino microcontrollers, were utilised in this system. Additionally, methods for assessing vegetation cover in pastures and their nutritional productivity using vegetation indices, such as NDVI, were also analysed. The research results show that the proposed solution enables increased efficiency in livestock management, as well as optimisation of energy and time costs. This, along with creating conveniences for farmers, will serve to improve the health and productivity of livestock.
Downloads
Article Details
Section

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Herrera, O., 2018. Comportamiento en pastoreo del ganado bovino criollo Argentino y aberdeen angus ecotipo Riojano, en pastizales naturales del chaco árido. Universidad Nacional del Mar de Plata. Universidad Nacional de Mar del Plata, Balcarce, Argentina, p. 94. https://repositorioslatinoamericanos.uchile.cl/handle/2250/6210205
J.Plaza, C.Palacios, M.Sánchez-García, M.Criado, J.Nieto, N.Sánchez; Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2020, https://DOI:10.5194/isprs-archives-XLIII-B4-2020-169-2020
S.Oleinik, V.Skripkin, T.Lesnyak*, and D.Litvin, BIO Web of Conferences 66, 09007 (2023). https://doi.org/10.1051/bioconf/20236609007
R. Catorci, L. Lulli, M. Malatesta, F. Tavoloni, and M. Tardella, Agric. Ecosyst. Environ., 314, 107372 (2021)
https://doi:10.1016/j.agee.2021.107372.
R.R. Fern, E.A. Foxley, A. Bruno, and M. L. Morrison, Ecol. Indic., 94, 16–21 (2018) https://doi:10.1016/j.ecolind.2018.06.029.
Bailey, D. W. (2016). “Grazing and animal distribution,” in Animal Welfare in Extensive Systems, eds J. J. Villalba and X. Manteca (Sheffield: 5M Publishing), 53–77. DOI:10.1093/tas/txx006
Siebert, B., and Macfarlane, W. (1975). Dehydration in desert cattle and camels. Physiol. Zool. 48, 36–48. https://Doi:10.1086/physzool. 48.1.30155636
Bailey, D. W., Trotter, M. G., Knight, C. W., and Thomas, M. G. (2018). Use of GPS tracking collars and accelerometers for rangeland livestock production research. Transl. Anim. Sci. 2, 81–88. https://doi:10.1093/tas/txx006
Bailey, D. W. (2004). Management Strategies for Optimal Grazing Distribution and Use of Arid Rangelands. J. Anim. Sci. 82, E147–E153.
Roath, L. R., and Krueger, W. C. (1982a). Cattle grazing and behavior on a forested range. J. Range Manage. 35, 332–338. https://doi:10.2307/3898312
Bailey, D. W., VanWagoner, H. C., and Weinmeister, R. (2006). Individual animal selection has the potential to improve uniformity of grazing on foothill rangeland. Rangeland Ecol. Manage. 59, 351–358. https://doi:10.2111/04-165R2.1
Bailey, D. W., Lunt, S., Lipka, A., Thomas, M. G., Medrano, J. F., Cánovas, A., et al. (2015). Genetic influences on cattle grazing distribution: association of genetic markers with terrain use in cattle. Rangeland Ecol. Manage. 68, 142–149. https://doi:10.1016/j.rama.2015.02.001
Bailey, D. W., Kress, D. D., Anderson, D. C., Boss, D. L., and Miller, E. T. (2001). Relationship between terrain use and performance of beef cows grazing foothill rangeland. J. Anim. Sci. 79, 1883–1891. https://doi:10.2527/2001.7971883x
Pierce, C. F., Speidel, S. E., Coleman, S. J., Enns, R. M., Bailey, D. W., Medrano, J. F., et al. (2020). Genome-wide association studies of beef cow terrain-use traits using Bayesian multiple-SNP regression. Livestock Sci. 232:103900. https://doi:10.1016/j.livsci.2019.103900
Qazi Mudassar Ilyas and Muneer Ahmad, Volume 2020, Article ID 6660733, 12 pages https://doi.org/10.1155/2020/6660733
D. B. Lindenmayer, W. Blanchard, M. Crane, D. Michael, and C. Sato, “Biodiversity benefits of vegetation restoration are undermined by livestock grazing,” Restoration Ecology, vol. 26, no. 6, pp. 1157–1164, 2018. https://doi.org/10.1111/rec.12676
Wolfger, B., Jones, B. W., Orsel, K., and Bewley, J. M. (2017). Technical note: evaluation of an ear-attached real-time location monitoring system. J. Dairy Sci. 100, 2219–2224. https://doi:10.3168/jds.2016-11527
Sanchez-Iborra, R., Sanchez-Gomez, J., Ballesta-Viñas, J., Cano, M.-D., and Skarmeta, A. (2018). Performance Evaluation of LoRa Considering Scenario Conditions. Sensors 18:772. doi:10.3390/s18030772
Habib ur Rehman, M., Jayaraman, P. P., Malik, S. U. R., Khan, A. U. R., and Medhat Gaber, M. (2017). Rededge: a novel architecture for big data processing in mobile edge computing environments. J. Sensor Actuator Netw. 6:17. https://doi:10.3390/jsan6030017
Cheng, Y., Jiang, P., and Peng, Y. (2014). “Increasing big data front-end processing efficiency via locality sensitive bloom filter for elderly healthcare,” in 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD), 1–8. doi: 10.1109/CIBD.2014.7011524
García, R., Aguilar, J., Toro, M., Pinto, A., and Rodríguez, P. (2020). A systematic literature review on the use of machine learning in precision livestock farming. Comput. Electron. Agric. 179:105826. https://doi:10.1016/j.compag.2020.105826
Hu, W., Gao, Y., Ha, K., Wang, J., Amos, B., Chen, Z., et al. (2016). “Quantifying the impact of edge computing on mobile applications,” in Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems (New York, NY), 1–8.
https://doi.org/10.1145/2967360.2967369
Barwick, J., Lamb, D. W., Dobos, R., Welch, M., and Trotter, M. (2018). Categorising sheep activity using a tri-axial accelerometer. Comput. Electron. Agric. 145, 289–297. https://doi:10.1016/j.compag.2018.01.007