THE APPLICATION OF DRONE TECHNOLOGY AND IMAGE ANALYSIS FOR MONITORING GRAZING PATTERNS AND RANGELAND CAPACITY IN CATTLE FARMING
Article Sidebar
-
Drone Technology, Image Analysis, Grazing Patterns, Precision Livestock Farming, Rangeland Capacity
Abstract
The increasing demand for sustainable cattle farming and the pressure on rangeland resources have highlighted the need for efficient monitoring of grazing patterns and land carrying capacity. Traditional methods of monitoring rely on manual field surveys, which are labor-intensive and have limited coverage. Recent advancements in drone technology and image analysis present new opportunities for data-driven decision-making in livestock and rangeland management. This study explores the use of drone technology combined with image analysis techniques to monitor grazing patterns and assess rangeland capacity. A research and development design was employed, with drones capturing high-resolution aerial imagery of grazing areas at regular intervals. Image analysis techniques, including vegetation index extraction and spatial pattern analysis, were used to assess grazing intensity, vegetation cover, and biomass distribution. Data from the drone-based imagery were validated through ground observations and rangeland productivity records. The results show that drone-derived imagery accurately captured spatial variations in grazing behavior and vegetation condition, allowing for precise mapping of grazing zones and reliable estimates of rangeland carrying capacity. Compared to traditional methods, the drone-based approach was more efficient, offered greater spatial accuracy, and reduced the need for field surveys. In conclusion, integrating drone technology and image analysis offers a scalable solution for sustainable rangeland and livestock management.
Full text article
References
Abbas, S., Akhter, A. M., Mushtaque, M., Usama, R. M., Rasool, A., Umar, M., Mahmood, M. U., & Malik, M. (2025). Enhancing vegetation cover growth through water spreading bunds and dry afforestation in arid rangelands: Integrating remote sensing indicators with field insights. Science of The Total Environment, 978, 179382. https://doi.org/10.1016/j.scitotenv.2025.179382
Adar, S., Paz-Kagan, T., Argaman, E., Dubinin, M. (Vladislav), & Sternberg, M. (2024). Identifying climatic drivers of forage quantity and quality in Mediterranean rangelands using remote sensing. Science of The Total Environment, 957, 177797. https://doi.org/10.1016/j.scitotenv.2024.177797
Amineh, R., Dong, Z., Li, F., & Panero, M. (2024). Technologies for Soil and Water Quality Monitoring, and Their Role in Precision Agriculture. In M. A. Abraham (Ed.), Encyclopedia of Sustainable Technologies (Second Edition) (pp. 623–634). Elsevier. https://doi.org/10.1016/B978-0-323-90386-8.00104-2
Das, S., Dutta, S., Roy Choudhury, M., Garai, S., Mukherjee, S., Sengupta, S., Jana, S., Dey, S., Dhar, A., Dutta, S., & Awasthi, A. (2025). Regenerating rural soil and ecosystems: A 15-year systematic review of emerging methods and technologies. Science of The Total Environment, 990, 179926. https://doi.org/10.1016/j.scitotenv.2025.179926
Dolman, M. R., Kolarik, N. E., Caughlin, T. T., Brandt, J. S., Som Castellano, R. L., & Cattau, M. E. (2025). Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification. Remote Sensing Applications: Society and Environment, 37, 101472. https://doi.org/10.1016/j.rsase.2025.101472
Fleming, P. A. (2025). All that glitters – Review of solar facility impacts on fauna. Renewable and Sustainable Energy Reviews, 224, 115995. https://doi.org/10.1016/j.rser.2025.115995
Ghavi Hossein-Zadeh, N. (2025). Artificial intelligence in veterinary and animal science: Applications, challenges, and future prospects. Computers and Electronics in Agriculture, 235, 110395. https://doi.org/10.1016/j.compag.2025.110395
Halder, S., Rafiqul Islam, M., Mamun, Q., Mahboubi, A., Walsh, P., & Zahidul Islam, M. (2025). A comprehensive survey on AI-enabled secure social industrial Internet of Things in the agri-food supply chain. Smart Agricultural Technology, 11, 100902. https://doi.org/10.1016/j.atech.2025.100902
Harkort, L., Okujeni, A., Amputu, V., Mahler, J., Nill, L., Pflugmacher, D., Röder, A., & Hostert, P. (2025). Mapping fractional vegetation cover in Sub-Saharan rangelands using phenological feature spaces. Remote Sensing of Environment, 319, 114646. https://doi.org/10.1016/j.rse.2025.114646
Houinato, R. K., Idohou, R., Kakaï, R. L. G., & Brostaux, Y. (2026). A comprehensive analysis of the use of modelling and remote sensing techniques for monitoring and managing rangelands. Trees, Forests and People, 23, 101102. https://doi.org/10.1016/j.tfp.2025.101102
Juillard, L. Q., Ashby, L. M., McEvoy, T. J., & Ramp, D. (2025). Thermoregulation strategies differ for large macropods during high temperatures. Journal of Thermal Biology, 132, 104245. https://doi.org/10.1016/j.jtherbio.2025.104245
Kariminejad, N., Ahmadi, A., Garajeh, M. K., Hosseinalizadeh, M., & Pourghasemi, H. R. (2024). Chapter 14—Soil erosion analysis based on UAV and SPOT-6 satellite images. In H. R. Pourghasemi & N. Kariminejad (Eds.), Advanced Tools for Studying Soil Erosion Processes (pp. 243–255). Elsevier. https://doi.org/10.1016/B978-0-443-22262-7.00018-7
Koçakl?, K., Renschler, C. S., Flanagan, D. C., McGehee, R. P., Harbor, J. M., Ar?kan, B., & Özcan, O. (2025). Modeling archaeological excavation-induced soil erosion in cultural heritage landscapes: A QGeoWEPP-based assessment at the Resulo?lu Mound, Türkiye. Geoderma Regional, 43, e01018. https://doi.org/10.1016/j.geodrs.2025.e01018
Kodl, G., Streeter, R., Cutler, N., & Bolch, T. (2024). Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery. Remote Sensing of Environment, 301, 113935. https://doi.org/10.1016/j.rse.2023.113935
Kumar, A., Singh, S., Gupta, A. K., & Rani, K. (2025). Ecorestoration of degraded coal mines in India, present status and way forward for sustainable land management: A systematic review. Environmental and Sustainability Indicators, 28, 100872. https://doi.org/10.1016/j.indic.2025.100872
Mukhopadhyay, D., & Mishra, G. (2024). Chapter 24—Monitoring land degradation and desertification using the state-of-the-art methods and remote sensing data. In S. Chandra Pal, U. Chatterjee, & R. Chakrabortty (Eds.), Modern Cartography Series (Vol. 12, pp. 627–654). Academic Press. https://doi.org/10.1016/B978-0-443-23890-1.00024-4
Müllerová, J., Bartaloš, T., Gago, X., Kent, R., Michez, A., Mokroš, M., Mücher, S., & Paulus, G. (2023). Chapter 4—Vegetation mapping and monitoring by unmanned aerial systems (UAS)—Current state and perspectives. In S. Manfreda & E. Ben Dor (Eds.), Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments (pp. 93–124). Elsevier. https://doi.org/10.1016/B978-0-323-85283-8.00008-4
Nduko, J. M., Muthoka, S. K., & Ogendo, J. O. (2026). Transforming Sub-Saharan African food systems for food and nutrition security. In P. Alexander (Ed.), Encyclopedia of Agriculture and Food Systems (Third Edition) (pp. 474–508). Academic Press. https://doi.org/10.1016/B978-0-443-15976-3.00109-4
Nsabiyeze, A., Zhang, M., Li, J., Zhao, Q., & Zhang, X. (2025). Precision livestock farming for climate-resilient livestock management: A review of real-time monitoring and decision support systems. Journal of Cleaner Production, 524, 146454. https://doi.org/10.1016/j.jclepro.2025.146454
Nyamuryekung’e, S. (2024). Transforming ranching: Precision livestock management in the Internet of Things era. Rangelands, 46(1), 13–22. https://doi.org/10.1016/j.rala.2023.10.002
Ocholla, I. A., Pellikka, P., Karanja, F. N., Vuorinne, I., Odipo, V., & Heiskanen, J. (2024). Livestock detection in African rangelands: Potential of high-resolution remote sensing data. Remote Sensing Applications: Society and Environment, 33, 101139. https://doi.org/10.1016/j.rsase.2024.101139
Rapiya, M., Ngcoliso, N., Qabaqaba, M., Truter, W., & Ramoelo, A. (2026). The potential of Sentinel-1 for monitoring forage productivity in Rangeland Ecosystems: A review. Journal of Arid Environments, 232, 105494. https://doi.org/10.1016/j.jaridenv.2025.105494
Schmidt, H. E., Osorio Leyton, J. M., Popescu, S. C., Noa Yarasca, E., Sarkar, S., & Wilcox, B. P. (2024). Connecting the Dots: How Ecohydrological Connectivity Can Support Remote Sensing and Modeling to Inform Management of Woody Plant Encroachment. Rangeland Ecology & Management, 95, 84–99. https://doi.org/10.1016/j.rama.2024.05.001
Shahi, T. B., Balasubramaniam, T., Sabir, K., & Nayak, R. (2025). Pasture monitoring using remote sensing and machine learning: A review of methods and applications. Remote Sensing Applications: Society and Environment, 37, 101459. https://doi.org/10.1016/j.rsase.2025.101459
Uniyal, S., Bankhwal, P., & Panwar, S. (2026). Chapter eleven—Integration of geospatial technology and machine learning for livestock management. In D. Singh, K. Chaurasia, & G. Yasmin (Eds.), Agricultural Insights from Space (pp. 257–273). Academic Press. https://doi.org/10.1016/B978-0-443-34113-7.00017-1
Walsh, S. J., Laso, F., & Giefer, M. (2026). 9.08—Animals and Land Cover/Land Use Change: A Remote Sensing—Galapagos Islands Assessment. In S. Liang (Ed.), Comprehensive Remote Sensing (Second Edition) (pp. 160–190). Elsevier. https://doi.org/10.1016/B978-0-443-13220-9.00004-4
Zhang, T., Xuan, C., Tang, Z., Gao, X., Liu, S., Song, Y., Cao, Q., & Cheng, F. (2026). Cross-task collaborative learning for aboveground biomass estimation from UAV hyperspectral imagery in sample-scarce desert rangelands. Computers and Electronics in Agriculture, 243, 111386. https://doi.org/10.1016/j.compag.2025.111386
Zhao, B., Hiller, J., Awada, T., Wardlow, B., Erickson, G., & Shi, Y. (2025). Forage biomass estimation using UAV-based remote sensing and machine learning: A tool for assessing management practices. Ecological Informatics, 90, 103361. https://doi.org/10.1016/j.ecoinf.2025.103361
Zhao, J., Zhu, Y., Wang, L., Li, Z., Shi, Z., Yang, X., & Yahdjian, L. (2025). Plant invasion risk assessment in Argentina’s arid and semi-arid rangelands. Journal of Environmental Management, 377, 124648. https://doi.org/10.1016/j.jenvman.2025.124648
Authors
Copyright (c) 2025 Ricardo Figueroa, Carlos Chavarria, Luis Ramirez
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
