A scalable, end-to-end IoT and remote sensing platform for precision rangeland and livestock management

Authors: BAKIR, MEHMET - New Mexico State University; PEREA, ANDRES - New Mexico State University; FUNK, MICAH - New Mexico State University; RAHMAN, SAJIDUR - New Mexico State University; SPETTER, MAXIMILIANO - New Mexico State University; Macon, Lara; COX, ANDREW - New Mexico State University; Estell, Richard; CAO, HUIPING - New Mexico State University; Cibils, Andres; Spiegal, Sheri; Bestelmeyer, Brandon; UTSUMI, SANTIAGO - New Mexico State University

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2026
Publication Date: 3/25/2026
Citation: Bakir, M.E., Perea, A.R., Funk, M., Rahman, S., Spetter, M.J., Macon, L.K., Cox, A., Estell, R.E., Cao, H., Cibils, A.F., Spiegal, S.A., Bestelmeyer, B.T., Utsumi, S.A. 2026. A scalable, end-to-end IoT and remote sensing platform for precision rangeland and livestock management. Computers and Electronics in Agriculture. 247:Article e111615. https://doi.org/10.1016/j.compag.2026.111615.
DOI: https://doi.org/10.1016/j.compag.2026.111615

Interpretive Summary: Beef cattle ranching is a major part of agricultural economies in the western United States. Livestock management on arid rangelands is challenging and labor intensive due to the difficulty monitoring animals in large pastures with rugged terrain. Precision ranching may provide opportunities to assist ranchers with livestock management by reducing labor and fuel costs through the use of Internet of Things (IoT) devices and remote sensing technologies. However, the expansive nature and harsh conditions typically encountered by producers in arid regions combined with constraints due to low connectivity creates challenges not encountered in most agricultural systems. We developed a precision ranching platform combining IoT sensors, LoRaWAN networks, satellite imagery and advanced data-driven analytics to assist livestock management in large pastures. The platform collects, processes and reports data from sensors and devices distributed over large areas. Cattle equipped with tracking devices allow animal location, movement and activities to be collected in real time so that ranchers can locate cattle quickly and monitor behaviors associated with animal health and calving. Water level sensors and rain guages allow ranchers to check water tanks and assess forage resources remotely. This system has been deployed and tested on a dozen beef cattle ranches in four states spanning over half a million acres and successfully overcame limitations (power supply and unreliable connectivity) typical of remote regions. This platform has potential to increase operational efficiency, reduce labor and costs, and enhance animal welfare on ranches across the western United States.

Technical Abstract: Efficient livestock grazing management across vast, arid rangelands is challenging due to resource scarcity, environmental variability, and labor-intensive monitoring. Precision agriculture has successfully integrated Internet of Things (IoT) devices and remote sensing technologies for farm management; however, precision ranching, the counterpart for extensive livestock production on rangelands, remains underdeveloped. This gap is largely due to the large spatial extent and harshness of these environments, which limit connectivity, impose strict power constraints on distributed IoT devices, and generate high-volume, heterogeneous data streams that are difficult to collect, transmit, and analyze at scale. To address this gap, this study aimed to design and operationally validate a scalable, modular, and vendor-agnostic system architecture for precision ranching that integrates IoT sensors, satellite imagery, and artificial intelligence (AI)-driven analytics to support rangeland and livestock management across large-scale ranching operations. The resulting platform employs a multi-tier architecture designed to collect, process, and present data from distributed IoT devices and satellite imagery efficiently. Its core functionalities include near real-time livestock tracking, vegetation assessment, and environmental monitoring. Predictive analytics are used to optimize resource allocation, detect potential animal health issues, assess environmental risks, and support day-to-day decision-making. By addressing the scalability and computational challenges inherent to extensive operations, the platform establishes a practical framework for data-driven and sustainable rangeland and livestock management. The system was deployed across beef cattle operations spanning more than half a million acres in four states. The platform demonstrated potential to improve operational efficiency, reduce labor and associated costs, and support animal welfare.