ReviewSensors (Basel, Switzerland)2026
Space-Air-Ground Integrated Networks for Smart Agriculture and Smart Breeding: A Review of SDN/NFV-Enabled Intelligent Resource Management.
Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Smart agriculture and smart breeding are evolving from isolated sensing toward geographically distributed, long-term, data-driven closed-loop management. Dispersed farms and breeding sites, UAV and remote-sensing phenotyping, real-time field control, edge inference, and digital-twin synchronization impose heterogeneous demands on coverage, latency, bandwidth, reliability, computing, and energy. Space-air-ground integrated networks (SAGINs) combine satellites, unmanned aerial vehicles/high-altitude platforms, and terrestrial networks for wide-area coverage and elastic access, but introduce dynamic topologies, heterogeneous multi-domain resources, and complex cross-layer orchestration. Focusing on intelligent SAGIN resource management enabled by software-defined networking (SDN) and network function virtualization (NFV), this review examines controller placement, NFV/service function chain orchestration, SDN/NFV cooperation, learning-driven optimization, and security mechanisms. Representative studies are compared by objectives, decision variables, mechanisms, applicability boundaries, and engineering costs. These mechanisms are connected to smart agriculture and smart breeding through ubiquitous connectivity, edge computing, task offloading, phenotyping, and digital-twin closed loops, while distinguishing experimentally supported agricultural evidence from SAGIN-oriented architectural inference. Future research should move beyond single-metric optimization toward joint evaluation of state freshness, decision latency, reconfiguration cost, learning and security overhead, and agronomic outcomes to improve deployability, robustness, and verifiability of cross-domain resource management under real deployment conditions across heterogeneous agricultural environments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.