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Machine learning in NTN-assisted IoT: systematic insights into agriculture and cross-domain applications

artificial-intelligence
computer-networks-and-communications
emerging-technologies
internet-of-things

Abstract

Non-terrestrial networks (NTNs) integrating unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites with Internet of Things (IoT) devices are being increasingly applied in smart agriculture. NTN-assisted IoT supports precision farming through crop disease detection, soil and water management, environmental surveillance, and decision support. Machine learning (ML) methods, including supervised, unsupervised, reinforcement, and federated learning, enhance these applications by improving data fusion, resource allocation, prediction, and automation. However, most existing reviews remain domain-specific and overlook agriculture within the broader NTN-assisted IoT ecosystem, which also enables anomaly detection, healthcare, and environmental monitoring. To address this gap, this study conducted a systematic bibliometric and thematic analysis of ML-driven NTN-assisted IoT research. Twelve major clusters were identified, three of which were closely related to agriculture: (i) sustainability and smart farming, (ii) image processing and anomaly detection, and (iii) big data and cloud/fog-enabled applications. Emerging directions from low-frequency keywords include environmental risk management, air quality monitoring, wildfire detection, edge intelligence, digital twins, and explainable AI. These complement agricultural applications while extending the societal impact of NTN-assisted IoT. The paper provides a unified mapping of the field and highlights opportunities for advancing Agriculture 4.0 and 5.0 through cross-sectoral innovation.
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