ICT-based status monitoring and anomaly detection techniques of smart greenhouse components: A review

다국어 초록 (Multilingual Abstract)

The advancement of Information and Communication Technologies (ICT) has transformed greenhouse agriculture by improving crop productivity, resource efficiency, and system sustainability. The objective of this review was to provide an overview of recent advancements in ICT-enabled environmental status monitoring and anomaly detection technologies implemented in smart greenhouse components. Key technologies include Internet of Thing (IoT), Wireless Sensor Network (WSN), Machine Learning (ML)-based predictive maintenance, digital twin, and performance management.
Anomaly detection approaches are analyzed across domains such as sensor fault, communication failure, and cybersecurity threat. A comparative evaluation highlights the evolution from conventional threshold-based methods to advanced ML techniques, including supervised, unsupervised, deep learning, and Edge Artificial Intelligence (AI).
ML-based systems report detection accuracies up to 97%, yet challenges remain regarding data integrity, computational overhead, scalability, and adversarial robustness. Future prospects emphasize AI-based self-healing mechanism, blockchain-integrated data pipeline, 6G-enabled communication, and hybrid anomaly detection models. The study concludes that resilient ICT architectures are essential for maintaining intelligent greenhouse functionality and enhancing food security under climate variability.