Abstract
In Korea, agricultural policy increasingly promotes high-efficiency digital agriculture; however, insufficient sensor reliability and data accuracy continue to limit the practical adoption of smart farm technologies. To address these limitations, this study aims to develop and field-validate an AI-based Prognostics and Health Management (PHM) framework for anomaly detection and remaining useful life (RUL) estimation of sensors and actuators in commercial smart farms. To collect smart farm data, we developed a switch voltage and current data acquisition system and selected problematic switches and environmental sensors in operating greenhouses as PHM targets. Using PHM techniques, we implemented mathematical and artificial intelligence (AI)-based anomaly detection and failure prediction algorithms. In experiments, sensor behavior was predicted with mathematical and AI models, achieving over 90% predictive accuracy compared with observations. Based on these predictions, thresholds were estimated and the remaining useful life (RUL) of sensors was predicted up to 80 h in advance. For switches, vibration, noise, and voltage data were collected to detect anomalies. Actuator anomaly detection employed thresholds derived from statistical indicators and machine learning; a hybrid approach combining interquartile range, Z-score, and Isolation Forest leveraged the strengths of both paradigms to provide robust and adaptive detection. Deviation features were then combined with environmental factors to construct an RUL model, and the remaining life of devices in operation was estimated using a k-nearest neighbors approach. In field validation, the lifetime of four switches was predicted, yielding a mean RUL of 1655 d. Finally, we implemented a web-based platform that enables farms to monitor and manage equipment health. Compared with prior studies, the key novelty of this work lies in integrating sensor-and-actuator PHM, providing real-field validation in operating greenhouses, and delivering an operational web platform that supports practical smart farm maintenance. By integrating these methods, the study aims to improve system efficiency, reduce energy consumption, and extend the operating life of smart farm components. We anticipate substantial benefits as the proposed approach is applied to smart farm equipment, enabling more reliable data acquisition and stable maintenance in practice.