Anomaly detection in smart agricultural control systems using semi-supervised bidirectional encoding

Anomaly detection is vital for maintaining the reliability of smart agricultural control systems, especially under conditions where sensor faults may disrupt automated irrigation and environmental monitoring. To address the limitations of traditional manual inspections and purely supervised methods, this study proposes DE-TabNet—a semi-supervised anomaly detection model based on bidirectional encoding. The model integrates temporal sequence learning via a BiGRU-VAE network and feature dependency extraction through a sparse autoencoder, forming an unsupervised feature fusion network. This representation is subsequently fine-tuned using a limited set of labelled data through a TabNet-based supervised module. Experimental evaluations on a real-world smart drip irrigation dataset demonstrated that DE-TabNet significantly outperforms state-of-the-art methods, including the DAGMM, iForest, VAE-LSTM, OCSVM, and Semi-TabNet methods. Specifically, DE-TabNet achieved up to 86.9% accuracy and an 80.8% F1 score, corresponding to improvements of 11.6% and 3.82%, respectively, over those of the baseline models. Additional validation on public datasets confirmed the model’s strong generalization ability, highlighting its practical value for robust anomaly detection in agricultural and other time series sensor systems.