Smart sensing and anomaly detection for microalgae culture based on LoRaWAN sensors and LSTM autoencoder

The increasing demand for sustainable aquaculture solutions has accelerated interest in microalgae as an alternative fish feed source. This study presents a novel integration of Long-Range Wide Area Network (LoRaWAN) sensors and a Long Short-Term Memory (LSTM) autoencoder model to enable real-time monitoring and anomaly detection in 300L outdoor microalgae cultivation. A solar-powered Internet of Things (IoT) sensor and LoRaWAN system were developed to monitor key water quality parameters such as pH, water temperature, electrical conductivity (EC), and oxidation-reduction potential (ORP). ORP was identified as a sensitive alternative for monitoring microalgae growth dynamics and contamination. The LSTM autoencoder, trained on normal ORP data, effectively distinguished abnormal culture patterns based on reconstruction errors, achieving high precision (1.000), recall (0.944), F1-score (0.971), and AUC-ROC (1.000). The addition of a moving average filter improved data stability for model training of filtered sensor signals. A bespoke visual reconstruction error heatmap further simplified anomaly interpretation for end-users. This framework advances smart aquaculture by enabling timely intervention, supporting sustainable microalgae production, and aligning with global food security and environmental sustainability goals.