CNN-LSTM-POT-Based Anomaly Detection for Smart Greenhouse Sensor Data: A Real-Time Edge Deployment Approach

Abstract

Traditional agricultural greenhouse environmental monitoring systems often lack effective anomaly detection mechanisms, which can lead to inaccurate environmental regulation and negatively affect plant growth. To address this issue, this paper proposes a greenhouse monitoring system integrating Zigbee and 4G communication technologies, combined with a CNN-LSTM-POT anomaly detection algorithm. The system employs a Convolutional Neural Network (CNN) to extract local spatial features from multi-source sensor data and a Long Short-Term Memory (LSTM) network to model long-term temporal dependencies. To accurately identify anomalies, the Peaks Over Threshold (POT) method from extreme value theory is applied to prediction residuals, enabling adaptive dynamic threshold determination. Experimental results show that the proposed algorithm substantially improves anomaly detection precision, prevents erroneous data from disrupting greenhouse control decisions and reduces the volume of data transmitted to the cloud platform, thereby lowering computational overhead. This work provides a reliable and efficient solution for data monitoring and precise environmental control in smart agricultural greenhouses.