Humidity Sensor Drift Detection and Correction Based on a Kalman Filter with an Artificial Neural Network for Commercial Cultivation of Tropical Orchids

Polymer dielectric-based humidity sensors used in the orchid greenhouse monitoring system usually work improperly after continuously being used in a high humid condition for some time (e.g., after eight months). This problem, called sensor drift, has been broadly observed. This paper proposes a simple data-driven technique based on a Kalman filter with an artificial neural network to detect the drift and correct data. The combination of two proposed measures based on the \(L^1\) distance and the cosine similarity is used to determine the sensor’s status, which is later used to adjust the Kalman gain accordingly. That is, when the sensor malfunctions, the gain is biased toward the prediction. When the sensor is in the normal status, the gain is biased toward the measurement. When the sensor drift is detected, the gain varies in between the prediction and the measurement. The experimental results show that the proposed method could reduce the accumulated mean absolute deviation by approximately 55.66%.