Improving climate monitoring in greenhouse cultivation via model based filtering. (May 2019)

• Record Type: Journal Article
• Authors: van Mourik, Simon; van Beveren, Peter J.M.; López-Cruz, Irineo L.; van Henten, Eldert J.
• Is Part Of: Biosystems engineering. Volume 181(2019)
• Page Start: 40
• Page End: 51
• Publication Date: 2019-05
• Subjects: Protected horticulture -- Climate monitoring -- Sensitivity analysis -- Extended Kalman filter -- Unscented Kalman filter -- Moving average filter; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2019.03.001
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : The possibility of improving the accuracy of climate monitoring in greenhouse cultivation by way of model based filtering was explored. The focus was on estimating the average climate inside a greenhouse compartment. Starting point was employing an extended Kalman filter (EKF), combined with a greenhouse climate differential equation model. In two different greenhouses (A and B), temperature and humidity were monitored with a 5-min sampling resolution with a sensor grid. The available data sets spanned 1 and 0.5 years. With the average over all sensors as reference signal, the root mean squared errors (RMSEs) of the unfiltered signals (coming from single sensors) were 0.43 °C and 0.48 g m −3 for greenhouse A, and 0.80 °C and 0.64 g m −3 for greenhouse B. The filter was compared with a moving average (MA) filter, and an unscented Kalman filter (UKF). Overall, monitoring accuracy was not improved by any of the filters, and in most cases it deteriorated. Performance was strongly linked to the choice of filter, where the EKF outperformed the other filters by a considerable difference. The violations on the assumptions of whiteness and normality of the noise were severe but had a moderate effect on the RMSEs (0.11 °C and 0.10 g m −3 for greenhouse A). A clear link was found between model accuracy and monitoring accuracy. A 10–15 fold decrease of state errors was associated with an RMSE reduction down to 0.1 °C and 0.1 g m −3, the expected equivalent of increasing the number of climate sensors from 1 to 25. Highlights: A novel methodology for assessing data filtering methods was developed. Three data filters were tested in two different greenhouses for in total 1.5 years. None of the filters improved climate monitoring accuracy. Bias, non-Gaussianity, and auto-correlation had a moderate effect on performance. Increasing model accuracy has the same effect as increasing the number of sensors.