Nonlinear Observability Analysis and Joint State and Parameter Estimation in a Lettuce Greenhouse using Ensemble Kalman Filtering

Estimating crop states accurately and reliably through climate sensing is a promising alternative for high tech crop sensing investments. This paper explores and demonstrates the applicability of joint crop parameter and crop state estimation through indoor climate monitoring in a lettuce greenhouse system via Ensemble Kalman filtering combined with a nonlinear observability analysis via the empirical observability Gramian. The observability analysis indicated that crop dry-weight can be estimated from the indoor CO2 concentration, temperature and humidity, while simultaneously the parameter that represents the light use efficiency can be estimated and even corrected for. These outcomes were confirmed by a simulation study. This showed that the method is robust against one level of process and measurement noise, and a 50 % error in the model parameter that represents the light use efficiency. More precisely, it has been shown that improvements of 50 % of the dry-weight estimation in terms of average root mean squared error can be achieved with respect to the case where no Ensemble Kalman filtering and parameter update is used. Copyright (C) 2022 The Authors.