Deliverable 11. An on-line control algorithm for greenhouse and fertigation computers (AUA)
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Summary
An optimisation-based methodology for irrigation control and nutrient supply is developed using common measurements of greenhouse climate. Although the method is based on drain water measurements, it can be adapted to other sensors as well, i.e. tensiometers or other soil moisture sensors for soil fertigation management. The high level code appears in tables 1 and 2.
Contemporary greenhouse operations require precise control of irrigation and nutrient supply in order to optimise crop growth and minimise cost and pollution due to effluents. Moreover, in Mediterranean countries and elsewhere, there is a need to minimise water waste due to seasonal shortages. In modern greenhouses, nutrient supply is computer controlled and based on measuring salinity and compensating deficiencies using a mix of clean water and two or more stock nutrient solutions. The process of applying this solution to the crop presents several control problems such as time delays and seasonal variations due to plant growth. The monitoring of the process may have a minimal time delay, as in water content measurement in soil or substrates, or an extremely long time delay, as in drain flow measurement. Several solutions to the nutrient supply problem have been proposed such as direct feedback control of drain water flow in both closed and open growing systems using flow measurement of the drain water. Gieling et al. (2000) presents a design for a water supply controller using system identification but the proposed controller performs well only when a feedforward element is added in the control loop, in order to estimate water uptake as a function of global radiation. On the other hand, progress has been achieved in model prediction of crop irrigation needs. Usually, a transpiration model (Stanghellini, 1987) is used that predicts plant transpiration based on ambient conditions of temperature, solar radiation, CO2 concentration and vapor saturation deficit. Approaches based on such experimental models lead to open loop control of water and nutrient supply, using model estimation of crop needs (Hamer, 1997). These growth-based models, whether complex multivariable non-linear systems or reduced order models, usually suffer from an inaccurate estimate of the transpiring surface (Leaf Area Index), when used without an error correction scheme.
This work proposes a hybrid approach, where a simplified crop transpiration model is used to predict the necessary supply of water. At the same time, drain water flow from the crop is measured using an appropriate flow sensor. Using the error between drain measurement and the model estimate, the coefficients of the model are adapted iteratively. The adaptation process is continuous so that the model accounts for temporal variation of load (i.e. radiation) while it is also adapted for seasonal variations of crop growth.