Abstract
Combining information of plant physiological processes with climate control systems can improve control accuracy in controlled environments as greenhouses and plant factories. Through that, resource optimization can be achieved. To predict the plant physiological processes and implement them in control actions of interest, a reliable monitoring system and a capable control system are needed. In this paper, we focused on the option to use real-time crop monitoring for precision climate control in greenhouses. For that, we studied the processes and external factors influencing leaf net CO2 assimilation rate (AL, µmol CO2 m-2 s-1) as possible variables of a plant performance indicator. While measured greenhouse environmental variables such as light, temperature, or humidity showed a direct relation between AL and light-quantum yield of photosystem II (Φ2), we defined three objectives: (1) to explore the relationship between climate variables and AL, as well as Φ2; (2) create a simple and reliable method for real‐time prediction of AL with continuously Φ2 measurements; and (3) calibrate parameters to predict chloroplast electron transport rate as input in AL modelling. Due to practical obstacles in measuring CO2 gas-exchange in commercial production, we explored a method to predict AL by measuring Φ2 of leaves in a commercial hydroponic greenhouse tomato crop (“Pureza”). We calculated AL with two different approaches based on either the negative exponential response model with simplified biochemical equations (marked as Model I) or the non-rectangular hyperbola full biochemical photosynthetic models (marked as Model II). Using Model I can only be used to predict AL with large uncertainty (R2 0.64; RMSE 2.21), while using Φ2 as input to Model II could be used to improve the prediction accuracy of AL (R2 0.71; RMSE 1.98). Our results suggests that (1) Φ2 light signals can be used to predict net photosynthesis rate with high accuracy; (2) a parameterized photosynthetic electron transport rate model is suitable predicting measured electron transport rate (J) and AL. The system can be used as decision support system (DSS) for plant and crop performance monitoring when leaf-dynamics are up-scaled to the plant or crop level.
Highlights
In modern greenhouses and plant factories plant cultivation is usually done with computerized environmental climate control
The results showed that the environmental variables can better explain the variation of AL than the variation in F2: 61.5% of the variation of AL could be assessed by Ta, VPD, and I, whereas only 50.2% of F2 variation can be assessed by environmental factors in our measurements AL is a better suited to evaluate plant responses to the environmental factors than F2
Net photosynthesis prediction in a tomato crop can be improved significant when on-line measurements with sensor systems and intelligent algorithms of models are combined to a so-called softsensor (De Koning, 2006; Körner, 2019)
Summary
In modern greenhouses and plant factories plant cultivation is usually done with computerized environmental climate control. Sensorbased monitoring and real-time model predictions strongly improved early warning and greenhouse climate control (Körner and Hansen, 2012; Mahlein, 2015; Körner, 2019), realtime crop monitoring still suffers from inadequate equipment and/or insufficiently model quality. The realization of softsensors (i.e. mathematical models using real-time sensor data) (De Koning, 2006) with deterministic explanatory models in greenhouse cultivation monitoring is still under development. Robust and simple sensors combined with models calibrated with data from laboratory experiments would be the most suitable approach to implement physiological based automatic control system in the greenhouse (Janka et al, 2013; Körner, 2019). A reliable system with both measured and modelled plant physiological parameters is needed
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