Crop Photosynthetic Performance Monitoring Based on a Combined System of Measured and Modelled Chloroplast Electron Transport Rate in Greenhouse Tomato

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.