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

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 CO(2) assimilation rate (A(L), µmol CO(2) 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 A(L) and light-quantum yield of photosystem II (Φ(2)), we defined three objectives: (1) to explore the relationship between climate variables and A(L), as well as Φ(2); (2) create a simple and reliable method for real‐time prediction of A(L) with continuously Φ(2) measurements; and (3) calibrate parameters to predict chloroplast electron transport rate as input in A(L) modelling. Due to practical obstacles in measuring CO(2) gas-exchange in commercial production, we explored a method to predict A(L) by measuring Φ(2) of leaves in a commercial hydroponic greenhouse tomato crop (“Pureza”). We calculated A(L) 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 A(L) with large uncertainty (R(2) 0.64; RMSE 2.21), while using Φ(2) as input to Model II could be used to improve the prediction accuracy of A(L) (R(2) 0.71; RMSE 1.98). Our results suggests that (1) Φ(2) 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 A(L). 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.