Testing will be conducted at the lab or greenhouse scale, either at WUR or at producer organisations. The VTC is evaluated from an integrated and practical perspective and based on technical and economic performance. Furthermore, the digital twin can be used to virtually explore leaf pruning strategies, to test different greenhouse cover types, and to select superior crop traits. Research questions concern effects of model granularity on climate control advice, and the effect of daily crop status update on control performance in terms of light use efficiency. The output of the model will be used for automatic control of greenhouse climate settings. We will use and develop deep-learning methods to obtain morphological, reflectance, and physiological traits. The focus is therefore on estimating plant traits from sensor data. In real time, data on plant growth and growing conditions will be captured using the NPEC greenhouse facilities (Data from several sensors in the NPEC facilities, such as the multi-spectral 3D laser scanner, chlorophyll fluorescence camera, thermal camera and climate sensors, will be processed to estimate plant traits and climate conditions. The VTC will be continuously updated with data from the real twin a tomato crop growing in the greenhouse. The environmental variables driving plant growth and development will be simulated by a greenhouse module based on the Kaspro model. Crop behaviour is thus the result of individual plants using shared resources. The crucial property of FSP models is that growth and development of the plants feedback on the resources driving growth, in terms of increased shading and depletion of nutrients and water. All rights reserved.The Virtual Tomato Crops (VTC) model is based on the concepts of functional-structural plant modelling, which simulates individual plants and their functioning as well as their 3D architectural development. We recommend more research on the measurements of these variables and on the development of 2-D and 3-D gas diffusion models, since these do not require the diffusion pathway length in the stroma as predefined parameter.Ĭ(3) Diffusion Leaf anatomy Mesophyll conductance Mesophyll resistance Photosynthesis.Ĭopyright © 2015 Elsevier Ireland Ltd. Some variables (diffusion pathway length in stroma, diffusion coefficient of the stroma, curvature factors) substantially affected the predicted CO2 assimilation. Next, we conducted a similar analysis for assumed diffusive properties and curvature factors. We did a sensitivity analysis to assess how the rate of CO2 assimilation responds to changes in various leaf anatomical properties. There was generally a good agreement between the predicted and measured light and CO2 response curves. We parametrized the model by gas exchange, chlorophyll fluorescence and leaf anatomical measurements from three tomato cultivars. We combined a model that quantifies the diffusive resistance for CO2 using anatomical properties, a model that partitions this resistance and an extended version of the Farquhar-von Caemmerer-Berry model. Biochemical processes add or remove CO2 along its diffusion pathway through mesophyll. Leaf anatomical structures act as physical barriers for CO2 transport. The CO2 concentration near Rubisco and, therefore, the rate of CO2 assimilation, is influenced by both leaf anatomical factors and biochemical processes.
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