![]() Research questions to be answered 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, which is updated as the crop grows and develops, will be used for automatic control of greenhouse climate settings, following a model predictive control strategy. Building on previous work, we will use and develop deep-learning methods to obtain morphological, reflectance, and physiological traits (such as photosynthesis, transpiration, pigmentation). The focus is therefore on estimating plant traits from raw sensor data. ![]() The climate sensors measure the desired quantities directly. 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. de Visser) and right: the interface of greenhouse climate model Kaspro (G.J. Left: example of a functional-structural tomato plant (P.H.B. 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 feed back on the resources driving that growth, in terms of increased shading and depletion of nutrients and water. The core of the VTC is based on the concepts of functional-structural plant (FSP) modelling, which simulates individual plants and their functioning (such as leaf photosynthesis) as well as their 3D architectural development. The VTC is the first step towards this ambitious goal. The VTC will work towards production of greenhouse tomatoes with a minimum of resources as well as demand driven by consumer preferences, and thus will realise a feasible cultivation and production system. This will reduce costs and reliance on inputs, making tomato growing more economical. ![]() The ultimate goal of this digital twin is to increase resource use efficiency of greenhouse tomato systems, resulting in lower dependence on external energy inputs, a further reduction in CO2 emissions and optimization of water use and fertigation. Based on the model predictions, crop management strategy can be adjusted, and improved plant traits can be identified.ĭevelopment and testing of the VTC will be done in regular contact with stakeholders. Simulations are based on real-time measurements of tomato plants and their growing conditions. The model simulates crop yield, CO 2 uptake, and use of nutrients, energy and water, as well as profit and environmental impact. The VTC project aims to develop a simulation model that predicts tomato plant growth in 3D. The Digital twin project ‘Virtual Tomato Crops (VTC)’ is a 3-year project within Wageningen University & Research, and one of the three investment themes of the WUR Strategic Plan 2019-2022. (G×E×M = interaction between genotype, environment and management). Concept map of the Virtual Tomato Crops digital twin. ![]()
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