Experiment: Digital twin project ‘Virtual Tomato Crops’

The digital twin can be used to predict growth and development of tomato plants in response to real-time environmental factors and management decisions. This allows for better informed decisions regarding the agronomic management. Also, the digital twin can be used to inform tomato breeding by identifying key target traits. In the digital twin, each tomato plant in the crop is represented in 3D, integrating a set of traits that correspond to model parameters. Thereby, the twin enables users to predict crop responses (growth, development and production) to greenhouse and management conditions that affect production efficiency, light intensity and quality, CO₂ dosing, nutrient availability and leaf pruning.

The output of the model, which is updated as the real crop grows and develops, can be used for automatic control of greenhouse climate settings, following a model predictive control strategy. 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. 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.

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. This will reduce costs and reliance on inputs, making tomato growing more economical. The digital twin 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. The VTC project is the first step towards this ambitious goal.