Rick van de Zedde - Pieter de Visser

Rick van de Zedde – Pieter de Visser

Wageningen University & Research

Rick van de Zedde is project manager of the new phenotyping facility NPEC @ WUR, next to that he is senior scientist/ business developer Phenomics and Automation at the Wageningen Plant Science Group where he has worked at WUR since 2004. His background is in Artificial Intelligence with a focus on imaging and robotics. Netherlands Plant Eco-phenotyping Centre (NPEC) is an integrated, national research facility housed by Wageningen University & Research and Utrecht University and is co-funded by The Netherlands Organisation for Scientific Research (NWO). More info Pieter de Visser is a senior scientist in the team Crop Physiology of the business unit Greenhouse Horticulture at the Wageningen Plant Science Group. Since 2001 he developed into an expert in novel crop simulation models, in particular 3D crop models on architecture and physiology and self-learning models that are linked to plant sensors. The models are applied in decision support systems for horticulture with the focus on crop production, energy use and climate related crop diseases.

Horticulture – where Digital Twins, virtual plants models and AI meet

Rick van de Zedde & Pieter de Visser, Wageningen University & Research. At Wageningen University & Research from April 2021 onwards a digital twin (DT) will be operational. The DT will digitally represent a tomato crop of individual, virtual plants in their local greenhouse environment, and grown simultaneously. The DT will feature real-time updating of plant parameters and environmental variables based on high-tech sensor equipment available in the Netherlands Plant Eco-phenotyping Centre (NPEC) facilities. In the DT, each tomato plant in the crop will be modelled in 3D integrating a set of traits that correspond to model parameters. Thereby, the DT enables us to predict crop response (growth, development and production) to greenhouse and management conditions that affect production efficiency; light intensity and quality, CO2 dosing, nutrient availability and leaf pruning. Thus, the DT can support greenhouse management in real-time. This will be the first ever 3D simulation model of individual plants growing in greenhouses that get updated by sensor data and that delivers updated predictions as the real plants grow. In that sense it is a true digital twin, which does not yet exist for plants. This is an important extension of the plant and greenhouse modelling that exists today. As well, the DT allows for hypothesis testing and in silico experiments. As a scientific aim, we will develop and study novel methods on e.g. deep learning for processing of sensor data to transform the raw data to plant traits. Moreover, novel methods will be dealt with on Bayesian inference of state parameters of the plant and greenhouse models, allowing efficient model updating and optimizing the accuracy of the model predictions. Scientific issues that will be addressed include processing the high-dimensional sensor data, further refinement of the plant and greenhouse model, estimating the model parameters and using that to make decision about control. Furthermore, with our systematic and process-based approach we can analyse the whole system, and investigate possible bottlenecks in sensing, modelling, and control, and in what way or to which extent they hinder optimal performance. For example by simulating how small errors in each of the modules propagate through the system, and influence the performance. Subsequent investigation can then be targeted efficiently to find remedies, e.g., by improved sensing equipment or algorithms, improved model accuracy, or by selecting a different type of controller. The constructed DT can be used to predict growth and development of tomato plants in response to real-time environmental factors and management decisions. This allows for more informed decisions regarding the agronomic management in commercial practice, as well as the selection pressure applied by breeders to specific traits. More info: