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MSc thesis topic: Visualizing crop growth in a Digital Twin: combiningUAV data and a crop growth models

Understanding crop growth in the field is an important condition to improve food production. Large amounts of data are being acquired from in situ sensors, robots in the field or remote sensing from drones or satellites. Its challenging to combine this data in such a way that it results in relevant management information for farmers, growers, advisors and food companies.

A recent project on Virtual Tomatoes has shown that a Digital Twin can be the link between sensing observations from crops real-world and simulation of crop growth in the virtual world. Simulations are based on real-time measurements of plants traits and their growing conditions. Based on the model predictions, crop management strategy can be adjusted, and improved plant traits can be identified. This Digital Twin concept for crops was not tested yet for crops grown in the field like potato, maize or wheat. For that case natural variation in the field and the variation over the growing season can be measured through UAV based observations which are used as input for crop growth modelled. Nowadays open source versions of Digital Twins can be adopted to link measurements and models to create a virtual interactive digital twin of a cropping field.

Relevance to research/projects at GRS or other groups

In this thesis topic we want to bridge two GRS research themes, sensing and visualization, and evaluate how the digital twin concept can be adopted for applications related to food production and agricultural monitoring. Several UAV datasets are available both multispectral and LiDAR, and an open source Digital Twin platform can be adopted.

Objectives and Research questions

  • Prepare a design for a Digital Twin of an agricultural crop field representing main spatial and temporal variation and growth processes at 3D individual plant level
  • Implement and evaluate the design for a case study field adopting open source Digital Twin standards for a selected crop using available UAV and other sensor sources as real-world input.

Literature and information

Theme(s): Sensing & measuring, Modelling & visualisation