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Context and background

In the context of challenges such as climate change, scarcity of workforce, pressure from new pests and diseases, regulations concerning the use of pesticides, production of horticultural crops has become a difficult endeavour. There is a real need to develop new production systems, that overcome these problems. At the same time, enormous progress has been made recently at the frontiers of information science, artificial intelligence and sensor technology. 3D plant models representing plant architectural and physiological development in space and over time at different resolutions (scales) are now available, putting the creation of a horticultural digital twin within reach. Such a digital twin (i.e. a multi-scaled model able to update its parameters automatically) would be a powerful tool enabling us to rapidly optimize existing, and to propose novel, production systems in silico.
A digital twin consists in multiscale models with a multitude of parameters. The mater is how best to interconnect these models, and to reason simplifications at the scale of the digital twin. We therefore need to automatize the exploration of these different scales. This can be achieved thanks to a formal representation of the multi-dimensional landscape of scales and parameters through an ontology. The aim of this thesis is to navigate the ontology to determine what is relevant by comparing simulated with real data. The challenge is to carry out such a comparison by developing a method for automatically moving from one scale to another, without losing essential information.

What you will do

  • Characterizing the multidimensional landscape of scales and parameters: Inventory of photosynthesis and biomass production models (especially for tomato), characterize the key parameters to create an ontology describing the parameter landscape of each model.
  • Building the integration system: Define how to transfer data between ecophysiological models and scales, and represent them in the ontology for the tomato crop case. Exploit the information to describe how to use the output of one model in another.
  • Greenhouse trials: Define how to measure the environment and the plants at the desired level of detail for the model(s) under consideration, based on the results of the system (output from point 2).
  • Refining the integration system: Compare the experimental results with the integration system to improve the representation. A second set of experimental data may be used to validate the corrections made. Data analysis, parameterization, calibration and validation of the model

Generally, you will conduct a bibliographical comparison and an analysis of the code of various models, then propose a (re)coding of the models (Functional-Structural Plant Model, Process-Based Model, or 3D model of the greenhouse) based on an ontology to be created. This work will be followed by a sensitivity analysis, optimization studies, simulation of scenarios and validation using the platforms GroIMP and R. Validation will be provided by experiments planned on a greenhouse located on the campus.

Your profile

You should have sound skills in at least two of the following domains: bioinformatics, data sciences, computer science or plant sciences. You must be at ease with programming (knowledge of the JAVA language would be a plus) and should have a strong interest in agronomy (or plant science) and be ready to carry out experiments in interaction with agronomists, but knowledge about the biological processes involved could be acquired during the Ph.D. thesis.
Applications with both data science and plant sciences degree will be appreciated. Your ability to communicate in English both orally and in writing is essential. (Basic) knowledge of the French language (resp., willingness to learn it) will be a strong asset, as you will have to communicate with technical staff.

More details, including the application procedure, are given in the attached file.