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UNSAT

U-Net segmentation of 3D micro-CT images of rooted soils using label data from multi-physics simulators

The health of soil is equally important as the health of humans; soil has to be strong enough to sustain the built infrastructures while being functional to allow water and nutrient transport to plants. While image processing has been a great help to support medical doctors in diagnosis, the use of images is not so straightforward for soil characterization. Difficulties arise simply because soil is random in nature (it contains e.g., solid grains, water, and plant roots) and has living organisms (e.g., plants) constantly modifying its structure. The project UNSAT tries to improve the identification capability of 3D image processing by training machine learning classifiers with high-fidelity, physics-based simulation data. We are interested in how new machine-learning techniques can help us to observe water transport and how root and soil react to water cycles during the growth of young maize.

Participating organisations

University of Twente
Netherlands eScience Center

Team

FA
Floriana Anselmucci
Lead Applicant
University of Twente
Rena Bakhshi
Rena Bakhshi
Programme Manager
Netherlands eScience Center
SJ
Sherin Joseph
HC
Hongyang Cheng

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