Sathyanarayan Rao, PhD

Environmental Sensing & Inverse Problems

Electrical Anisotropy as Root Architecture Fingerprint

Demonstrated that electrical anisotropy encodes root architecture — species discrimination at 95% accuracy using k-NN on electrical signatures alone. First mechanistic proof of 3D structural information in geoelectrical data.

Period
2018–2019
Where
UCLouvain — PhD Research
Role
PhD Researcher (Visiting Scholar, Univ. Bonn)
Methods
k-NN/PCA/ML, Anisotropy Tensors, Root Architecture
Directional conductivity structure of a simulated root system
Directional conductivity structure of a simulated root system

Research Question: Does electrical anisotropy contain information about root architecture?

Finding: Electrical anisotropy is a fingerprint of root organization — the first mechanistic proof that geoelectrical measurements encode 3D structural information.

Methodology:

  • Generated synthetic root architectures using C-Rootbox (monocots vs. dicots)
  • Computed direction-dependent conductivity tensors
  • Extracted geometrical indices (convex hull, depth, width, tortuosity)
  • Applied machine learning (PCA + k-NN classification)

Key Results:

  • Magnitude component (low frequency): water uptake patterns
  • Phase component (high frequency): root architecture directly
  • Species discrimination: 95% accuracy using k-NN on electrical signatures alone

Publications: 2 conference papers (Geophysical Research Abstracts) Thesis Chapter: 4 Collaboration: Prof. Andreas Kemna (Bonn)