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Duration: 6 months
Status: Concluded
Project Contact:
Rita Ribeiro

Scientific Advisors: Rita Ribeiro
Financing: ESA / ACT
Technical Officer:
Project poster: (pdf)

SIMoRG - Swarm Intelligence Modelling of Root Growth - Past Research Projects
Project Summary
The physiological union of the tips of the roots of a plant (apexes) serves the greater task of supporting and nurturing the plant (among others). In fact, all directional growth decisions and a majority of environmental sensing are made in the apex. Growth patterns of roots are basically influenced by gravity, genetics, soil conditions, and distribution of nutrients. Since there is no anatomic evidence for a central sensing and decision unit and considering the rather low computational capacity of a plant cell, it appears meaningful to consider the apex as a simple autonomous unit taking decisions on own account. Yet, when looking at the root as a collective, growth patterns are not chaotic, but seem to follow a higher order, and emerge as a result of the individual decision-making of the apexes.

Analyzing and subsequently simulating root growth has been in the focus of previous research. The major justification for these analyses derives from agricultural/physiological questions on root efficiency, soil exploitation, nutrient uptake per volume root, etc. The main means used are fractal methods, i.e., describing root architecture as a fractal. That work nicely models root architectures, however, the technique involves recursive formulation and hierarchical levels and although the simulations of roots match quite well the observed growth patterns in real plants, it does not reflect the decision processes actually going on during root growth.

We introduce a cellular automata model of soil and root dynamics. This model captures a set of essential features that condition root development, and enables the evaluation of candidate root systems in terms of efficiency. Evolutionary algorithms are then applied in order to identify local and decentralized apex behaviors (specified in terms of rule-bases and neural networks), that by parallel, repeated application, result in maximal collective efficiency at the level of the root system.

Research Areas
  • Feedforward neural networks
  • Particle Swarm Optimization
  • Plant root biology