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Plant stem tissue modeling and parameter identification using metaheuristic optimization algorithms.

Mohamed S Ghoneim1, Samar I Gadallah2, Lobna A Said2

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New fractional-order bio-impedance models accurately characterize plant stem tissue, offering improved insights for agricultural applications. These advanced models enhance understanding of biochemical and biophysical changes in plants.

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Area of Science:

  • Agricultural Science
  • Biophysics
  • Electrical Engineering

Background:

  • Bio-impedance measurement is increasingly used in agriculture to assess plant tissue.
  • Impedance variations correlate with biochemical and biophysical changes.
  • Circuit modeling is crucial for interpreting bio-impedance data.

Purpose of the Study:

  • To propose and validate two novel fractional-order bio-impedance models for plant stems.
  • To compare the proposed models against established fractional-order models (Cole, Double Cole, Fractional-order Double-shell).
  • To evaluate model performance in characterizing plant stem biological cellular morphology.

Main Methods:

  • Experimental measurements of bio-impedance on Lamiaceae plant species using an electrochemical station (SP150).
  • Data acquisition across a frequency range of 100 Hz to 100 kHz.
  • Model fitting using four metaheuristic optimization algorithms to extract parameters and assess model efficiency.

Main Results:

  • The two proposed fractional-order models demonstrated superior performance in fitting measured plant stem bio-impedance data.
  • The proposed models provided the best results across all tested inter-electrode spacing distances.
  • Comparative analysis identified the most effective metaheuristic optimization algorithm for bio-impedance parameter extraction.

Conclusions:

  • The novel fractional-order bio-impedance models effectively characterize plant stem tissue.
  • These models offer enhanced accuracy for non-invasive plant monitoring in agriculture.
  • The study validates the utility of advanced bio-impedance modeling for agricultural applications.