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<p>An empirical model of the probability of successful data transmission in the developed wireless sensor network is constructed. The simulation results enable predicting network data transmission and optimizing the network topology based on data transmission quality, energy efficiency, and coverage area. An example of constructing a polynomial model of the chlorophyll fluorescence induction curve using stepwise regression is provided. Machine learning methods are tested for analyzing measured chlorophyll fluorescence induction curves using the example of determining the watering needs of zinnia plants.</p>

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Methods and Models for Processing Data From a Wireless Sensor Network for Plant Condition Assessment by Chlorophyll Fluorescence Induction

  • 期刊论文

  • 发表日期：

    2026年1月1日

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  • H. V. Antonova,
  • V. M. Hrusha,
  • A. V. Kedych,
  • O. V. Kovyrova

摘要

An empirical model of the probability of successful data transmission in the developed wireless sensor network is constructed. The simulation results enable predicting network data transmission and optimizing the network topology based on data transmission quality, energy efficiency, and coverage area. An example of constructing a polynomial model of the chlorophyll fluorescence induction curve using stepwise regression is provided. Machine learning methods are tested for analyzing measured chlorophyll fluorescence induction curves using the example of determining the watering needs of zinnia plants.