DOI
10.34229/KCA2522-9664.26.1.15
UDC 004.942, 004.67
H.V. Antonova
V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine,
Kyiv, Ukraine,
antanna78@gmail.com
V.M. Hrusha
V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine,
Kyiv, Ukraine,
vhrusha@gmail.com
A.V. Kedych
V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine,
Kyiv, Ukraine,
annet.kedich@ukr.net
O.V. Kovyrova
V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine,
Kyiv, Ukraine,
kovyrova.oleksandra@gmail.com
METHODS AND MODELS FOR PROCESSING DATA OF WIRELESS SENSOR NETWORK
FOR ASSESSING THE STATE OF PLANTS BY MEANS OF CLOROPHYLL
FLUORESCENCE INDUCTION METHOD
Abstract. An empirical model of the probability of successful data transmission in the developed wireless sensor network has been constructed. The modeling results make it possible to predict data transmission in the network and optimize the network topology based on data transmission quality, energy efficiency, and coverage area. The example of building a polynomial model of the chlorophyll fluorescence induction curve using the stepwise regression method is presented. Machine learning methods were applied for analyzing the measured chlorophyll fluorescence induction curves on the example of the task of determining the need for watering zinnia plants.
Keywords: wireless sensor network, empirical data transmission model, logistic regression model, stepwise regression, data analysis, neural networks, SVM, XGBoost, microcontroller, Intelligent edge.
full text
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