2022, issue 1, p. 42-48
Received 15.06.2022; Revised 25.06.2022; Accepted 28.06.2022
Published 30.06.2022; First Online 03.08.2022
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Intelligent Processing of Data From Chlorophyll Fluorometric Sensors
V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv
Introduction. Chlorophyll fluorescence induction (CFI) is a monitoring method of plant objects. CFI is a radiation of chlorophyll in red spectrum during a chlorophyll lighting of alive plant in blue spectrum. Chlorophyll fluorometers – the special devices that are used for measurement of CFI. Series of such devices were developed in V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine. In particular, fluorometer «Floratest» and a network of wireless sensors were developed for CFI measurement. An accumulation of massive amount of measurements resulted into possibility to use intellectual methods like neural networks.
The purpose of the paper is to research the possibilities of machine learning methods (neural networks, support vector machine (SVM), XGBoost algorithm) for analysis of CFI curves that were measured by means of sensors developed in V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine.
Results. Neural networks, SVM, XGboost ensure early detection of influence of stress factors on state of plants before appearance of external symptoms on plants that was showed on basis of data received during experiments with treatment of plants by herbicide. Analogically there was showed the possibility of using the machine learning methods for determination of soil humidity. The better methods for given tasks were determined. The study of possibilities to enhance the results of mentioned methods by means of normalization was conducted. The best results were demonstrated by z-score normalization and by minimax normalization to the range [-1;1].
Conclusions. The application of different machine learning algorithm for processing CFI curves demonstrated that SVM and XGBoost better suit for task of classification plants treated by means of herbicide. Neural network demonstrated worst results. The application mentioned methods for task of determination of artificial watering necessity demonstrated that neural network shows better result, SVM shows worse result and XGBoost shows worst result.
Keywords: Chlorophyll fluorescence induction, neural network, support vector machine, algorithm XGBoost.
Cite as: Hrusha V. Intelligent Processing of Data From Chlorophyll Fluorometric Sensors. Cybernetics and Computer Technologies. 2022. 1. P. 42–48. (in Ukrainian) https://doi.org/10.34229/2707-451X.22.1.5
1. Romanov V. Galelyuka I., Antonova H., Kovyrova O., Hrusha V., Voronenko O. Application of Wireless Sensor Netwoks for Digital Agriculture. Proceedings of the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems.2019. P. 340–344. https://doi.org/10.1109/IDAACS.2019.8924267
2. Samborska I., Alexandrov V., Sieczko L, Kornatowska B., Goltsev V., Cetner M., Kalaji H. Artificial neural networks and their application in biological and agricultural research. Signpost open access journal of nanophotobiosciences. Vol. 02. 2014. P. 14–30.
3. Silva J., Figueiredo A., Cunha J., Eiras-Dias J., Silva S, Vanneschi L., Mariano P. Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes. Plants. Vol. 9. Issue 2. 174. 2020. https://doi.org/10.3390/plants9020174
4. Chlorophyll fluorescence spectral discrimination by artificial neural network methods. DEFRA project code HH1530SPC. 2002.
5. Kirova M., Ceppi G., Chernev P., Goltsev V., Strasser R. Using Artificial Neural Networks for Plant Taxonomic Determination Based on Chlorophyll Fluorescence Induction Curves. Biotechnology and Biotechnological Equipment. XI Anniversary Scientific Conference 120 Years of Academic Education in Biology 45 Years Faculty of Biology. P. 941–946. https://doi.org/10.1080/13102818.2009.10818577
6. Goltsev V., Zaharieva I., Chernev P., Kouzmanova M., Kalaji H.M., Yordanov I., Krasteva V., Alexandrov V., Stefanov D., Allkhverdiev S.I., Strasser R.J. Drought-induced modification of photosynthetic electron transport in intact leaves: Analysis and use of neural network as a tool for a rapid non-invasive estimation Biochimica et Biophysica Acta. 1817 (8). 2012. P. 1490–1498. https://doi.org/10.1016/j.bbabio.2012.04.018
7. Rybka K., Janaszek-Mankowska M., Siedlarz P., Mankowski D. Machine learning in determination of water saturation deficit in wheat leaves on basis of Chl a fluorescence parameters. Photosynthetica. 57 (1). 2019. P. 226–230. https://doi.org/10.32615/ps.2019.017
8. Soja G., Soja A.M. Recognizing the Sources of Stress in Wheat and Bean by using chlorophyll fluorescence induction parameters as inputs for neural network models. Phyton. Special issue: «D. Grill». 45 (3). 2005. P. 157–168.
9. Xanthoula Eirini Pantazi, Dimitrios Moshou, Dimitrios Kasampalis and Pavlos Tsouvaltzis. Automatic Accessment of Phenotypes in lettuce plants by using Chlorophyll Fluorescence Kinetics and Machine Learning. Proceedings International Conference of Agricultural Engineering. AgEng. 2014. Zurich, 6-10.07.2014. P. 167–176.
ISSN 2707-451X (Online)
ISSN 2707-4501 (Print)
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