Operational Mapping of Salinization Areas in Agricultural Fields Using Machine Learning Models Based on Low-Altitude Multispectral Images

Mukhamediev R. Amirgaliyev Y. Kuchin Y. Aubakirov M. Terekhov A. Merembayev T. Yelis M. Zaitseva E. Levashenko V. Popova Y. Symagulov A. Tabynbayeva L.
June 2023 Multidisciplinary Digital Publishing Institute (MDPI)

Drones
2023 #7 Issue 6

Salinization of cultivated soil is an important negative factor that reduces crop yields. Obtaining accurate and timely data on the salinity of soil horizons allows for planning the agrotechnical measures to reduce this negative impact. The method of soil salinity mapping of the 0–30 cm layer on irrigated arable land with the help of multispectral data received from the UAV is described in this article. The research was carried out in the south of the Almaty region of Kazakhstan. In May 2022, 80 soil samples were taken from the ground survey, and overflight of two adjacent fields was performed. The flight was carried out using a UAV equipped with a multispectral camera. The data preprocessing method is proposed herein, and several machine learning algorithms are compared (XGBoost, LightGBM, random forest, support vector machines, ridge regression, elastic net, etc.). Machine learning methods provided regression reconstruction to predict the electrical conductivity of the 0–30 cm soil layer based on an optimized list of spectral indices. The XGB regressor model showed the best quality results: the coefficient of determination was 0.701, the mean-squared error was 0.508, and the mean absolute error was 0.514. A comparison with the results obtained based on Landsat 8 data using a similar model was performed. Soil salinity mapping using UAVs provides much better spatial detailing than satellite data and has the possibility of an arbitrary selection of the survey time, less dependence on the conditions of cloud cover, and a comparable degree of accuracy of estimates.

elastic net , LightGBM , machine learning , random forest , ridge regression , soil salinity , spectral indexes , support vector machines , unmanned aerial vehicle , XGBoost

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Institute of Automation and Information Technology, Satbayev University (KazNRTU), Satpayev Str., 22A, Almaty, 050013, Kazakhstan
Institute of Information and Computational Technologies, Pushkin Str., 125, Almaty, 050010, Kazakhstan
Department of Information Technology, Maharishi International University, Fairfield, 52557, IA, United States
Faculty of Management Science and Informatics, University of Zilina, Univerzitná 8215/1, Žilina, 010 26, Slovakia
Baltic International Academy, Lomonosov Str. 1/4, Riga, LV-1019, Latvia
LLP Kazakh Research Institute of Agriculture and Plant Growing, Almaty, 040909, Kazakhstan

Institute of Automation and Information Technology
Institute of Information and Computational Technologies
Department of Information Technology
Faculty of Management Science and Informatics
Baltic International Academy
LLP Kazakh Research Institute of Agriculture and Plant Growing

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