Application of UAVs and Machine Learning Methods for Mapping and Assessing Salinity in Agricultural Fields in Southern Kazakhstan
Mukhamediev R.I.
December 2025Multidisciplinary Digital Publishing Institute (MDPI)
Drones
2025#9Issue 12
Highlights: What are the main findings? A method for quickly assessing field salinity has been developed based on the use of a UAV equipped with a multispectral camera and laboratory studies of the electrical conductivity of soil samples. Labeled datasets have been developed for tuning machine learning models and mapping field salinity in southern Kazakhstan. What is the implication of the main finding? The use of a UAV equipped with a multispectral camera and machine learning methods enables highly detailed salinity mapping of large field areas. Conditions in each field can vary; therefore, achieving expected results requires individual tuning of the models for each field. Soil salinization is an important negative factor that reduces the fertility of irrigated arable land. The fields in southern Kazakhstan are at high risk of salinization due to the dry arid climate. In some cases, even the top layer of soil has a significant degree of salinization. The use of a UAV equipped with a multispectral camera can help in the rapid and highly detailed mapping of salinity in cultivated arable land. This article describes the process of preparing the labeled data for assessing the salinity of the top layer of soil and the comparative results achieved due to using machine learning methods in two different districts. During an expedition to the fields of the Turkestan region of Kazakhstan, fields were surveyed using a multispectral camera mounted on a UAV; simultaneously, the soil samples were collected. The electrical conductivity of the soil samples was then measured in laboratory conditions, and a set of programs was developed to configure machine learning models and to map the obtained results subsequently. A comparative analysis of the results shows that local conditions have a significant impact on the quality of the models in different areas of the region, resulting in differences in the composition and significance of the model input parameters. For the fields of the Zhetisay district, the best result was achieved using the extreme gradient boosting regressor model (linear correlation coefficient Rp = 0.86, coefficient of determination R2 = 0.42, mean absolute error MAE = 0.49, mean square error MSE = 0.63). For the fields in the Shardara district, the best results were achieved using the support vector machines model (Rp = 0.82, R2 = 0.22, MAE = 0.41, MSE = 0.46). This article presents the results, discusses the limitations of the developed technology for operational salinity mapping, and outlines the tasks for future research.
digital surface model , machine learning , mapping , monitoring , precision farming , soil salinity , soil samples , unmanned aerial vehicles
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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
Institute of Automation and Information Technology
Institute of Information and Computational Technologies
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