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Agricultural land is a strategic government resource. Successful agribusiness depends on good land management. Therefore, it is important to take into account many points, including the degree of overgrowth of agricultural land. Overgrowing of fields leads to a decrease in the quality of soil fertility. Monitoring of geographical objects is most easily carried out with the help of geographic information systems and unmanned aerial vehicles. This reduces the time spent exploring the area, as well as improving the reliability of the results. In our case, this is the definition of overgrowth of agricultural fields. The developed methodology will be useful both for state agribusiness monitoring and control services and for landowners. This determines the relevance of scientific research. The work aims to develop a method based on computer vision or automated processing of images from unmanned aerial vehicles (UAVs) to determine the degree of overgrowth of agricultural fields. As a result, a conceptual model of the methodology was built, as well as an information system developed on its basis for thematic image processing. A snapshot of an agricultural plot of the Vologda region (Russia) is used as source material. An error matrix is used to evaluate the image's division into areas of agricultural fields and vegetation. The matrix is based on a comparison of the reference result of decoding with the result of using the method. The matrix allows us to take into account errors associated with the incorrect classification. The reliability assessment consists of two stages: the creation of a matrix, the dimension of which is determined by the number of classes; the calculation of statistical accuracy estimates in percentage terms, based on the results. Using the error matrix and Cohen's Kappa index, the reliability of the decoding was evaluated using the developed methodology. Compared to conventional decoding without preprocessing steps and trainable classification, the confidence increased by 11%.
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