The article is devoted to the study of the use of neural networks to optimize the selection of plant stands in precision agriculture technologies. The study takes into account the complex aspects of sample selection, such as the speed of image acquisition, the effectiveness of assessing the state of mineral nutrition and soil moisture, etc. This data is a necessary component for precision farming technologies and, in particular, crop management. Research was conducted on production fields in 2019-2020 in Boryspil district of Kyiv region. Spectral studies were performed using the Slantrange 3p complex installed on the UAV. Data processing was performed both with the specialized software for spectral data Slantview and with the mathematical package MathCad. The assessment of the nature of the distribution of both individual spectral channels and their combination in the form of vegetation indices turned out to be unprepared for the identification of uneven water supply of areas. The red channel and its derivatives turned out to be the most promising in the direction of identifying the water supply of wheat. The use of neural networks made it possible to identify probable areas with increased water supply on the maps of the distribution of vegetation indices in the field. The duration of identification using neural networks will not interfere with the sampling procedure, so that such a procedure can be effectively implemented in agronomic practices. Therefore, the use of neural networks allows you to automate and increase the accuracy of selection, improving the quality of the analysis of plant stands, subject to compliance with soil sample evaluation technologies. The obtained results indicate the prospects of implementing this approach in modern agriculture
selection of plant samples, precision agriculture, remote monitoring, vegetation indices, UAV, neural networks
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