FIELD: physics.
SUBSTANCE: presented technical solution relates, in general, to computer engineering, and in particular to a method and a device for determining types of agricultural crops using deep learning based on the Earth remote sensing (ERS) data and weather data. Said technical result is achieved owing to implementation of the method for determining the type of agricultural crop placed on a field, which is carried out by at least one computing device, comprising steps of: receiving a request to determine the type of agricultural crop field; obtaining a first set of multispectral images for a given period of time, wherein each multispectral image of the agricultural field contains wave polarization values, in particular VH polarization values, VV polarization and angle of incidence of the beam when shooting by radar aperture synthesis; based on said first set of images, determining a set of polarization indices for the agricultural field; obtaining a second set of multispectral images for a given period of time, wherein each multispectral image of the agricultural field contains pixel intensities in the visible infrared spectrum, near infrared spectrum and short-wave infrared spectrum, as well as image pixel cloud values; based on said second set of images, determining a set of vegetation indices for the agricultural field and a set of colour indicators; determining weather data for a given period of time for an agricultural field; based on data characterizing the type of crop, a set of polarization indices, a set of vegetation indices, a set of colour indices and weather data, the type of crop of the field is determined.
EFFECT: high accuracy when determining the type of agricultural crop.
29 cl, 6 dwg
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Authors
Dates
2025-02-11—Published
2022-10-17—Filed