RU2693255C1 - Technique for remote reconnaissance diagnostics of providing plants with nitrogen (using a multispectral camera and unmanned aerial vehicles) - Google Patents

Abstract

FIELD: agriculture.

SUBSTANCE: invention relates to agriculture, in particular to methods for reconnaissance diagnostics of plants state using unmanned aerial vehicles (UAV) to obtain photometric data. In the method, a reference area is formed next to the diagnosed field, wherein the platform has a homogeneous surface and is marked with markers. Control areas marked with markers are formed in the area of the diagnosed area. Reference sites have different degree of plant density. Method includes aerial survey of plant growth zone by means of UAV equipped with multichannel chamber, UAV overlap shooting area is equal to 60 %. Using UAV, multispectral images of the field are obtained with reference to geographical coordinates of the survey areas. Plant ground measurement is carried out using an N-tester on the above control field sites to obtain data on nitrogen content in plant leaves. Ground-based measurement of spectral brightness of point objects on a reference area and values of spectral brightness of the calibration panel is carried out using a spectroradiometer. Determining the terrestrial coefficients of spectral brightness (CSB) by dividing the measurement results of the sections on the reference site with a spectroradiometer to the result of measuring the calibration panel by itself with bringing the result to range of 0–1 (0–100 %). Data of aerial photography and ground measurements are transmitted to a computing device and processed, during which a multispectral orthophotomap of the diagnosed field is formed from aerial survey data. Calculating atmospheric correction values for each pixel of orthophotomap by regressing initial values of brightness of pixels obtained directly during aerial photography, to CSB obtained during ground measurements of reference sites included in the orthophotomap. Map of vegetation index NDVI or GNDVI displaying nitrogen content in plants of the diagnosed field is plotted. Calibration of the obtained maps is performed using data of ground measurement of reference sites.

EFFECT: method provides higher accuracy of plants status determination.

1 cl, 5 dwg, 1 tbl

Description

TECHNICAL FIELD

[1] The claimed technical solution relates to the field of agriculture, in particular, to methods of reconnaissance diagnostics of the state of plants using unmanned aerial vehicles (UAVs) for obtaining photometric data.

BACKGROUND

[2] Diagnostics of the state of plants, for example, mineral, primarily nitrogen, plant nutrition is a priority of agrochemical science and agricultural practice [1]. Soil diagnostics were widely used to determine the need of crops for such basic nutrients as phosphorus and potassium, i.e. determination of the mobile forms of these elements in soils, on the basis of which the doses of the respective types and forms of fertilizers were calculated. Soil diagnostics was also used to identify the need of plants for nitrogen fertilizers, mainly in the pre-sowing period or at the beginning of the active growing season of crops. But in contrast to the content of mobile forms of phosphorus and potassium, which is characterized by relative stability even for several years, the content of nitrogen compounds available for plant nutrition in soils requires constant monitoring during each vegetation period due to the instability over time, the dynamics of this indicator side, and the special requirements of plants to nitrogen almost throughout their growing season, on the other.

[3] As a rule, nitrogen fertilizers are applied to the soil in early spring immediately before sowing of spring crops or superficially at the beginning of the growing season of winter crops, focusing on the data of agrochemical soil surveys, including operational diagnostics. During the critical periods of vegetation (tillering-branching, booting-stinging, earing-flowering, seed formation), vegetative dressing with nitrogenous fertilizers according to chemical methods of plant diagnostics, stem and leaf, is carried out to optimize the nitrogen nutrition of grain and other crops [2, 3].

[4] In recent decades, along with chemical methods, physical, namely photometric, methods for diagnosing nitrogen nutrition in crops, based on the relationship between the intensity of the green color of plants and their nitrogen supply, have become ever more important [4]. Photodetectors of diagnostic devices record either the concentration of chlorophyll in the indicator organs of plants, or the intensity of its fluorescence. As a result of the thematic processing of contact or remote determination of these indicators, the need of certain crops for nitrogen fertilizers is calculated during a particular growing season. The most widely used is the calculation of the so-called vegetation index (NDVI), which represents the ratio of the difference between the values of the infrared and red spectra of electromagnetic reflection of solar or artificial light from plants to their sum.

[5] Electromagnetic radiation detectors of plant biomass are photometric devices of various designs used as portable (hand-held) devices (European YARA, CropCircle, American SSM-200, SSM-1000, GreenSeeker, domestic models are single-beam and dual-beam "Spectrolux"), as well as mobile N-sensors installed on fertilizer spreaders ("YARA", "ALS"), and multi-zone photometers installed on aviation or space platforms. Of the types of space imagery, photographing and TV shooting with a fixed wavelength of 0.3–1.1 μm, spectrometric display — 0.3–3.0 μm, infrared display — 3– 300 μm, microwave display — 0.3 - 10 cm, radar indication - 10 - 70 cm. For example, the Russian Resurs-02D satellite is equipped with the ADAPTON multi-zone scanning device with a spectral range from 0.5 to 2.4 μm with a resolution of 30 m on the ground, video-spectrometry equipment “ Sun "with a spectral range from 0.4 to 1.0 microns and a resolution of 30 m, scanning device Ultra high resolution VZOR with a spectral range of 0.5–0.9 μm and a resolution of 2 m in the panchrome and 4 m in the spectrum with a total number of 266 spectral channels. Spectrometric or radar information obtained in one way or another is used by different industries National economy. Studies have shown that ground and aerospace surveys can be successfully used to diagnose nitrogen nutrition of plants [5].

[6] However, despite significant advantages over traditional chemical diagnostics, both the ground and space indication of nitrogen supply of crops have certain limitations: ground - on the scale of coverage, aerospace - on time parameters. At the same time, it was shown that low-flying aircraft, in particular helicopters, are the most suitable for photometric surveys of crops, although its use has a significant limitation on the cost of diagnostic work. In this respect, the use of unmanned aerial vehicles, UAVs equipped with appropriate photometric equipment, for the prompt diagnosis of nitrogen nutrition of plants is the most promising.

[7] Private applications of UAVs for diagnostics, in particular, nitrogen nutrition of plants, are known, for example, from application US20150254800A1 (FI2 Solutions LLC, 09/10/2015). This solution reveals the general principles of applying the NDVI index obtained by aerial photography of UAVs, with its subsequent correlation to predict the nitrogen saturation index of plants. This approach has a sufficiently large error in accuracy, since it does not provide for the correction of the resulting orthophotomap, which is used to build the NVDI index map, and also does not provide for calibration of UAV equipment based on ground-based photometric diagnostics data.

DISCLOSURE OF INVENTION

[8] The technical problem to be solved is the provision of a new, more accurate and universal method of reconnaissance diagnostics of the state of plants using UAVs, as well as expanding the possibility of analyzing various indicators of the condition of plants.

[9] The technical result is to improve the accuracy of determining the state of plants using photometric information obtained during aerial surveys of a UAV, by calibrating photometric equipment of UAVs based on ground-based photometric diagnostics and correcting aerial data by reducing them to spectral brightness coefficients (CLS), measured ground-based spectroradiometer on the reference sites.

[10] The stated result is achieved due to the method of reconnaissance diagnostics of the state of plants on the fields, which contains the steps in which:

- form a reference area next to the diagnosed field, and the area contains a uniform surface and marked with markers;

- in the field of the diagnosed field form control sites, marked with markers, and control sites have varying degrees of plant density;

- carry out aerial surveys of the growing area of plants using a UAV, equipped with a multi-channel camera, and the overlap zone of the shooting UAV is 60%;

- using the UAV, multispectral images of the field are obtained with reference to the geographic coordinates of the survey areas;

- perform a ground-based measurement of plants using an N-tester on the above-mentioned control plots of the field to obtain data on the nitrogen content in the leaves of plants;

- using a spectroradiometer to perform ground-based measurements of the values of the spectral brightness of point objects on the reference platform and the values of the spectral brightness of the calibration panel;

- determine the ground-based spectral brightness coefficients (CLS) by dividing the results of measuring the areas on the reference platform with a spectroradiometer by the measurement result of the calibration panel with the same name, and bringing the result to the range of 0-1 (0-100%).

- transmit aerial survey data and ground-based measurement data to a computing device, and process them, during which:

- form a multispectral orthophotomap of the diagnosed field according to aerial survey data;

- calculate the atmospheric corrected KLS for each pixel of the orthophotomap, by regressing the initial brightness values of the pixels obtained directly during the aerial survey, to the QLS, obtained by ground measurements of the reference sites included in the orthophotomap;

- carry out the construction of a map of the vegetation index NDVI or GNDVI, showing the nitrogen content in plants of the diagnosed field; and

- perform calibration of the obtained maps using ground-based measurement data of reference sites.

[11] In one of the particular examples of the implementation of this solution, at the stage of constructing maps of the vegetation index, at least one of the following indicators selected from the group is determined: the number of elevated plant phytomass, density of seedlings.

DESCRIPTION OF THE DRAWINGS

[12] FIG. 1 illustrates the general scheme for implementing the claimed solution.

[13] FIG. 2 illustrates the process of transferring data to a primary computing device.

[14] FIG. 3 illustrates a general view of the elements of the UAV.

[15] FIG. 4 illustrates a general view of a computing device.

[16] FIG. 5 illustrates a flowchart of the algorithm for constructing maps of the state of plants.

IMPLEMENTATION OF THE INVENTION

[17] As shown in FIG. 1, the main elements ensuring the implementation of the claimed method for diagnosing the state of plants on the field (10) are the UAV (20), the ground-based spectrometer (30) and the N-tester (30).

[18] As a ground-based spectrometer (30), for example, OceanOptics USB 2000+, Maya and analogs can be used, covering the entire wave range of a multispectral camera mounted on a UAV, and having the required sensitivity. As an N-tester (40), for example, Spad 502 Plus, Yara or their analogues can be used.

[19] The UAV (20) can be a helicopter or aircraft type aircraft, for example, a UAV (20) developed by AgroDronGroup, such as: Phantom 4 Pro or Orlan (http://agrodronegroup.ru/).

[20] At the first stage of the implementation of the declared solution, ground works are performed before obtaining aerial survey data using UAVs (20).

[21] Next to the diagnosed field (10), one or several test pads (110, 111) are prepared, each of which is marked with white markers, which ensures their exact identification in the images. The site (110, 111) is prepared with a size of approximately 7x5 meters and should contain a uniform surface of medium brightness. For example, a dirt or asphalt road, a section of a plowed field without vegetation can act as a test site (110, 111). When performing aerial surveys, the test pad (110, 111) should fall into the survey area.

[22] Calibration of photometric instruments (cameras, spectrometers), i.e. the creation of scales that should show the levels of plant nutrition of a particular crop with nitrogen nutrition is the most time-consuming, important and responsible operation in the whole technological chain of practical diagnostics of nitrogen nutrition of plants.

[23] Field experiments should consist of variants representing doses of ammonium nitrate or other nitrogen fertilizers in the active substance, i.e. nitrogen (N) from zero (N0) to supposedly optimal or exceeding optimum, for example, N 120-150 kg / ha. Since the calibration of devices does not require large areas of sowing, field experiments are laid with field-field control sites for each variant of several square meters, for example, 40 m2 (4 mx 10 m). If any other tasks are put before the field experience, for example, ecological or economic, then each variant of the fertilizer of such scientific experience is laid in triplicate or quadruplicate. If the task is limited only to the calibration of a photometric device, then a bookmark of the so-called production (scientific and production) field experience in a one-to-two repetition is made to simplify technological operations.

[24] In different places of the field (10), in particular, with low, high and medium plant density, are marked with markers, clearly distinguishable on aerial photography, field field control sites (101) - (106). Intra-field areas (101) - (106) are manufactured, preferably, in the amount of 7-10 pieces. size 5x5 meters.

[25] The next step is to prepare the UAV (20) for aerial surveys. The UAV sensors (20) are set up for shooting, during which, the UAV camera sensors (20) are calibrated and adjusted using a certified calibration panel. Then a flight task is loaded into the memory of the UAV (20) with an indication of the coordinates of the field (10). The flight altitude is set, depending on the aircraft used to perform the aerial survey. For example, in the case of using a UAV in the form of a kopter, the height of its flight is set no higher than 100 m, and the overlap of the shooting is 60%. When using an aircraft type UAV, its flight altitude may exceed the mentioned value. After the setup of the UAV (20) has been completed, it is launched to perform aerial filming in an automated mode.

[26] The speed of movement of the UAV (20) depends on the flight altitude and speed of its work (taking pictures of specified areas of the field), which is determined according to the data of the autonomous route of the UAV (20), in particular, the trajectory of its work, as well as the longitudinal overlap - frame rate , and transverse overlap - the distance between the zones of flight.

[27] During the aerial survey, ground-based work is also carried out, which consists in measuring the spectral brightness coefficients of the surface of the test site (110, 111) with a uniform surface of medium brightness (7-10 points) using the ground-based spectrometer (30). The obtained indicators are then used to make atmospheric correction in the results of spectral aerial photography.

[28] Measurements at the test site (110, 111) alternate with frequent measurements of a certified calibration panel (at least one measurement of the spectrum of the panel before and after 10 measurements of an object with stable lighting or measurement of the spectrum of a panel before and after each measurement of an object with changing lighting, for example variable cloud cover).

[29] At the intra-field control sites (101) - (106), the N content of the plant leaves is measured using an N-tester (40). The number of measurements may be different and depends on the type of plant, for example, for winter wheat in flag leaves, at least 30 measurements are carried out.

[30] First of all, in special field experiments, the nature of the dependence of the state of crops on increasing doses of nitrogen fertilizers was identified, since this indicator should be the basis of logical and statistical evaluations of the photometric diagnosis of nitrogen nutrition of plants. As a result of studies, the dependence of photometric indicators on increasing doses of nitrogen fertilizers applied to agricultural crops, the relationship with other diagnostic indicators studied in these field experiments, the relationship with crop yield and crop quality was established.

[31] It is characteristic that the testimony of N-testers somewhat decreases during the growing season of crops, which is associated with a gradual decrease in the content of chlorophyll, i.e. the transition of plants from the growth of organic substances to their transportation from vegetative organs to generative ones with an appropriate biochemical transformation. This is evidenced, in particular, by the results of photometry of white mustard (Fig. 4). During the transition from the beginning of flowering to the seed formation phase, the N-tester Nara testimony noticeably decreased, and the dependence on the doses of nitrogen fertilizers added to the crop even increased, due to the high dependence of the mustard biomass on the availability of nitrogen. In the beginning of flowering phase, the correlation coefficient of the photometer in the diagnosis of mustard leaves with nitrogen doses was 0.78, in the end of flowering phase, 0.91, and in the seed formation phase, it reached 0.94. From this it follows that the readings of the photometers reflect the real supply of plants with nitrogen nutrition, which in turn affects the yield of agricultural crops. In other words, high statistical and biologically validated reliance of N-testers on doses of nitrogen fertilizers serves as the scientific basis for diagnosing nitrogen nutrition of plants, which makes it possible to abandon complex and time-consuming manual operations of plant diagnostics unsafe for health, and to a certain extent to robotize diagnostic processes.

[32] Also, at each inner-field site (101) - (106),

(using a video camera, smartphone, etc.): general photo of the field, which characterizes the uniformity of shoots, with GNSS reference coordinates; a photograph of the plants that fit inside the metering frame of the agronomist (agronomist's line) laid on the plants, characterizes the density of standing seedlings; A photograph of the most typical 5 –ti plants on a white background, characterizes the degree of plant development.

[33] As shown in FIG. 2, after the flight mission of the UAV (20) is completed, the aerial survey data is transmitted together with the GNSS reference data of the image coordinates and exterior orientation parameters (coordinates of image centers, roll, pitch, height, etc.) to the computer computing device (50). Data from ground-based measurements using a spectroradiometer (30) and an N-tester (40) are also transmitted to a computing device (50).

[34] FIG. 3 shows a general example of a UAV scheme (20). The UAV (20) contains a computing unit (201), which provides processing of the received data from the camera (205), and may be a processor or a microcontroller.

[35] The memory (202) of the UAV (20) may represent one or several means that provide the storage of the necessary program logic for the flight mission, as well as the storage of images recorded by the camera (205). The memory (202) is preferably a combination of RAM and ROM, and may be of various known volatile and / or nonvolatile types, such as an integrated or removable flash memory (memory card), EEPROM, HDD, SSD, DRAM, SRAM and etc.

[36] The navigation system (203) is a receiver of GNSS signals, in particular, GPS, GLONASS, Galileo, Compass, or combinations thereof. The navigation system (203) also provides a binding of coordinates to photographs.

[37] As a means of receiving and transmitting information (204), solutions can be used to provide a data channel of a wired and / or wireless type, for example, GSM / GPRS / LTE / 5G modem, Wi-Fi module, Bluetooth and / or BLE module, satellite module, etc.

[38] The UAV camera (205) is a multispectral (5 channels) or hyperspectral camera (up to 40 channels) providing images in the visible (RGB) and near infrared (NIR) ranges.

[39] The UAV (20) may also contain a battery charge control sensor (206), which monitors the battery charge (207) and provides optimization of the UAV flight time (20) and its return for charging / replacing the battery (207).

[40] The UAV (20) performs movement with the help of a propeller group (208). The elements of the UAV (20) are interconnected by means of a data bus (210), which ensures the transmission of the necessary control signals and information flows.

[41] FIG. 4 shows an example of a computing device (50). The device (50) can be selected from a number of known solutions and represent, without limitation, a personal computer, laptop, tablet, smartphone, server, mainframe, etc.

[42] In general, the device (50) contains such components as: one or more processors (501), at least one memory (502), data storage medium (503), input / output interfaces (504), means B / B (505), networking tools (506).

[43] The processor (501) of the device performs the basic computational operations necessary for the operation of the device (50) or the functionality of one or more of its components. The processor (501) executes the necessary machine-readable instructions contained in the RAM (502), ensuring the execution of the required logical data processing operations.

[44] Memory (502), as a rule, is made in the form of RAM and contains the necessary program logic providing the required functionality.

[45] The data storage tool (503) can be in the form of HDD, SSD disks, array raid, network storage, flash memory, optical information storage devices (CD, DVD, MD, Blue-Ray disks), etc.

[46] Interfaces (504) are standard tools for connecting and working with a device (50), for example, USB, RS232, RJ45, LPT, COM, HDMI, PS / 2, Lightning, FireWire, etc. The choice of interfaces (504) depends on the specific design of the device (50).

[47] As a means of I / O data (505) can be used: keyboard, joystick, display (touch screen), projector, touchpad, mouse, trackball, light pen, speakers, microphone, etc.

[48] Networking tools (506) are selected from devices that provide network data reception and transmission, for example, Ethernet card, WLAN / Wi-Fi module, Bluetooth module, BLE module, NFC module, IrDa, RFID module, GSM / GPRS / LTE / 5G modem, etc. With the help of means (505), the organization of data exchange via wired or wireless data transmission channel, for example, WAN, PAN, LAN (LAN), Intranet, Internet, WLAN, WMAN, UMTS, GSM, 4G / 5G is provided.

[49] The device components (50) are interfaced by a common data bus (510).

[50] FIG. 5 shows a flowchart for performing plant diagnostics based on data obtained during ground scanning and aerial surveys, which are transmitted (601) to a computing device (50). Next, at the stage (602), the primary processing of the mentioned data is performed using a computing device (50), during which using a photogrammetric algorithm, a balanced in brightness digital spectral orthophotomap of the diagnosed field (10) is created.

[51] At the next stage (603), the results of ground-based and aerial spectrometric measurements are given in spectral brightness coefficients (CML), and atmospheric correction is carried out. Such correction can be carried out in the camera software (205) to ensure correction of newly acquired images from the UAV (20), or in a specialized GIS software using regression of the average brightness values of the reference site (110, 111) in each spectral zone (visible and infrared ranges) to the average values of the CMS reference sites from ground-based measurements.

[52] Next, in step (605), one or more plant state maps are constructed on the basis of spectrometric and photographic measurements of the diagnosed field (10).

, [53] The mapping of nitrogen content in plants is carried out on the basis of calculations using the formulas:
NDVI =

,

where RXXX is the spectral brightness value at the wavelength.

[54] The map of the above-ground plant phytomass and seedling density can be calculated based on the regression of the NDVI or GNDVI values to the values of these parameters, determined at the intrafield control sites.

[55] Index maps are created in a GIS computing device (50) and are calibrated in step (606) using regression on test pads (110, 111) marked by markers, which are clearly visible in the images.

[56] Ground-based measurements using the N-tester allow you to compare the NDVI and GNDVI indices with an agronomic index — the nitrogen content index. Several dozen measurements allow for linear regression and, instead of NDVI or GNDVI, provide a map of the nitrogen content in plants.

[57] The following is a more detailed example of the implementation of the presented method. The studies were carried out on the basis of the Central Field Experimental Station of the Russian Academy of Sciences' Agrochemistry. D.N. Pryanishnikov (Moscow region) by setting up a field experiment with increasing doses of nitrogen fertilizers introduced in the spring of 2017 for winter wheat varieties Moskovskaya 3. Repetition of the experiment -3-fold, the size of the intra-field control sites 4 x 15 m. Experience scheme includes 5 options: 1) . The control is N0, 2) N30, 3) N60, 4) N90, 5) N120. The experiment used traditional agricultural cultivation of winter wheat, consisting in the fight against harmful organisms through the use of chemical plant protection products.

[58] In the course of the study, indicators of NDVI values calculated using data obtained from UAVs were obtained with other indicators for winter wheat (booting phase, 2017), which are presented in Table 1.

Table 1

Indications NDVI, points Doses of nitrogen, in the experiment, kg / ha (N) kg / ha Indications photometer "Yara" points Stem diagnostic data, indexes Winter wheat yield, t / ha 0.65 0 356 0 2.83 0.81 thirty 511 0.87 4.41 0.84 60 541 1.4 4.99 0.85 90 580 2.5 five, 0.86 120 620 2.7 5.1 Coeff. correlations 0.84 0.97 0.86 0.99

[59] To determine the accuracy of remote diagnostics of nitrogen nutrition using UAVs, it is necessary to compare it with the already approved photometric diagnostic techniques. These include, in particular, contact photometry with the use of the Nara tester. Studies have shown that with the correlation coefficient of NDVI readings with UAVs with the readings of the Yara photometer at 0.97 (Table 1), the coefficient of determination, i.e., the direct relationship between these indicators is 0.94, or 94%. Thus, the difference between the supply of winter wheat with nitrogen supplied by the ground-based method from the Yar photometer and remotely using a photometer installed on the UAV "is only 6%, and some of them (about 2%) are inaccurate actually photometer "Yar" and only 4% - on the error of remote photometry.

[60] Atmospheric correction of images makes it possible to compare aerial data not only within maps for a single date, but also between dates, which makes monitoring of the condition of agricultural crops much more efficient and allows obtaining reliable maps of quantitative dynamics.

Bibliography:

1. Pryanishnikov D.N. Selected Works. T. 1. - M .: Kolos, 1965. - 767 p.

2. Methods of field experiments to optimize the nitrogen nutrition of crops, sugar beet and potatoes based on operational soil and plant diagnostics. The team of authors. - M. VNII agrochemistry and agrosovodoveve them. D.N. Pryanishnikov, 1985. - 92 p.

3. Tserling V.V. Diagnostics of food crops: a Handbook. - M .: Agropromizdat, 1990. - 235 p.

4. Osipov Yu.F., Ivanitsky Ya.V., Shirinyan M.Kh., Afanasyev RA, Galitsky V.V. Using the device "N-tester" Yara "for the diagnosis of nitrogen nutrition of winter wheat. Fertility, 2011. № 1.

5. Afanasyev R.A. Agrochemical maintenance of precision farming // Problems of agrochemistry, 2008. No. 3. P. 46-53.

Claims (14)

1. The method of reconnaissance diagnostics of the state of plants in the fields, containing the steps in which: form a reference area next to the diagnosed field, and - the site contains a uniform surface and is marked with markers; - in the field of the diagnosed field form control sites, marked with markers, and control sites have varying degrees of plant density; - carry out aerial surveys of the growing area of plants using a UAV, equipped with a multi-channel camera, and the overlap zone of the shooting UAV is 60%; - using the UAV, multispectral images of the field are obtained with reference to the geographic coordinates of the survey areas; - perform a ground-based measurement of plants using an N-tester on the above-mentioned control plots of the field to obtain data on the nitrogen content in the leaves of plants; - using a spectroradiometer to perform ground-based measurements of the values of the spectral brightness of point objects on the reference platform and the values of the spectral brightness of the calibration panel; - determine the ground-based spectral brightness coefficients (CLS) by dividing the results of measuring the areas on the reference platform with a spectroradiometer by the measurement result of the calibration panel with the same and reducing the result to a range of 0-1 (0-100%); - transmit aerial survey data and ground-based measurement data to a computing device and process them, during which: - form a multispectral orthophotomap of the diagnosed field according to aerial survey data; - calculate the atmospheric corrected KLS for each pixel of the orthophotomap by regressing the initial brightness values of the pixels obtained directly during the aerial survey to the QLS, obtained by ground measurements of the reference sites included in the orthophotomap; - carry out the construction of a map of the vegetation index NDVI or GNDVI, showing the nitrogen content in plants of the diagnosed field; and - perform calibration of the obtained maps using ground-based measurement data of reference sites. 2. The method according to claim 1, characterized by the fact that at the stage of constructing maps of the vegetation index, at least one of the following indicators is selected, selected from the group: the number of elevated plant phytomass, seedling density.

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