Crop Canopy Characteristics Research Articles

Analysis of the research progress on the deposition and drift of spray droplets by plant protection UAVs

Plant protection unmanned aerial vehicles (UAVs), which are highly adapted to terrain and capable of efficient low-altitude spraying, will be extensively used in agricultural production. In this paper, single or several independent factors influencing the deposition characteristics of droplets sprayed by plant protection UAVs, as well as the experimental methods and related mathematical analysis models used to study droplet deposition and drift, are systematically investigated. A research method based on farmland environmental factors is proposed to simulate the deposition and drift characteristics of spray droplets. Moreover, the impacts of multiple factors on the droplet deposition characteristics are further studied by using an indoor simulation test system for the spraying flow field of plant protection UAVs to simulate the plant protection UAVs spraying flow field, temperature, humidity and natural wind. By integrating the operation parameters, environmental conditions, crop canopy characteristics and rotor airflow, the main effects and interactive effects of the factors influencing the deposition of spray droplets can be explored. A mathematical model that can reflect the internal relations of multiple factors and evaluate and analyze the droplet deposition characteristics is established. A scientific and effective method for determining the optimal spray droplet deposition is also proposed. In addition, this research method can provide a necessary scientific basis for the formulation of operating standards for plant protection UAVs, inspection and evaluation of operating tools at the same scale, and the improvement and upgrading of spraying systems.

Resistance characteristics of broad-leaf crop canopy in air-assisted spray field and their effects on droplet deposition.

Air-assisted spray technology is widely applied in high-efficiency pesticide applications. The resistance characteristics of the crop canopy reflect its energy dissipation effect on the assisted airflow, connecting the structure of the crop canopy, assisted airflow velocity, and droplet deposition effect. Using a common broad-leaf crop canopy as the research object, the resistance characteristics of the crop canopy in the air-assisted field were investigated in this study by performing theoretical analysis and wind tunnel tests. Further, the feasibility of using the resistance characteristics of the crop canopy was assessed to evaluate its droplet deposition effect. The results showed that under the conditions of different number of leaf layers and initial leaf azimuth angles, the canopy pressure drop experiences a non-linear increasing trend with increasing assisted airflow velocity and that its regression function conforms to the Darcy–Forchheimer function. Moreover, when the initial azimuth angles of single- and multi-layer leaves were 90°–270°, the change rate of the canopy pressure drop with airflow velocity was 7–9 m/s, and there was a critical wind speed. However, with an increasing number of leaf layers in the crop canopy and changes in the initial leaf azimuth angle, the corresponding changes between the maximum canopy pressure drop and resistance coefficient were non-linear. Thus, it is proposed that the resistance characteristics of multi-layer leaves cannot be quantified as the results of the linear superposition of the resistance characteristics of several single-layer leaves—that is, it should be regarded as a whole research object. Combined with the analysis of the influence of the crop canopy resistance on droplet deposition, it is considered that when the crop canopy has multiple leaf layers in the airflow direction, the existing air-assisted spray technology cannot guarantee droplet deposition and canopy penetration simultaneously.

Responses of canopy characteristics and water use efficiency to ammoniated straw incorporation for summer maize (Zea mays L.) in the Loess Plateau, China

Straw application has a wide range of environmental benefits that make it an effective practice for sustainable agriculture. However, little is well known about the impacts of ammoniated straw addition on soil water and crop canopy characteristics in maize cultivation systems. A 3-year field experiment of summer maize (Zea mays L.) in Northwest China was conducted to evaluate the effects of different straw applications on soil water storage, maize canopy growth, grain yield and water use efficiency (WUE). The three treatments were: (i) no straw (traditional tillage, CK), (ii) straw mulch (traditional straw returning, T1), and (iii) ammoniated straw incorporated into the soil (optimized straw returning, T2). The T2 treatment increased average WUE by 14.8%, 7.8%, and 16.1% in 2016, 2017, and 2018, respectively, relative to the CK treatment. The 3-year average grain yield of the T2 treatment was 13.5% and 8.3% greater than grain yield of the CK and T1 treatments, respectively. Harvest index and 100-kernel weight of summer maize in the T2 treatment were higher than observed for the CK and T1 treatments. Across the three growing years, the T2 treatment was superior over the T1 and CK treatments in improving average LAI, spatial density of leaf area, green canopy cover and aboveground biomass. Hence, our study recommends that using ammoniated straw incorporation could be a promising application for synergistically improving maize growth, WUE and grain yield within this semi-arid region.

Radiation Dynamics on Crop Productivity in Different Cropping Systems

Global demand for food has always been on the increase due to the increase of the population in this world. Intercropping is one of the alternatives of agronomic practices that is widely practiced in ensuring food security and enhancing yield stability. Strip, mixed, and relay intercropping can be practiced to increase crop production. In addition to achieving a successful intercropping system, factors such as suitable crops, time of sowing, maturity of the crop, and plant density need to be considered before and during planting. Besides, practiced intercropping becomes a useful cropping system to increase efficient resource utilization, enhance biodiversity, promote soil health, enhance soil fertility, erosion control, yield advantage, weed, pest, and disease control, insurance against crop failure, ecosystem and modification of microclimate, market instability, and increase farmers income. Crop productivity in any types of cropping system implemented relies primarily on the interception of photosynthetically active radiation (PAR) of crop canopy and conversion of intercepted radiation into biomass or known as radiation use efficiency (RUE). Both PAR and RUE are important measurements that have significant roles in crop growth and development in which the accessibility of these radiation dynamics is connected with the leaf area index and crop canopy characteristics in maximizing yield as well as total productivity of the crop component in intercropping systems.

Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model.

Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content () and water content ()) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of mainly affected the reflectivity of visible region; the vertical profile of only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, and affected VIs estimation related to LAI, and respectively. The Root Mean Square Error (RMSE) of the new-proposed was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers.

Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications

Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications.

Trinexapac‐ethyl Affects Seed Production in Crimson Clover

Core Ideas Crimson clover canopy height was reduced with trinexapac‐ethyl applications. Seeds m–2 in crimson clover was increased by trinexapac‐ethyl applications. Seed yield responses to trinexapac‐ethyl were dependent on spring weather conditions. Seed weight reductions were inversely proportional to trinexapac‐ethyl rate. This is the first study to document effects of trinexapac‐ethyl on crimson clover seed crops. ABSTRACTTrinexapac‐ethyl (TE), a plant growth regulator (PGR) and lodging‐control agent, increases seed yield in red clover (Trifolium pratense L.), but the effects of TE are unknown in crimson clover (Trifolium incarnatum L.) seed crops. The objectives of this study were to determine TE application rate and timing effects on crop canopy characteristics, seed yield, and seed yield components of crimson clover. On‐farm trials were conducted at two locations (Banks and St. Paul, OR) in 2012 and 2013 and at the Hyslop Research Farm near Corvallis, OR, in 2015 and 2016. Treatments were TE PGR application rates (0, 105, 140, 210, 280, 420, and 560 g a.i. ha−1) and two application timings (stem elongation, Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie [BBCH] 32; inflorescence emergence, BBCH 51). Application of TE PGR reduced height of the seed crop canopy. Seed yield was increased in 2012 and 2013 with rates ≤280 g TE ha−1 when applied at the BBCH 32 timing. Seed yield increases attributable to TE ranged from 12 to 36%. However, no effects of TE application on seed yield were observed in 2015 and 2016, likely as a result of dry and warm spring weather. Seed yield increases in 2012 and 2013 were a result of TE‐induced increases in seeds m−2. Seed weight was reduced by TE application, and these reductions were inversely proportional to TE rate. Seed yield responses in crimson clover to TE PGR varied across years and locations, with seed yield increases occurring in wet and cool spring weather conditions.

High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging.

In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.

Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines.

Genetic improvement in sorghum breeding programs requires the assessment of adaptation traits in small-plot breeding trials across multiple environments. Many of these phenotypic assessments are made by manual measurement or visual scoring, both of which are time consuming and expensive. This limits trial size and the potential for genetic gain. In addition, these methods are typically restricted to point estimates of particular traits, such as leaf senescence or flowering and do not capture the dynamic nature of crop growth. In water-limited environments in particular, information on leaf area development over time would provide valuable insight into water use and adaptation to water scarcity during specific phenological stages of crop development. Current methods to estimate plant leaf area index (LAI) involve destructive sampling and are not practical in breeding. Unmanned aerial vehicles (UAV) and proximal-sensing technologies open new opportunities to assess these traits multiple times in large small-plot trials. We analyzed vegetation-specific crop indices obtained from a narrowband multi-spectral camera on board a UAV platform flown over a small pilot trial with 30 plots (10 genotypes randomized within 3 blocks). Due to variable emergence we were able to assess the utility of these vegetation indices to estimate canopy cover and LAI over a large range of plant densities. We found good correlations between the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) with plant number per plot, canopy cover and LAI both during the vegetative growth phase (pre-anthesis) and at maximum canopy cover shortly after anthesis. We also analyzed the utility of time-sequence data to assess the senescence pattern of sorghum genotypes known as fast (senescent) or slow senescing (stay-green) types. The Normalized Difference Red Edge (NDRE) index which estimates leaf chlorophyll content was most useful in characterizing the leaf area dynamics/senescence patterns of contrasting genotypes. These methods to monitor dynamics of green and senesced leaf area are suitable for out-scaling to enhance phenotyping of additional crop canopy characteristics and likely crop yield responses among genotypes across large fields and multiple dates.

Evaluation of wheat nitrogen status by colorimetric characteristics of crop canopy presented in digital images

The main factor responsible for non-uniform density of planting is the inequality of soil fertility in different parts of the field and above all the inequality of the content of available nutrients. The aim of our work is to study, in field conditions, the relationship between the nitrogen status of crops and colorimetric characteristics of their digital images and to develop a method of selecting uniform management zones for spatially differentiated fertilization. The field was photographed using a small radio-controlled aircraft equipped with a digital high resolution camera at the flight altitude of 500-700 m. The colour of crop images was characterized by parameters L, A and B of the CIELAB colour system. Changes in these parameters (?L, ?A and ?B), hue (?C) and saturation (?H) were calculated relative to their values at reference plots with different content of nitrogen in the soil. Dissimilarities in the colour of the crop canopy between reference plots and various sections of the field were determined from the value ?E that summarizes changes in the parameters L, A and B. To quantify nitrogen status of plants for spatially differentiated fertilization, regression relationships between colour characteristics of the canopy and the dose of nitrogen at test plots were used. The experiment performed has shown that the method we used to analyze colorimetric characteristics of crop canopy by its digital image, test plots with known nitrogen content in the soil including, is of considerable promise for quantitative evaluation of plant needs in nitrogen nutrients, management zones selection and spatially differentiated fertilization.

Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery

Abstract. Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations. This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R² value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.

AGDISP Sensitivity to Crop Canopy Characterization

As computer-based decision and modeling systems become increasingly integrated into agriculture, it is important that users understand the effects that various inputs have on these systems. The objective of the work presented here is to quantify the effects that different crop canopy characteristics, such as height and canopy closure, have on aerially applied spray deposition and downwind movement and compare these results to the predictions of spray movement and deposition by the AGDISP computer model. Six trials were conducted in cotton fields ranging from bare ground up to 1 m in height and canopy closure ranging from 0% to 100%. Horizontal deposition measurements were used to verify that each of the applications made during each of the trials produced similar levels of deposition. Monofilament strings were placed at four sampling heights (1, 2 ,4, and 6 m) above the canopy at a distance of 50 m from the downwind edge of the spray swath to measure the airborne droplets at this distance. The vertical deposition values at 50 m for crop heights between 0.3 and 0.8 m were comparable between the field-collected and AGDISP-predicted data. The AGDISP model (version 8.08) overpredicted by a factor of 2 the levels of deposition at 1 m for the trials conducted with crop canopy height of 0 or 1 m as compared to field measurements; however, at 4 and 6 m above the ground, AGDISP and the field data agreed well. Users of AGDISP should be encouraged by the accuracy of the model but are cautioned when using the model with canopies that are more than 80% closed.

The efficiency of crop recognition on ENVISAT ASAR images in two growing seasons

The aim of the presented project was to assess the efficiency of crop recognition based on microwave Advanced Synthetic Aperture Radar (ASAR) images acquired from ENVISAT-1. Investigations were conducted during two consecutive growing seasons, in 2003 and 2004. The agrometeorological conditions during the selected seasons differed markedly, which induced year-to-year variations regarding the relevant characteristics of crop canopy. Multitemporal series of ASAR alternating polarization images were used for crop differentiation. Classification was performed using a neural network classifier trained separately for each year. Field observations conducted in the western part of Poland supplied datasets for training, validation, and testing of the classifier. Despite some differences noted in the classifier performance on two datasets, the results obtained for 2003 and 2004 showed high mutual consistency.

Efficacy of Mechanical Cultivation with and without Herbicides in Broccoli (Brassica oleracea), Snap Bean (Phaseolus vulgaris), and Sweet Corn (Zea mays)

Five cultivation implements (two flex-tine cultivators, brush hoe, and rolling and shovel cultivators) were evaluated for weed control and effect on broccoli, snap bean, and sweet corn yield. Each implement was used alone or in combination with flex-tine cultivators. Standard broadcast herbicide treatments were: broccoli, metolachlor at 2.3 kg ai/ha posttransplant; snap bean, metolachlor preemergence (PRE) at 2.3 kg ai/ha followed by fomesafen postemergence (POST) at 0.4 kg ai/ha; sweet corn, atrazine at 1.1 kg ai/ha plus metolachlor at 2.3 kg ai/ha PRE. In the snap bean trials, an additional treatment consisted of metolachlor at 2.3 kg ai/ha PRE applied in a 25-cm band over the crop row followed by fomesafen POST at 0.4 kg ai/ha directed at the crop row, with a subsequent shovel cultivation. In the sweet corn trials, all cultivation treatments received an application of atrazine at 1.1 kg ai/ha plus metolachlor at 2.3 kg ai/ha PRE in a 25-cm band over the crop row. In broccoli, weed control and yields with all cultivation treatments were comparable to those with broadcast herbicide. In snap bean, flex-tine cultivators failed to control weeds and prevent yield reductions compared to the broadcast herbicide treatment. When either the brush hoe or the shovel cultivator was preceded by flex-tine cultivation, weed control and snap bean yield were comparable to those with broadcast herbicides. Banded herbicides followed by shovel cultivation also controlled weeds and prevented yield reductions. In sweet corn, banded herbicides controlled in-row weeds, and despite variable between-row weed control, all cultivation treatments yielded as well as the broadcast herbicide treatment. Relative performance of the cultivators was influenced by soil type and rainfall as well as crop canopy characteristics.

Detection and Evaluation of Plant Stresses for Crop Management Decisions

The ability to quantitatively assess crop conditions using remotely sensed data would not only improve yield forecasts but would also provide information that would be useful to farm managers in making day-to-day management decisions. Experiments were conducted using ground-based radiometers to relate spectral response to crop canopy characteristics. It was found that radiometrically measured crop temperature, when compared with a reference temperature, was related to the degree of plant stress and could indicate the onset of stress. Reflectance based vegetation indices, on the other hand, were not sensitive to the onset of stress but were useful in evaluating the consequences of stress as expressed in changing quantities of green phytomass. Anatomical and physiological changes occur within plant cells when plants are stressed and increase the amount of reflected radiation. However, canopy geometrical changes may alter the amount of radiation that reaches a radiometer, complicating the interpretation of spectral response to stress. Timeliness, frequency of coverage, andresolution are three factors that must be considered when satellite-based sensors are used to evaluate crop conditions for farm management applications.

Influence of wind on crop canopy reflectance measurements

Remote sensing techniques of measuring red and far-red crop canopy reflectance are frequently used to estimate crop canopy characteristics. The variability introduced in reflectance data from nonvegetative factors such as wind decreases the usefulness of the techniques. The objective of this study was to quantify and minimize the variability from wind on spectral reflectances. Red and far-red reflectances were acquired above wheat, barley, and alfalfa canopies throughout days of changing wind conditions. Periods of 312 s with little changes in irradiance values were used for the analysis. Wind had negligible effect on reflectances of a short canopy such as cut alfalfa, while it had a significant effect on reflectances from canopies with a higher vertical structure, particularly during gusty conditions. Within the windy and calm periods, extreme values of spectral reflectance differed by 60% and 12%, respectively, in the red, and by 40% and 8% in the far-red for the barley canopy. For the compact and dense canopy structure of alfalfa, these differences reached a maximum of 10% under windy conditions in both spectral regions. The plant canopy architecture, the wind conditions, and the spectral regions all affected the magnitude of the influence of wind on crop canopy spectral reflectances. The mean reflectance of a canopy overestimated the true reflectance by 2–4% while the use of the median reduced this overestimation. Sampling requirements for this sensor are evaluated, and the possibility of decreasing either the sampling rate or the sampling period is discussed.

Visible/Infrared/Microwave Agriculture Classification, Biomass, and Plant Height Algorithms

This paper describes the results of a study to determine if visible, infrared, and microwave data is correlated to crop-canopy characteristics (biomass and crop height) and can improve estimates of crop acreage. The objectives were to 1) determine if different crops can be discriminated using multifrequency microwave data, and 2) determine which visible, infrared, and microwave spectral regions can classify crops and correlate well to plant biomass, crop height, and the perpendicular vegetation index (PVI). The study was conducted at Dalhart, Texas, in 1980. Aircraft multispectral data collected during the study included visible and infrared data and active multifrequency microwave data. Ground-truth data from each field consisted of soil moisture, total plant biomass, and crop height. Results indicated that C- (4.75 GHz), L- (1.6 GHz), and P- (0.4 GHz) band active microwave data combined with visible and infrared data maintained or improved crop-discrimination accuracy compared to models using only visible and infrared data. The active microwave frequencies were more sensitive to plant height differences than total biomass differences; the K- (13.3 GHz) and C-bands were sensitive to height variations in short plants, while the P-band was sensitive to differences between tall and short plants.

Multispectral radiometer to measure crop canopy characteristics

A spectroradiometer has been designed for the study of crop reflectance characteristics in the spectral range of 400–1000 nm. Since the instrument records the ratio of incoming to reflected radiation the values obtained are independent of variations in solar elevation, and azimuth angle and atmospheric conditions. A filter wheel with four interchangeable interference filters is used for wavelength selection. The spectroradiometer traverses above a crop canopy on a movable track. This makes it possible to compare measurements from various locations several times in an hour, and to study more than one canopy a day. This instrument provides agronomists with data to estimate crop canopy characteristics such as leaf area index (LAI) rapidly and nondestructively. It also measures the variability of canopy reflectance introduced by temporal and spatial factors.

NEW METHOD OF QUANTITY ESTIMATION OF INTRA FIELD VARIABILITY BY OPTICAL CHARACTERISTICS OF SOWINGS FOR PRECISION FARMING

The scientific and technological progress in the agricultural production is based on the application of digital technologies of “smart agriculture” and precision farming, which is its key segment. Precision farming is a global trend of the adaptation of agricultural technologies for crop production to within-field spatial variability of crop formation conditions. It is necessary to quantify the degree of heterogeneity of these conditions and determine the boundaries of their variability in a given agricultural field. Based on the analysis of the available approaches to the within-field variability assessment, the paper substantiates the expediency of searching for new, more precise and inexpensive methods for solving this problem. Due to the large area of Russia, there is no alternative to the technology of remote sensing of the Earth in the information support of precision farming. The proposed new method of within-field heterogeneity detection is based on the use of optical criteria (reflection indexes), characterizing specific and non-specific features of spectral characteristics of crop canopy under the impact of various stress factors. Under controlled conditions, using a special spectrometer, a specific list of reflection indices and a set of quantitative indicators for each criterion was obtained to assess the physiological state of wheat under optimal conditions and nitrogen and water deficiency. The obtained results provide new possibilities for automating the process of interpretation of hyperspectral satellite images in order to identify and reveal the boundaries of the negative effects of stressors that inhibit the growth and development of crops. The relevant algorithmic scheme of the method implementation is considered in the paper.