UAVs are innovative equipment for monitoring fields that are free from a lot of the disadvantages of satellites such as availability, low cost, and high image resolution. However, the issues of quality, reproducibility, and suitability for crop management processes remain relevant. Now, the issue of assessing the suitability of the results of spectral monitoring of plantations in relation to the condition of plants has not been resolved. Since spectral monitoring is a necessary component in the concept of crop management, the development of a methodology for assessing the suitability of remote monitoring spectral data for the calculation of agrochemical practices was the purpose of the work. According to the publications, the dependence of the number of pixels on the values of the intensity of color components for plants and soil is described by the Gaussian distribution. Deviation from such distribution is caused by the imposing of distributions from various objects fixed on a photo. The experimental test was carried out on the basis of wheat, using the results obtained during 2017-2020 when considering the stresses of nutrient deficiency and technological nature. The investigation found experimental evidence that the pixel distribution of plantations on the example of the wheat crop is described by the Gaussian distribution. It was found that the analysis of the correspondence of the nature of the distribution on the spectral channels, namely the presence of several max peaks that affects the value of the distribution maximum may indicate the presence of foreign inclusions or a transitional stage of vegetation. The suitability of the data can be assessed on the basis of the reference values of the width of the distribution on the spectral channels. Vegetation indices GNDVI and RNDVI were unsuitable for assessing the suitability of the data based on the parameters of the pixel distribution of the image in the experimental plots. This determines the feasibility of introducing in the sets of regular vegetation indices of geographic information systems additional packages that reflect the spectral channels.
UAVs are innovative equipment for monitoring fields, which are deprived of a number of fundamental shortcomings of satellites in terms of availability, cost, image resolution. However, the issues of quality, reproducibility, and suitability for crop management processes remain relevant. More often, designers focus on the improvement of spectral equipment, but there are also methodological problems in the perception and interpretation of information from devices of technical vision. Thus, most of the vegetation indices currently used to interpret UAV data, such as NDVI, were developed for satellite platforms with their inherent low image resolution when each pixel had a group of plants. The indices developed on the basis of the soil line concept were primarily intended to assess the availability of biomass, and crop management issues require other methodological approaches to crop monitoring. It should be borne in mind that the implementation of agrochemical measures, in particular fertilization should be carried out only at certain stages of the growing season. However, the state of plant development is determined by many factors, including the state of mineral nutrition, water supply, etc., so within one field there may be a situation when the plants are at different stages of the growing season. Accordingly, in such situations, the calculation of the mean value over the site, which is inherent in satellite solutions, is erroneous. At present, the issue of assessing the suitability of the results of spectral monitoring of plantations in relation to the condition of plants has not been resolved. Since spectral monitoring is a necessary component in the concept of crop management, the development of a methodology for assessing the suitability of remote monitoring spectral data for the calculation of agrochemical practices was the purpose of our work.
The spectral performance of objects critically
depends on the state of illumination, and the
reproducibility of data is tried to ensure by a
combination of technical and organizational
measures. The work of Helge Aasen and others
(2015) in [
Another approach is based on the use of
reference values of plant spectral indicators to
identify the spread of forest pests described in
PerOla Olsson and others (2016) in [
An original approach to the identification of
plants in terms of changes in their dimensions on
the example of sugar beet is shown in the work of
Yang Cao Liu and others (2020) in [
Spectral indicators of plants, even those that
are in the same stage of the growing season have
some differences. To obtain the average value for
plants when fixing the soil in a photograph,
Yaokai Liu et al. (2012) in [
Thus, based on the analysis of the literature, we can conclude that the dependence of the number of pixels on the values of the intensity of the color components for plants and soil is described by the Gaussian distribution. Deviation from such distribution is caused by the imposing of distributions from various objects fixed on a photo. However, experiments were performed in hospitals where the plants were in one phase of the growing season in the air-dry state of the soil, respectively, it is advisable to check the suitability of the method and in moist soil. 3. Materials and research software and hardware
The research was carried out on the basis of wheat, using the results obtained during 20172020. Stresses due to lack of nutrients were studied in the fields of the long-term stationary experiment of the Department of Agrochemistry and Plant Quality of NULES of Ukraine, where fertilizer application systems are studied. Technological stresses were studied on and in the production fields of farms in the Kyiv region. In fig. Green Chlorophyll index distribution maps are presented (Fig. 1).
The experiments were performed in the optical
range using a standard UAV camera DJI Phantom
3+. A description of the methodology of
experimental research was covered in the work of
V. Lysenko and others (2017) in [
In fig. 2 shows the results of calculations for the red component for experimental data obtained on 2017.05.05 in studies of the impact on the spectral indicators of the state of mineral nutrition using a universal camera FC200 (a standard tool for UAV DJI Phantom 3).
1000 800 s itpo 600 n f o r e bm400 u N 200 0
All xc=116, w=25; Max1 xc=118, w=22;
Max2 xc=64, w=23. 0 20 40 60 80 100 120 140 160 180 200
Red color intensity Figure 2: The results of the approximation of the dependence of the number of pixels on the intensity of the red component of color (05.05.2017)
As can be seen from the above data when using the proposed method, it was found that the value of the maximum distribution shifted by 2 units, while reducing the width w by 3 units. The presence of the Max2 distribution can be explained both by the presence of shadow on the lower and upper leaves and by the fixation of the soil.
The proposed approach to the processing of experimental results will be effective if the condition Max1≫ Max2 is satisfied. In practice, a situation is possible when plants of the same crop are in the field at the same time, but at different stages of the growing season or in a fundamentally different physiological state, such as the appearance of a flag leaf, which was recorded on 06.08.2018. According to the presented in fig. 3 data Max1≅Max2, so the approach was used when at the first stage separately determined separately 2 Gaussian distributions, after which the calculations were carried out according to the method proposed in section 3.
All xc=170, w=28; Max1 xc=156, w=16; Max2 xc=193, w=12; Max3 xc=118, w=17;
Max1+Max2+Max3 600 500 tsn400 i o p fro300 e b m u200 N 100 0 20 40 60 80 100 120 140 160 180 200 220 240 260
Red color intensity Figure 3: The results of approximation of the dependence of the number of pixels on the intensity of the red component of the color for winter wheat (2018.06.08 - there is a flag sheet)
Detection of the presence of several individual maxima can be done based on the magnitude of the distribution when using to approximate the experimental data. For the presented data, the value was 28 while in the remaining sections was 18… 23.
Based on the obtained results, the results obtained by approximating all the data by a single Gaussian dependence (All) are incorrect because they do not correspond to any of the distribution maxima. That is, monitoring was performed when the plants were in a transitional state and monitoring should be repeated after a few days when the vast majority of plants in the field are in a single stage of vegetation. For automatic processing of monitoring results, reference values for distribution parameters can be obtained in stationary experiments, etc.
For universal digital cameras in the optical range, such as FC200, strict compliance with the selectivity of light filters is not required, so to verify the results, a study was conducted using a specialized spectral complex Slantrange 3. The results of mineral nutrition studies are presented in Fig. 4.
G Fertilizers MIN w=7.1, R2=0,98; G Fertilizers MAX w=3.6, R2=1; R Fertilizers MIN w=18, R2=0.84;
R Fertilizers MAX w=9.8, R2=0.98; 3000 2500 lse2000 x i p f ro1500 e b m uN1000 500 0 20 40 60 80 100
Spectral channel Figure 4: Dependence of the number of pixels on the value of the intensity of the green (G) and red (R) components of the color and the wall of mineral nutrition at a dose of mineral fertilizers (Fertilizers MAX) and without fertilizers (Fertilizers MAX). Date of research 2020.04.27
When approximating the experimental data by the GaussAmp dependence, the distribution width for the green channel was 7.1 for plants under stress and 3.6 for healthy plants, respectively, at 0,98≤R2. For the red component, regardless of the state of mineral nutrition, the imposition of 2 maxima will be recorded, which were more pronounced in the absence of nutrients. Similarly to the green channel, the calculated distribution width in healthy plants was approximately twice less than in stress plants 9, 8 and 18, respectively. The coefficient of determination at 1.5 doses of mineral fertilizers was 0.98 and for affected plants 0.84.
The results of research on the technological stress caused by the action and aftereffect of herbicides from the predecessor culture were carried out in production fields near the village of Gvardiyske with the coordinates of lat. 50,0347 long. 30,0286 is presented in fig. 5.
According to the results obtained under stress conditions, the width of distribution on both the green and red channels is 1.5≤ times greater than in healthy plants. On the red channel, regardless of the presence of technological stresses, 2 pronounced maxima of distribution were not observed, in contrast to the results in Figs. 4, regardless of the channel, the coefficient of determination is 0.98≤R2. According to the authors, the difference in plant development is explained by the difference in climatic factors due to the location of the plots, so the production field is protected by dense forest strips in contrast to the used area of the experimental hospital.
260 240 220 200 180 s lxe160 ifp140 ro120 e b100 m uN80 60 40 20 0
G Teh.str. +, w=8.4; G w=5.5; R Teh.str. +, w=9; R w=5,6.
50
100
vegetation indices) maps of
Since the experimental plots with different fertilizer contents of the stationary experiment have a relatively small width of 5 meters for remote sensing using a UAV, the results obtained from the Slantview software map window were used for the research. The obtained results are shown in Fig. 6 for stresses caused by the state of mineral nutrition and technological stresses, respectively.
Based on the data obtained for the distribution of the NDVI index, there is a difference in the distribution of spectral channels. Thus, the width of the distribution regardless of the nature of stress in stress plants was similar or even smaller than in healthy plants. The coefficient of determination was 0.85-0.95, it was much lower than in the green and red spectral channels.
GNDVI Fertilizers MIN, w=0,005; GNDVI Fertilizers MAX, w=0,010; RNDVI Fertilizers MIN, w=0,01;
RNDVI Fertilizers MAX, w=0,01. 800 700 600 s lxe500 i p fro400 e bm300 u N200
Data suitability can be assessed on the basis of spectral channel width reference values.
Vegetation indices GNDVI and RNDVI were unsuitable for assessing the suitability of data based on the parameters of the pixel distribution of the image in the experimental plots. This determines the feasibility of introducing in the sets of regular vegetation indices of geographic information systems additional packages that reflect the spectral channels.
0 0,64 0,66 0,68 0,70 0,72 0,74 0,76 0,78 0,80 0,82 0,84 0,86 Vegetation indexes (NDVI)
RNDVI RNDVI Teh.stress +
GNDVI GNDVI Teh.stress + 0 0,65 0,70 0,75 0,80 0,85 0,90
Vegetation indexes (NDVI) Figure 6: Dependence of the number of pixels on the value of the variant of the vegetation index GrennNDVI (GNDVI) and RedNDVI (RNDVI) at stresses caused by lack of mineral nutrition (upper) and technological nature (lower)
The study found experimental evidence that the pixel distribution of plantations on the example of wheat crops is described by the Gaussian distribution.
Analysis of the conformity of the nature of the distribution along the spectral channels, namely the presence of several maxima that affect the value of the maximum distribution may indicate the presence of foreign inclusions or a transitional stage of vegetation.