CN110069895B - Method for establishing winter wheat nitrogen content full-growth period spectrum monitoring model - Google Patents
Method for establishing winter wheat nitrogen content full-growth period spectrum monitoring model Download PDFInfo
- Publication number
- CN110069895B CN110069895B CN201910416807.6A CN201910416807A CN110069895B CN 110069895 B CN110069895 B CN 110069895B CN 201910416807 A CN201910416807 A CN 201910416807A CN 110069895 B CN110069895 B CN 110069895B
- Authority
- CN
- China
- Prior art keywords
- sdr
- nitrogen content
- edge
- spectrum
- winter wheat
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a method for establishing a spectrum monitoring model of winter wheat at a nitrogen content full growth period. According to the correlation between the spectral reflectivity of the winter wheat canopy and the nitrogen content of the plant canopy in each growth period under different nitrogen application levels, the spectral information basic transformation and the correlation between the trilateral parameters and the nitrogen content of the plant canopy, the invention provides a suitable model combination which considers the characteristics of each growth period and has higher precision, constructs a main component estimation model of the nitrogen content of the plant canopy which integrates a plurality of independent variables such as the spectral information basic transformation, the trilateral parameters and the like, breaks through the limitation of the spectrum monitoring growth period and the influence of other background noises, and provides theoretical basis and technical support for the hyperspectral accurate diagnosis of the nitrogen content of the winter wheat canopy in the whole growth period.
Description
Technical Field
The invention relates to the technical field of agricultural planting, in particular to a method for establishing a spectrum monitoring model of winter wheat at a nitrogen content full growth period.
Background
Nitrogen is a main nutrient element of wheat, and has extremely important influence on the vital activity of wheat and the yield and quality of wheat; therefore, the method has important theoretical and practical significance for monitoring the nitrogen fertilizer condition of crops in real time in a large scale, improving the quality of winter wheat, realizing sustainable utilization of land and ensuring the quality and efficiency improvement of the crops. The physiological and biochemical characteristic changes of the winter wheat and the spectral characteristic difference of the plant canopy caused by the shortage of the plant nitrogen make it possible to monitor the nitrogen content of the plant canopy by adopting a spectral technology. Compared with the traditional method for monitoring the nitrogen content in the plant canopy by destructive sampling, the spectral space-time resolution is greatly improved along with the rapid development of the spectral technology in recent years, and the technical support is provided for nondestructive monitoring of the hyperspectral information of crops.
In recent years, scholars at home and abroad carry out a great deal of research on crop nutrition state monitoring, and establish spectral estimation models of nitrogen content of different crops. Many research results are limited to the construction of a nitrogen content spectrum monitoring model of plants in a specific growth period; the spectral characteristics of the crops in different growth periods change remarkably, and whether a spectral model in a specific growth period is suitable for monitoring the nitrogen content of plants in the whole growth period is questionable. In order to facilitate operation, related scholars also develop comparison research on simulation effects of various models at different growth stages, and then propose a spectrum monitoring model suitable for nitrogen content of plants in the whole growth period, but the simulation precision and the applicability of the model in each growth period need to be further verified, so that the model is beneficial to application and popularization; due to the influence of the variation of factors such as physiological structures and growth characteristic indexes of plants in different growth periods, the spectral reflectivity in different growth periods also changes, the simulation precision of a specific full-growth-period spectral monitoring model in each growth period has larger difference, and particularly the monitoring precision of the whole growth period model needs to be improved urgently to meet the development requirements of crop nutrition diagnosis and growth monitoring technologies.
As spectral resolution increases, spectral information data is enriched, but background and noise interference also increases. Relevant researches show that the influence of background and noise can be reduced, the spectrum absorption and extraction characteristics are enhanced, and the model simulation precision is improved through the basic transformation of the reflection spectrum information, such as reciprocal, logarithm, differential and the like. Parameters such as three-edge position, amplitude, area and the like and information such as spectral index and the like constructed by the parameters can better reflect the spectral characteristics of the green vegetation, and the three-edge parameters are sensitive to the leaf area index, the nitrogen content and the chlorophyll content. Based on the method, the spectral parameters such as spectral transformation, trilateral parameters and the like are integrated, the method for establishing the spectral monitoring model of the winter wheat during the full growth period with nitrogen content is established, and the accurate and stable growth monitoring is ensured.
Disclosure of Invention
Aiming at the defects in the prior art, the method for establishing the spectrum monitoring model of the winter wheat nitrogen content full growth period breaks through the limitation of the plant canopy nitrogen content spectrum monitoring growth period and the influence of other background noises, and realizes the accurate and stable monitoring of the nitrogen content of crops in the growth period.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a method for establishing a spectrum monitoring model of winter wheat in a nitrogen content full growth period comprises the following steps:
s1, measuring the spectral reflectivity and the nitrogen content of the winter wheat canopy;
s2, constructing a spectrum parameter through the spectrum reflectivity;
the spectral parameters comprise a spectral transformation form and three-side parameters;
s3, performing correlation analysis on the nitrogen content of the winter wheat canopy, the spectrum transformation form and the three-edge parameters, selecting the spectrum parameters with obvious correlation in each growth period, performing principal component analysis on the spectrum parameters, respectively constructing a nitrogen content monitoring model of the comprehensive spectrum parameters in each growth period according to the principal component analysis result, and combining the nitrogen content monitoring models of the comprehensive spectrum parameters in each growth period to serve as a nitrogen content full-growth period spectrum monitoring model;
the growth period comprises an elongation-heading period, an ear-filling period and a filling-maturation period.
Further: the method for measuring the spectral reflectance in step S1 includes:
before each monitoring, a white board with the reflectivity of 1 is adopted for correction, a probe of the handheld ground object spectrometer is vertically downward and is 15cm away from the winter wheat canopy, three monitoring points are selected, the handheld ground object spectrometer is respectively adopted for monitoring the wheat plant canopy spectrum, and the average value of the spectrum monitoring results is used as the spectrum reflectivity of the winter wheat canopy.
Further: the measuring method of the SPAD value in the step S1 includes:
shearing the winter wheat plant after spectral monitoring, separating the stem, leaf and ear of the sheared winter wheat plant, deactivating enzyme in an oven at 105 deg.C for 20min, oven drying at 80 deg.C to constant weight, grinding the dried wheat plant into powder, and adding H2SO4-H2O2And (3) digesting the powder, and determining the nitrogen content of the powder by using a Kjeldahl apparatus, namely the nitrogen content of the winter wheat plant.
Further: the spectral transformation in step S2 is to transform the spectral reflectivity, including dividing by R450-750Divided by R930Reciprocal, logarithm of reciprocal, first order differential of reciprocal, logarithm, first order differential of logarithm, absorption depth 670nm and first order differential;
the R is450-750The average value of the reflectivity of the wave band of 450nm-750nm is shown in the specification930For the reflectivity value of 930nm band, the calculation formula of the absorption depth 670nm is as follows:
in the above formula, A560Spectral reflectance at 560nm as the starting point of the absorption feature, B670Spectral reflectance at 670nm of the center point of the absorption feature, C760Is the spectral reflectance at the absorption feature end point 760 nm;
further: the "three-edge" parameter in the step S2 includes a red edge amplitude Dr, a red edge position λ r, a blue edge amplitude Db, a blue edge position λ b, a yellow edge amplitude Dy, a yellow edge position λ y, a green peak amplitude Rg, a green peak position λ g, a red valley amplitude Rr, a red valley position λ v, a red edge area SDr, a blue edge area SDb, a yellow edge area SDy, (Rg-Rr)/(Rg + Rr), Rg/Rr, SDr/SDb, SDr/SDy, (SDr-SDb), (SDr-SDb)/(SDr + SDb), (SDr-SDy)/(SDr + SDy);
the red edge amplitude Dr is the maximum value in a first-order derivative spectrum in a red light range of 680-760 nm, the red edge position lambda r is the wavelength position corresponding to the red edge amplitude Dr, the blue edge amplitude Db is the maximum value in a first-order derivative spectrum in a blue light range of 490-530 nm, the blue edge position lambda b is the wavelength position corresponding to the blue edge amplitude Db, the yellow edge amplitude Dy is the maximum value in a first-order derivative spectrum in a yellow light range of 560-640 nm, the yellow edge position lambda y is the wavelength position corresponding to the yellow edge amplitude Dy, the green peak amplitude Rg is the maximum waveband reflectivity in a green light range of 510-560 nm, the green peak position lambda g is the wavelength position corresponding to the green peak amplitude Rg, the red valley amplitude Rr is the minimum waveband reflectivity in a red light range of 640-680 nm, and the red valley position lambda v is the wavelength position corresponding to the red valley amplitude Rr.
Further: the comprehensive spectral parameters of the nitrogen content monitoring model in the step S3 are selected by taking the correlation of the nitrogen content of the winter wheat canopy in each growth period, the corresponding spectral transformation form and the three-edge parameter as the standard, wherein the spectral parameters of the jointing-heading period comprise: first order differential of reciprocal, first order differential of logarithm, first order differential of logarithm of reciprocal, division by R450-750(SDr-SDb)/(SDr + SDb), by R930(SDr-SDb), original reflectance; the spectral parameters of the heading-filling period include: first order differential of logarithm of reciprocal, first order differential, yellow side area SDy, first order differential of logarithm, division by R930First order derivative of reciprocal, yellow edge amplitude Dy, red edge area SDr, (SDr-SDb); the spectral parameters of the grouting-maturity period include: first order differential of logarithm of reciprocal, (SDr-SDy)/(SDr + SDy), yellow edge area SDy, yellow edge amplitude Dy, first order differential, green peak position λ g, first order differential of reciprocal, first order differential of logarithm, SDr/SDy.
Further: the nitrogen content monitoring model in the step S3 is:
in the above formula, Y is the nitrogen content of winter wheat canopy, M1Is the first main component of the jointing-heading stage, M2Is the second main of jointing-heading stageComponent (A) M3Is the third main component of the jointing-heading stage, N1Is the first main component in heading-grouting period, N2Is the second main component in heading-grouting stage, N3Is the third main component in heading-grouting stage, L1Is the first main component of the grouting-maturation stage, L2A second main component of grouting-maturation stage, L3A third main component of grouting-maturation stage;
M1＝0.3480X1+0.0327X2+0.3231X3+0.3504X4+0.3453X5-0.4066X6+0.3970X7-0.2800X8+0.3608X9
M2＝-0.0539X1+0.8906X2-0.3320X3-0.1842X4+0.1823X5-0.0207X6+0.0796X7-0.1253X8+0.0647X9
M3＝0.0586X1-0.0821X2-0.1287X3-0.3165X4+0.0956X5-0.1221X6+0.1850X7+0.7407X8+0.5159X9
in the above formula, X1Is the first derivative of the reciprocal, X2Is a first differential of a logarithm, X3Is a first order differential, X4Is the logarithmic first-order differential of the reciprocal, X5Is divided by R450-750，X6Is (SDr-SDb)/(SDr + SDb), X7Is divided by R930，X8Is (SDr-SDb), X9Is the original reflectivity;
N1＝0.3744Q1+0.4332Q2+0.3916Q3-0.0238Q4+0.3176Q5+0.0619Q6-0.2645Q7+0.4233Q8+0.4054Q9
N2＝0.1931Q1-0.0941Q2-0.2700Q3+0.5598Q4+0.3405Q5+0.3860Q6-0.4616Q7-0.1407Q8-0.2639Q9
N3＝0.1213Q1-0.0476Q2+0.1846Q3-0.4013Q4-0.2733Q5+0.8339Q6+0.0178Q7-0.1298Q8-0.0292Q9
in the above formula, Q1Is the logarithmic first-order derivative of the reciprocal, Q2Is first order differential, Q3Is the area of the yellow edge SDy, Q4First order differential of logarithm, Q5Is divided by R930，Q6Is the first derivative of the reciprocal, Q7Is the yellow edge amplitude Dy, Q8Red edge area SDr, Q9Is (SDr-SDb);
L1＝0.3114Z1+0.4177Z2-0.4406Z3+0.3619Z4-0.3196Z5+0.3456Z6-0.0617Z7-0.2584Z8+0.3334Z9
L2＝-0.0860Z1+0.2759Z2-0.1275Z3-0.0473Z4+0.4580Z5+0.2037Z6+0.4712Z7+0.5462Z8+0.3561Z9
L3＝0.2924Z1-0.1706Z2+0.1931Z3+0.3040Z4-0.1973Z5+0.1269Z6+0.7444Z7-0.0756Z8-0.3757Z9
in the above formula, Z1Is the logarithmic first-order differential of the reciprocal, Z2Is (SDr-SDy)/(SDr + SDy), Z3Is the area of the yellow edge SDy, Z4Is the yellow edge amplitude Dy, Z5Is a first order differential, Z6Is the green peak position λ g, Z7Is the first derivative of the reciprocal, Z8Is the first differential of a logarithm, Z9Is SDr/SDy.
The invention has the beneficial effects that: according to the method, the correlation between the spectral reflectivity of the winter wheat canopy and the nitrogen content of the plant canopy in each growth period under different nitrogen application levels is analyzed, the spectrum information basic transformation and the correlation between the trilateral parameters and the nitrogen content of the plant canopy are analyzed, the method provides a suitable model combination which considers the characteristics of each growth period and has higher precision, constructs a main component estimation model of the nitrogen content of the plant canopy integrating a plurality of independent variables such as the spectrum information basic transformation, the trilateral parameters and the like, breaks through the influence of the spectral monitoring growth period restriction and other background noises, and provides theoretical basis and technical support for the hyperspectral accurate diagnosis of the full growth period of the nitrogen content of the winter wheat canopy.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of spectral signatures of winter wheat plant canopy at different levels of applied nitrogen.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
The winter wheat variety is medium wheat 175, and the sowing and harvesting dates of the 2016-year-old and 2017-year-old winter wheat are respectively 2016-year-old, 10-month, 6-year and 2017-year-old, 6-month and 7-year-old; the sowing and harvesting dates of the 2017-year and 2018-year winter wheat are respectively 10 and 13 days in 2017 and 6 days in 2018. The test is provided with 15 cells in total, comprises 5 nitrogen fertilizers for treatment, and is applied for 2 times in the whole growth period, namely, a base fertilizer applied compound fertilizer before sowing (the compound fertilizer to be tested contains 15% of N and P)2O5Amount of 15% and K2O content 15%) and urea (N content 46%) applied during the green-turning period. The fertilizing amount is 0, 150, 225, 300 and 375kg/hm respectively2I.e., N1, N2, N3, N4, N5 treatments, each treatment set 3 replicates, with N3 treatment being at normal fertilization level. The irrigation level of each cell is kept consistent. Other field management measures are performed according to local general high-yield fields.
As shown in figure 1, the method for establishing the winter wheat canopy nitrogen content monitoring model based on the spectral parameters comprises the following steps:
s1, measuring the spectral reflectivity and the nitrogen content of the winter wheat canopy;
before each monitoring, a white board with the reflectivity of 1 is adopted for correction, a probe of the handheld ground object spectrometer is vertically downward and is 15cm away from the winter wheat canopy, three monitoring points are selected, the handheld ground object spectrometer is respectively adopted for monitoring the wheat plant canopy spectrum, and the average value of the spectrum monitoring results is used as the spectrum reflectivity of the winter wheat canopy. The Field-Spec hand-held Held2 type ground object spectrometer manufactured by American ASD company is selected, the effective wave band range of the ground object spectrometer is 325 and 1075nm, the sampling interval is 1nm, and the resolution is 3 nm. In the winter wheat growth period, selecting the situation of clear and cloudy or little cloudy for spectral measurement, wherein the measurement time is generally 10: preferably 00-12: 00.
The method for measuring the nitrogen content comprises the following steps: shearing the winter wheat plant after spectral monitoring, separating the stem, leaf and ear of the sheared winter wheat plant, deactivating enzyme in an oven at 105 deg.C for 20min, oven drying at 80 deg.C to constant weight, grinding the dried wheat plant into powder, and adding H2SO4-H2O2And (3) digesting the powder, and determining the nitrogen content of the powder by using a Kjeldahl apparatus, namely the nitrogen content of the winter wheat plant.
S2, constructing a spectrum parameter through the spectrum reflectivity;
the spectral parameters comprise a spectral transformation form and trilateral parameters;
the spectral transformation in step S2 is to transform the spectral reflectivity, including dividing by R450-750Divided by R930Reciprocal, logarithm of reciprocal, first differential of reciprocal, logarithm, first differential of logarithm, absorption depth 670nm and first differential, as shown in table 1;
the R is450-750The average value of the reflectivity of the wave band of 450nm-750nm is shown in the specification930For the reflectivity value of 930nm band, the calculation formula of the absorption depth 670nm is as follows:
in the above formula, A560Spectral reflectance at 560nm as the starting point of the absorption feature, B670Spectral reflectance at 670nm of the center point of the absorption feature, C760Is the spectral reflectance at the absorption feature end point 760 nm;
table 111 spectrum transformation form calculation formula
The "three-edge" parameters include red edge amplitude Dr, red edge position λ r, blue edge amplitude Db, blue edge position λ b, yellow edge amplitude Dy, yellow edge position λ y, green peak amplitude Rg, green peak position λ g, red valley amplitude Rr, red valley position λ v, red edge area SDr, blue edge area SDb, yellow edge area SDy, (Rg-Rr)/(Rg + Rr), Rg/Rr, SDr/SDb, SDr/SDy, (SDr-SDb), (SDr-SDb)/(SDr + SDb), (SDr-SDy)/(SDr + SDy), as shown in table 2;
the red edge amplitude Dr is the maximum value in a first-order derivative spectrum in a red light range of 680-760 nm, the red edge position lambda r is the wavelength position corresponding to the red edge amplitude Dr, the blue edge amplitude Db is the maximum value in a first-order derivative spectrum in a blue light range of 490-530 nm, the blue edge position lambda b is the wavelength position corresponding to the blue edge amplitude Db, the yellow edge amplitude Dy is the maximum value in a first-order derivative spectrum in a yellow light range of 560-640 nm, the yellow edge position lambda y is the wavelength position corresponding to the yellow edge amplitude Dy, the green peak amplitude Rg is the maximum waveband reflectivity in a green light range of 510-560 nm, the green peak position lambda g is the wavelength position corresponding to the green peak amplitude Rg, the red valley amplitude Rr is the minimum waveband reflectivity in a red light range of 640-680 nm, and the red valley position lambda v is the wavelength position corresponding to the red valley amplitude Rr.
TABLE 2 spectral characteristics as used herein
The nitrogen content of the canopy of the winter wheat under different nitrogen application levels is shown in tables 3 and 4, the nitrogen content of the canopy of the winter wheat is reduced along with the growth period of the winter wheat, wherein in the year of 2016 + 2017, the nitrogen content is reduced from the node-heading 3.1491 to the grouting-maturation stage 1.5432, and the reduction rate reaches 104.1%; in 2017 and 2018, the reduction from the joint-heading stage 3.0246 to the grouting-maturation stage 1.7374 reaches 74.1 percent. Under different nitrogen application levels, the nitrogen content of the winter wheat canopy also tends to increase along with the increase of the fertilizing amount. In the jointing-heading period, the difference between 2016 & lt- & gt 2017 treatments is not significant, and the difference between W1 treatment and other treatments in 2017 & lt- & gt 2018 treatments reaches an extremely significant level; in the heading-grouting period, the difference of the nitrogen content of the canopy between the treatment with W1 in the year of 2016 & lt 2017 & gt and other treatments reaches a significant level, and the difference of the nitrogen content of the canopy between the treatment with W1 in the year of 2017 & lt 2018 & gt and other treatments reaches a very significant level; in the grouting-maturation stage, 2016-2017 did not reach a significant level during treatment, while 2017-2018 reached a very significant level.
TABLE 32016 and 2017. the nitrogen content of the canopy of winter wheat at different nitrogen application levels
TABLE 42017 Nitrogen content of winter wheat canopy in 2018 year at different nitrogen application levels
The spectral characteristics of the canopy of the winter wheat plant under different nitrogen application levels are shown in figure 2, the change rules of the spectral characteristics of the canopy of the plant in different growth periods in each year are basically consistent, namely the spectral reflectivity in the visible light band is lower, and the spectral reflectivity in the near infrared band is higher; two obvious absorption valleys exist in a blue-violet light wave band (350nm-500nm) and a red light wave band (650nm-710nm), while an obvious reflection peak is formed in a green light wave band (500-600nm), mainly because chlorophyll has stronger absorption capacity on red light and blue light and weaker absorption on green light under the photosynthesis of crops; after 690nm, the spectral reflectance rises sharply, forming a highly reflective plateau, i.e., the so-called "red-edge phenomenon" occurs at 690-740 nm. As the growth period advances, especially in the near infrared band (760-. The spectral characteristics of the plant canopy are different due to different growth periods, the spectral estimation model is established by adopting the spectral monitoring data of the whole growth period, whether the requirement on the monitoring precision of each growth period can be met needs to be further verified, and otherwise, the model is used for carrying out spectral monitoring of a specific growth period, so that the expected effect is difficult to achieve. Under different nitrogen application levels in the same growth period, in the range of visible light wave bands, the reflectivity of the canopy of the winter wheat plant is reduced along with the increase of the fertilizing amount; in the near-infrared band range, the reflectivity of the canopy of the winter wheat plant tends to increase along with the increase of the fertilizing amount. Therefore, the spectral reflectivity of different fertilization processing in each growth period is obviously different, and the similar change rule of the corresponding canopy nitrogen content indirectly proves the feasibility of constructing the winter wheat plant canopy nitrogen content estimation model by adopting spectral reflection data.
The calculation formula of the correlation is as follows:
in the above formula, r is correlation, xiAnd yiIn order to be the actual value of the measurement,
and
the measured average value is obtained.
Taking the high correlation coefficient as a principle, obtaining the sensitive wave bands in different spectral transformation forms and corresponding correlation coefficients thereof as shown in table 5, wherein in the jointing-heading stage, the correlation corresponding to the reciprocal and the logarithm of the reciprocal reaches a significant level, and the correlation corresponding to the sensitive wave bands in other transformation forms reaches a very significant level; in the heading-filling period, except that the absorption depth of 670nm and the correlation corresponding to R450-750 are not at a significant level, the correlation corresponding to the sensitive wave band in other transformation forms is at a very significant level; in the grouting-maturation stage, the correlation corresponding to the original reflectivity, reciprocal, logarithm of reciprocal, logarithm and absorption depth 670nm does not reach a significant level, the correlation corresponding to the division by R450-750 and the division by R930 only reaches a significant level, and the correlation corresponding to the sensitive wave bands of the logarithmic first-order differential, the reciprocal first-order differential and the first-order differential of reciprocal all reach a very significant level. The low association between the filling and maturation phase compared to other growth phases is mainly due to the gradual senescence and withering of winter wheat and the gradual decrease of nitrogen content during this growth phase. Obtaining a transformation form of the top 3 of the specific growth period sequence by sequencing the absolute values of the correlation coefficients from high to low, wherein the jointing-heading period is reciprocal first-order differential, logarithmic first-order differential and first-order differential, the corresponding sensitive wave bands are 891nm, 891nm and 668nm respectively, and the absolute values of the correlation coefficients are 0.682, 0.641 and 0.629 respectively; the heading-grouting period is logarithmic first-order differential, first-order differential and logarithmic first-order differential of reciprocal, the corresponding sensitive wave bands are 541nm, 722nm and 565nm respectively, and the absolute values of correlation coefficients are 0.724, 0.682 and 0.547 respectively; the grouting-maturity period is logarithmic first-order differential, first-order differential and reciprocal first-order differential of reciprocal, the corresponding sensitive wave bands are 552nm, 850nm and 779nm respectively, and the absolute values of the correlation coefficients are 0.606, 0.494 and 0.441 respectively. In conclusion, the spectrum transformation with higher correlation coefficient with the nitrogen content of the canopy of the winter wheat plant in different growth periods and the corresponding sensitive wave bands are different, so that the sensitive wave bands and the correlation conditions thereof in the spectrum transformation modes in different growth periods need to be considered for constructing the model of the nitrogen content of the canopy of the winter wheat plant by adopting the spectrum transformation, and the simulation result can be ensured to be more reasonable.
TABLE 5 correlation coefficient between different spectrum transformation forms of winter wheat in different growth periods and nitrogen content of canopy
Taking the highest correlation coefficient as a principle, obtaining corresponding correlation coefficients of different trilateral parameters and nitrogen content of the winter wheat canopy, wherein the corresponding significant correlation differences of the different trilateral parameters in a specific growth period are larger, wherein in a joint-heading period, green peak amplitude, red valley amplitude, red edge area, blue edge area, (SDr-SDb), SDr/SDb and (SDr-SDb)/(SDr + SDb) reach significant correlation, red valley positions, Rg/Rr and (Rg-Rr)/(Rg + Rr) only reach significant levels, and other trilateral parameters do not reach significant levels; in the heading-filling period, the correlation corresponding to the red edge amplitude, the yellow edge amplitude, the red edge area, the yellow edge area, (SDr-SDb) and (SDr-SDy)/(SDr + SDy) all reach a very significant correlation level, while the correlation corresponding to other 'three-edge' parameters does not reach a significant level; in the grouting-maturation period, the yellow edge amplitude, the green peak position, the red edge area, the yellow edge area, (SDr-SDb), (SDr-SDb)/(SDr + SDb), (SDr-SDy)/(SDr + SDy) and SDr/SDy correspond to significant levels, and other three-edge parameters do not reach significant levels. Aiming at different growth periods, ranking from high to low according to absolute values of correlation coefficients to obtain three-edge parameters of the first 3 ranked in each growth period, wherein the jointing-heading period is (SDr-SDb)/(SDr + SDb), (SDr-SDb) and SDr/SDb, and the absolute values of the correlation coefficients are 0.517, 0.437 and 0.398 respectively; the heading-grouting period is yellow edge area, yellow edge amplitude and red edge area, and the absolute values of the correlation coefficients are 0.584, 0.520 and 0.518 respectively; the grouting-maturation period is (SDr-SDy)/(SDr + SDy), the yellow edge area and the yellow edge amplitude, and the absolute values of the correlation coefficients are 0.522, 0.518 and 0.514 respectively. In conclusion, the parameters of the three edges with higher correlation coefficients with the nitrogen content of the canopy of the winter wheat plant in different growth periods are different, and in order to ensure accurate and reasonable estimation results, the correlation difference change characteristics in the growth periods of the winter wheat plant canopy nitrogen content model are considered when the parameters of the three edges are adopted.
TABLE 6 correlation coefficient of three-edge parameters of winter wheat in different growth periods and nitrogen content of plants
S3, performing correlation analysis on the nitrogen content of the winter wheat canopy, the spectrum transformation form and the three-edge parameters, selecting the spectrum parameters with obvious correlation in each growth period, performing principal component analysis on the spectrum parameters, respectively constructing a nitrogen content monitoring model of the comprehensive spectrum parameters in each growth period according to the principal component analysis result, and combining the nitrogen content monitoring models of the comprehensive spectrum parameters in each growth period to serve as a nitrogen content full-growth period spectrum monitoring model;
the growth period comprises an elongation-heading period, an ear-filling period and a filling-maturation period.
The comprehensive spectral parameters of the nitrogen content monitoring model are selected by taking the correlation of the nitrogen content of the winter wheat canopy at each growth period and the corresponding spectral transformation form and the three-edge parameter as a standard, wherein the spectral parameters at the jointing-heading stage comprise: first order differential of reciprocal, first order differential of logarithm, first order differential of logarithm of reciprocal, division by R450-750(SDr-SDb)/(SDr + SDb), by R930(SDr-SDb), original reflectance; the spectral parameters of the heading-filling period include: first order differential of logarithm of reciprocal, first order differential, yellow side area SDy, first order differential of logarithm, division by R930First order derivative of reciprocal, yellow edge amplitude Dy, red edge area SDr, (SDr-SDb); the spectral parameters of the grouting-maturity period include: first order differential of logarithm of reciprocal, (SDr-SDy)/(SDr + SDy), yellow edge area SDy, yellow edge amplitude Dy, first order differential, green peak position λ g, first order differential of reciprocal, first order differential of logarithm, SDr/SDy.
According to the correlation coefficients of the key parameters of each birth time period, as shown in tables 7-9, the absolute values of the correlation coefficients among the parameters are all larger than 0.394 at the jointing-heading stage and reach extremely significant level; in the heading-grouting period, only the reciprocal first order differential and the yellow edge amplitude are not or only reach a significant level with a few parameters, and other parameters are extremely and significantly related; in the grouting-maturation period, only a few parameters are not or are not significant, and most parameters are extremely significant, so that multiple collinearity exists among key spectral parameters, a key parameter is selected to establish a model in a multiple regression mode, and the prediction accuracy of the multiple linear regression estimation model is reduced due to the information overlapping problem among the key factors.
TABLE 8 correlation coefficient between the parameters of the jointing-heading stage spectra
TABLE 9 correlation coefficient between spectral parameters during heading-grouting period
TABLE 10 correlation coefficient between grouting-maturity spectral parameters
Principal component analysis can eliminate collinearity existing between independent variables to improve the prediction accuracy of the model. The method is used for screening and determining key spectral parameters for carrying out principal component analysis aiming at each growth period of winter wheat, the number of the principal component factors of each key growth period is fixed to be 3, wherein the cumulative contribution rates of an elongation-heading period, an elongation-filling period and a filling-maturing period are respectively 85.2%, 82.4% and 80.1%, and the spectral parameters are selected and determined to represent more than 80% of comprehensive spectral information. On the basis, a canopy nitrogen content monitoring model of the comprehensive spectrum parameters of the winter wheat in the whole growth period is established as follows:
in the above formula, Y is the nitrogen content/(g.g) of winter wheat canopy-1)，M1Is the first main component of the jointing-heading stage, M2Is the second main component of the jointing-heading stage, M3Is the third main component of the jointing-heading stage, N1Is the first main component in heading-grouting period, N2Is the second main component in heading-grouting stage, N3Is the third main component in heading-grouting stage, L1Is the first main component of the grouting-maturation stage, L2A second main component of grouting-maturation stage, L3A third main component of grouting-maturation stage;
M1＝0.3480X1+0.0327X2+0.3231X3+0.3504X4+0.3453X5-0.4066X6+0.3970X7-0.2800X8+0.3608X9
M2＝-0.0539X1+0.8906X2-0.3320X3-0.1842X4+0.1823X5-0.0207X6+0.0796X7-0.1253X8+0.0647X9
M3＝0.0586X1-0.0821X2-0.1287X3-0.3165X4+0.0956X5-0.1221X6+0.1850X7+0.7407X8+0.5159X9
in the above formula, X1Is the first derivative of the reciprocal, X2Is a first differential of a logarithm, X3Is a first order differential, X4Is the logarithmic first-order differential of the reciprocal, X5Is divided by R450-750，X6Is (SDr-SDb)/(SDr + SDb), X7Is divided by R930，X8Is (SDr-SDb), X9Is the original reflectivity;
N1＝0.3744Q1+0.4332Q2+0.3916Q3-0.0238Q4+0.3176Q5+0.0619Q6-0.2645Q7+0.4233Q8+0.4054Q9
N2＝0.1931Q1-0.0941Q2-0.2700Q3+0.5598Q4+0.3405Q5+0.3860Q6-0.4616Q7-0.1407Q8-0.2639Q9
N3＝0.1213Q1-0.0476Q2+0.1846Q3-0.4013Q4-0.2733Q5+0.8339Q6+0.0178Q7-0.1298Q8-0.0292Q9
in the above formula, Q1Is the logarithmic first-order derivative of the reciprocal, Q2Is first order differential, Q3Is the area of the yellow edge SDy, Q4First order differential of logarithm, Q5Is divided by R930，Q6Is the first derivative of the reciprocal, Q7Is the yellow edge amplitude Dy, Q8Red edge area SDr, Q9Is (SDr-SDb);
L1＝0.3114Z1+0.4177Z2-0.4406Z3+0.3619Z4-0.3196Z5+0.3456Z6-0.0617Z7-0.2584Z8+0.3334Z9
L2＝-0.0860Z1+0.2759Z2-0.1275Z3-0.0473Z4+0.4580Z5+0.2037Z6+0.4712Z7+0.5462Z8+0.3561Z9
L3＝0.2924Z1-0.1706Z2+0.1931Z3+0.3040Z4-0.1973Z5+0.1269Z6+0.7444Z7-0.0756Z8-0.3757Z9
in the above formula, Z1Is the logarithmic first-order differential of the reciprocal, Z2Is (SDr-SDy)/(SDr + SDy), Z3Is the area of the yellow edge SDy, Z4Is the yellow edge amplitude Dy, Z5Is a first order differential, Z6Is the green peak position λ g, Z7Is the first derivative of the reciprocal, Z8Is the first differential of a logarithm, Z9Is SDr/SDy.
The evaluation indexes comprise a decision coefficient, a root mean square error and an average absolute error;
the determination coefficient R2The calculation formula of (2) is as follows:
the calculation formula of the root mean square error RMSE is as follows:
the calculation formula of the average absolute error MAE is as follows:
in the above formula, yiIs a measured value of yi' is a predicted value,
the measured average value is n, and the number of samples is n.
To determine the coefficient R2The root mean square error RMSE and the average absolute error MAE are evaluation indexes to comprehensively evaluate the winter wheat canopy nitrogen content monitoring model, as shown in table 11. The results show that R at the jointing-heading stage20.424, RMSE 0.249, MAE 0.198; r in heading-filling stage20.619, RMSE 0.315, MAE 0.244; r in grouting-maturation stage2It was 0.425, RMSE 0.190, and MAE 0.146. Therefore, the constructed canopy nitrogen content monitoring model of the comprehensive spectrum parameters of the winter wheat in the whole growth period has the determining coefficient R of nitrogen content monitoring in each growth period of the winter wheat2The root mean square error RMSE is less than 0.424, the root mean square error RMSE is less than 0.315, and the average absolute error MAE is less than 0.244, so the canopy nitrogen content monitoring model with the comprehensive spectrum parameters in the whole growth period constructed by the method has higher estimation precision in the whole growth period and each growth period, and can provide a basis for monitoring, diagnosing and fertilizing the canopy nitrogen content of the winter wheat.
TABLE 11 evaluation index of model
Claims (3)
1. A method for establishing a spectrum monitoring model of winter wheat at a nitrogen content full growth period is characterized by comprising the following steps:
s1, measuring the spectral reflectivity and the nitrogen content of the winter wheat canopy;
s2, constructing a spectrum parameter through the spectrum reflectivity;
the spectral parameters comprise a spectral transformation form and three-side parameters;
s3, performing correlation analysis on the nitrogen content of the canopy layer of the winter wheat, the spectrum transformation form and the three-edge parameter, selecting the spectrum parameters with obvious correlation in each growth period, performing principal component analysis on the spectrum parameters, respectively constructing a nitrogen content monitoring model of the comprehensive spectrum parameters in each growth period according to the principal component analysis result, and combining the nitrogen content monitoring models of the comprehensive spectrum parameters in each growth period to serve as a spectrum monitoring model of the total growth period of the nitrogen content of the canopy layer;
the growth period comprises an elongation-heading period, an heading-filling period and a filling-maturation period;
the spectral transformation in step S2 is to transform the spectral reflectivity, including dividing by R450-750Divided by R930Reciprocal, logarithm of reciprocal, first order differential of reciprocal, logarithm, first order differential of logarithm, absorption depth 670nm and first order differential;
the R is450-750The average value of the reflectivity of the wave band of 450nm-750nm is shown in the specification930For the reflectivity value of 930nm band, the calculation formula of the absorption depth 670nm is as follows:
in the above formula, A560Spectral reflectance at 560nm as the starting point of the absorption feature, B670Spectral reflectance at 670nm of the center point of the absorption feature, C760To absorbSpectral reflectance at characteristic end point 760 nm;
the "three-edge" parameter in the step S2 includes a red edge amplitude Dr, a red edge position λ r, a blue edge amplitude Db, a blue edge position λ b, a yellow edge amplitude Dy, a yellow edge position λ y, a green peak amplitude Rg, a green peak position λ g, a red valley amplitude Rr, a red valley position λ v, a red edge area SDr, a blue edge area SDb, a yellow edge area SDy, (Rg-Rr)/(Rg + Rr), Rg/Rr, SDr/SDb, SDr/SDy, (SDr-SDb), (SDr-SDb)/(SDr + SDb), (SDr-SDy)/(SDr + SDy);
the red edge amplitude Dr is the maximum value in a first-order derivative spectrum in a red light range of 680-760 nm, the red edge position λ r is the wavelength position corresponding to the red edge amplitude Dr, the blue edge amplitude Db is the maximum value in a first-order derivative spectrum in a blue light range of 490-530 nm, the blue edge position λ b is the wavelength position corresponding to the blue edge amplitude Db, the yellow edge amplitude Dy is the maximum value in a first-order derivative spectrum in a yellow light range of 560-640 nm, the yellow edge position λ y is the wavelength position corresponding to the yellow edge amplitude Dy, the green peak amplitude Rg is the maximum waveband reflectivity in a green light range of 510-560 nm, the green peak position λ g is the wavelength position corresponding to the green peak amplitude Rg, the red valley amplitude Rr is the minimum waveband reflectivity in a red light range of 640-680 nm, and the red valley position λ v is the wavelength position corresponding to the red valley amplitude Rr;
the comprehensive spectral parameters of the nitrogen content monitoring model in the step S3 are selected by taking the correlation of the nitrogen content of the winter wheat canopy in each growth period, the corresponding spectral transformation form and the three-edge parameter as the standard, wherein the spectral parameters of the jointing-heading period comprise: first order differential of reciprocal, first order differential of logarithm, first order differential of logarithm of reciprocal, division by R450-750(SDr-SDb)/(SDr + SDb), by R930(SDr-SDb), original reflectance;
the spectral parameters of the heading-filling period include: first order differential of logarithm of reciprocal, first order differential, yellow side area SDy, first order differential of logarithm, division by R930First order derivative of reciprocal, yellow edge amplitude Dy, red edge area SDr, (SDr-SDb);
the spectral parameters of the grouting-maturity period include: first order differential of logarithm of reciprocal, (SDr-SDy)/(SDr + SDy), yellow edge area SDy, yellow edge amplitude Dy, first order differential, green peak position λ g, first order differential of reciprocal, first order differential of logarithm, SDr/SDy;
the nitrogen content monitoring model in the step S3 is:
in the above formula, Y is the nitrogen content of winter wheat canopy, M1Is the first main component of the jointing-heading stage, M2Is the second main component of the jointing-heading stage, M3Is the third main component of the jointing-heading stage, N1Is the first main component in heading-grouting period, N2Is the second main component in heading-grouting stage, N3Is the third main component in heading-grouting stage, L1Is the first main component of the grouting-maturation stage, L2A second main component of grouting-maturation stage, L3A third main component of grouting-maturation stage;
M1＝0.3480X1+0.0327X2+0.3231X3+0.3504X4+0.3453X5-0.4066X6+0.3970X7-0.2800X8+0.3608X9
M2＝-0.0539X1+0.8906X2-0.3320X3-0.1842X4+0.1823X5-0.0207X6+0.0796X7-0.1253X8+0.0647X9
M3＝0.0586X1-0.0821X2-0.1287X3-0.3165X4+0.0956X5-0.1221X6+0.1850X7+0.7407X8+0.5159X9
in the above formula, X1Is the first derivative of the reciprocal, X2Is a first differential of a logarithm, X3Is a first order differential, X4Is the logarithmic first-order differential of the reciprocal, X5Is divided by R450-750，X6Is (SDr-SDb)/(SDr + SDb), X7Is divided by R930，X8Is (SDr-SDb), X9Is original toAn initial reflectance;
N1＝0.3744Q1+0.4332Q2+0.3916Q3-0.0238Q4+0.3176Q5+0.0619Q6-0.2645Q7+0.4233Q8+0.4054Q9
N2＝0.1931Q1-0.0941Q2-0.2700Q3+0.5598Q4+0.3405Q5+0.3860Q6-0.4616Q7-0.1407Q8-0.2639Q9
N3＝0.1213Q1-0.0476Q2+0.1846Q3-0.4013Q4-0.2733Q5+0.8339Q6+0.0178Q7-0.1298Q8-0.0292Q9
in the above formula, Q1Is the logarithmic first-order derivative of the reciprocal, Q2Is first order differential, Q3Is the area of the yellow edge SDy, Q4First order differential of logarithm, Q5Is divided by R930，Q6Is the first derivative of the reciprocal, Q7Is the yellow edge amplitude Dy, Q8Red edge area SDr, Q9Is (SDr-SDb);
L1＝0.3114Z1+0.4177Z2-0.4406Z3+0.3619Z4-0.3196Z5+0.3456Z6-0.0617Z7-0.2584Z8+0.3334Z9
L2＝-0.0860Z1+0.2759Z2-0.1275Z3-0.0473Z4+0.4580Z5+0.2037Z6+0.4712Z7+0.5462Z8+0.3561Z9
L3＝0.2924Z1-0.1706Z2+0.1931Z3+0.3040Z4-0.1973Z5+0.1269Z6+0.7444Z7-0.0756Z8-0.3757Z9
in the above formula, Z1Is the logarithmic first-order differential of the reciprocal, Z2Is (SDr-SDy)/(SDr + SDy), Z3Is the area of the yellow edge SDy, Z4Is the yellow edge amplitude Dy, Z5Is a first order differential, Z6Is the green peak position λ g, Z7Is one of reciprocalOrder differential, Z8Is the first differential of a logarithm, Z9Is SDr/SDy.
2. The method for establishing the winter wheat nitrogen content full-growth period spectrum monitoring model according to claim 1, wherein the method for measuring the spectrum reflectivity in the step S1 comprises the following steps:
before each monitoring, a white board with the reflectivity of 1 is adopted for correction, a probe of the handheld ground object spectrometer is vertically downward and is 15cm away from the winter wheat canopy, three monitoring points are selected, the handheld ground object spectrometer is respectively adopted for monitoring the wheat plant canopy spectrum, and the average value of the spectrum monitoring results is used as the spectrum reflectivity of the winter wheat canopy.
3. The method for establishing the spectrum monitoring model for the winter wheat nitrogen content full growth period according to claim 2, wherein the method for measuring the nitrogen content in the step S1 comprises the following steps:
shearing the winter wheat plant after spectral monitoring, separating the stem, leaf and ear of the sheared winter wheat plant, deactivating enzyme in an oven at 105 deg.C for 20min, oven drying at 80 deg.C to constant weight, grinding the dried wheat plant into powder, and adding H2SO4-H2O2And (3) digesting the powder, and determining the nitrogen content of the powder by using a Kjeldahl apparatus, namely the nitrogen content of the winter wheat plant.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910416807.6A CN110069895B (en) | 2019-05-20 | 2019-05-20 | Method for establishing winter wheat nitrogen content full-growth period spectrum monitoring model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910416807.6A CN110069895B (en) | 2019-05-20 | 2019-05-20 | Method for establishing winter wheat nitrogen content full-growth period spectrum monitoring model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110069895A CN110069895A (en) | 2019-07-30 |
| CN110069895B true CN110069895B (en) | 2021-06-01 |
Family
ID=67371032
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910416807.6A Active CN110069895B (en) | 2019-05-20 | 2019-05-20 | Method for establishing winter wheat nitrogen content full-growth period spectrum monitoring model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110069895B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110596049A (en) * | 2019-09-17 | 2019-12-20 | 云南农业大学 | Method for quickly measuring phosphorus content in tobacco leaves by spectrum |
| CN110596048A (en) * | 2019-09-17 | 2019-12-20 | 云南农业大学 | Method for quickly measuring potassium content in tobacco leaves by spectrum |
| CN112613338B (en) * | 2020-11-19 | 2023-11-24 | 南京农业大学 | Wheat leaf layer nitrogen content estimation method based on RGB image fusion characteristics |
| CN112287886B (en) * | 2020-11-19 | 2023-09-22 | 安徽农业大学 | Wheat plant nitrogen content estimation method based on hyperspectral image fusion map features |
| CN112881309A (en) * | 2021-02-06 | 2021-06-01 | 内蒙古农业大学 | Establishment method of potato leaf nitrogen detection model and detection method of potato leaf nitrogen |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1223436A1 (en) * | 2001-01-16 | 2002-07-17 | The Soil Company B.V. | Measurement of properties of a field for cultivation and determination of cultivation treatment parameters |
| CN101403689A (en) * | 2008-11-20 | 2009-04-08 | 北京航空航天大学 | Nondestructive detection method for physiological index of plant leaf |
| CN101430276A (en) * | 2008-12-15 | 2009-05-13 | 北京航空航天大学 | Wavelength variable optimization method in spectrum analysis |
| CN106485345A (en) * | 2016-09-06 | 2017-03-08 | 西北农林科技大学 | Cotton Gossypii time of infertility canopy SPAD value remote sensing appraising and appraising model construction method |
| CN107817223A (en) * | 2017-10-20 | 2018-03-20 | 华东理工大学 | The construction method of quick nondestructive real-time estimate oil property model and its application |
| CN107907490A (en) * | 2017-11-02 | 2018-04-13 | 北京师范大学 | Soil erosion EO-1 hyperion inversion method based on outdoor rainfall and indoor soil |
| CN109187441A (en) * | 2018-08-27 | 2019-01-11 | 中国水利水电科学研究院 | The construction method of summer corn nitrogen content monitoring model based on canopy spectrum information |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10108757B1 (en) * | 2011-08-15 | 2018-10-23 | Adaptrum, Inc. | System and method for spectrum usage databases for cognitive radio |
| CN106372592B (en) * | 2016-08-29 | 2019-07-16 | 中国农业科学院农业资源与农业区划研究所 | A kind of winter wheat planting area calculation method based on winter wheat area index |
| CN109752487A (en) * | 2018-11-29 | 2019-05-14 | 北京农业信息技术研究中心 | Wheat Leavess nitrogen content predictor method and device |
-
2019
- 2019-05-20 CN CN201910416807.6A patent/CN110069895B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1223436A1 (en) * | 2001-01-16 | 2002-07-17 | The Soil Company B.V. | Measurement of properties of a field for cultivation and determination of cultivation treatment parameters |
| CN101403689A (en) * | 2008-11-20 | 2009-04-08 | 北京航空航天大学 | Nondestructive detection method for physiological index of plant leaf |
| CN101430276A (en) * | 2008-12-15 | 2009-05-13 | 北京航空航天大学 | Wavelength variable optimization method in spectrum analysis |
| CN106485345A (en) * | 2016-09-06 | 2017-03-08 | 西北农林科技大学 | Cotton Gossypii time of infertility canopy SPAD value remote sensing appraising and appraising model construction method |
| CN107817223A (en) * | 2017-10-20 | 2018-03-20 | 华东理工大学 | The construction method of quick nondestructive real-time estimate oil property model and its application |
| CN107907490A (en) * | 2017-11-02 | 2018-04-13 | 北京师范大学 | Soil erosion EO-1 hyperion inversion method based on outdoor rainfall and indoor soil |
| CN109187441A (en) * | 2018-08-27 | 2019-01-11 | 中国水利水电科学研究院 | The construction method of summer corn nitrogen content monitoring model based on canopy spectrum information |
Non-Patent Citations (2)
| Title |
|---|
| 冬小麦冠层光谱红边参数的变化及其与氮素含量的相关分析;卢艳丽;《遥感技术与应用》;20070228;第22卷(第1期);第1-7页 * |
| 基于高光谱的夏玉米冠层ＳＰＡＤ值监测研究;刘露;《水土保持学报》;20190228;第33卷(第1期);第353-360页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110069895A (en) | 2019-07-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110082300B (en) | Method for establishing winter wheat canopy water content monitoring model based on spectral parameters | |
| CN110069895B (en) | Method for establishing winter wheat nitrogen content full-growth period spectrum monitoring model | |
| CN109187441B (en) | Method for constructing summer corn nitrogen content monitoring model based on canopy spectral information | |
| CN110082309B (en) | Method for establishing SPAD value comprehensive spectrum monitoring model of winter wheat canopy | |
| CN110376167A (en) | Rice leaf nitrogen content monitoring method based on unmanned plane EO-1 hyperion | |
| CN113268923B (en) | Summer corn yield estimation method based on simulated multispectral | |
| CN107796764B (en) | Method for constructing wheat leaf area index estimation model based on three-band vegetation index | |
| CN110189793B (en) | Hyperspectrum-based wheat nitrogen fertilizer physiological utilization rate estimation model construction and wheat variety classification with different nitrogen efficiencies | |
| CN107505271B (en) | Plant nitrogen estimation method and system based on nitrogen component radiation transmission model | |
| CN111044516B (en) | Remote sensing estimation method for chlorophyll content of rice | |
| CN104502283A (en) | Two-band hyperspectral index and prediction model for estimating yield and shoot dry weight of soybean | |
| CN112287886B (en) | Wheat plant nitrogen content estimation method based on hyperspectral image fusion map features | |
| Peng et al. | Winter wheat canopy water content monitoring based on spectral transforms and “three-edge” parameters | |
| Brambilla et al. | Application of a low-cost RGB sensor to detect basil (Ocimum basilicum L.) nutritional status at pilot scale level | |
| CN112881327A (en) | Tobacco leaf SPAD value estimation method based on novel vegetation index | |
| CN112129709A (en) | Apple tree canopy scale nitrogen content diagnosis method | |
| CN115479902A (en) | Chlorophyll content determination model construction method based on wavelength and system thereof | |
| Yang | Nondestructive prediction of optimal harvest time of cherry tomatoes using VIS-NIR spectroscopy and PLSR calibration | |
| Feng et al. | Monitoring leaf pigment status with hyperspectral remote sensing in wheat | |
| CN115479903A (en) | Chlorophyll content measuring method and system based on wavelength | |
| CN113390797A (en) | Method for estimating total phosphorus content of pineapple leaves based on hyperspectral data | |
| CN115344997B (en) | Synergistic prediction method for leaf-canopy-pixel scale nitrogen concentration of summer maize plant | |
| Jang et al. | Estimation of Apple Leaf Nitrogen Concentration Using Hyperspectral Imaging-Based Wavelength Selection and Machine Learning | |
| CN113640230B (en) | Rapid detection method and system for field wheat moisture utilization rate | |
| CN113504186B (en) | Method for estimating utilization rate of nitrogen fertilizer in wheat by remote sensing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |