Comprehensive spectrum monitoring model building method for winter wheat

Abstract

The invention relates to the technical field of spectrum analysis, and particularly discloses a comprehensive spectrum monitoring model establishment method for winter wheat, and the method comprises the steps: selecting six key winter wheat growth indexes as the input of a model; obtaining winter wheat canopy spectral data through a spectral remote sensing technology; a factor analysis technology is introduced to process and analyze data of the six winter wheat growth vigor indexes; on the basis of a factor analysis result, constructing a winter wheat comprehensive growth vigor index; and in combination with the spectral data and the comprehensive growth vigor index, establishing a winter wheat comprehensive growth vigor spectral monitoring model based on a support vector machine, and realizing real-time monitoring and evaluation of the winter wheat comprehensive growth vigor.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral analysis, and more specifically, to a method for establishing a comprehensive spectral monitoring model for winter wheat. Background Art

[0002] In order to better implement refined management and quality control, winter wheat growth monitoring is particularly important. Spectral remote sensing technology has been widely used in winter wheat growth monitoring in the agricultural field due to its advantages such as rapidity, non-destructiveness, and wide coverage. Spectral data obtained through remote sensing can help evaluate the growth status of winter wheat and provide a reference for agricultural management. However, the existing spectral monitoring model is not accurate in practical applications and cannot accurately reflect the growth and health of winter wheat. This is mainly restricted by the following factors: 1. The ambiguity of spectral signals; 2. The factors that affect winter wheat growth are very complex, such as climate change and soil quality. A single growth parameter cannot accurately represent the growth information of winter wheat. Therefore, how to integrate the information of multiple growth indicators and accurately predict the growth and health of winter wheat has become a technical problem that needs to be solved urgently in this field.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for establishing a comprehensive spectral monitoring model for winter wheat, which is to use six key growth indicators as model input, use spectral remote sensing technology to obtain comprehensive spectral data of winter wheat canopy, introduce factor analysis technology to process and analyze the growth indicator data to extract potential factors affecting crop growth, construct a comprehensive growth index, effectively integrate multiple growth indicator information, combine the spectral data and the comprehensive growth index, and establish a winter wheat comprehensive growth spectral monitoring model based on a support vector machine, so as to overcome the ambiguity of spectral signals and the complexity of influencing factors, improve the accuracy of the spectral remote sensing monitoring model, and accurately predict the health value of winter wheat to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for establishing a comprehensive spectral monitoring model for winter wheat comprises the following steps: Step S1, selecting six key winter wheat growth indicators as inputs of the model; Step S2, obtaining winter wheat canopy spectral data by spectral remote sensing technology; Step S3, introducing factor analysis technology to process and analyze the data of six growth indicators; Step S4, constructing a comprehensive growth index based on the results of factor analysis; Step S5, combining the spectral data and the comprehensive growth index, establishing a winter wheat comprehensive growth spectral monitoring model based on a support vector machine to accurately predict the health value of winter wheat. The health index of winter wheat is used for field management. The formula of the comprehensive spectral monitoring model is: ; In the formula, is the final predicted health value of winter wheat, is the number of historical samples, For the The influence of the support vector on the final prediction result. For the The comprehensive growth index of samples, It is the comprehensive growth index of winter wheat at present. To adjust and The degree of influence on the final prediction results. is a bias term used to adjust the output so that the predicted value is closer to the actual winter wheat health value.

[0006] As a further solution of the present invention, six key winter wheat growth indicators are selected as inputs of the model, including the following specific contents: Six key growth indicators are selected to reflect the growth status of winter wheat and its environmental adaptability. The six key growth indicators include leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation; the leaf area index is an indicator that measures the ratio of plant leaf surface area to soil surface area, reflecting the photosynthetic capacity and overall growth status of the plant. The leaf area index is obtained by inverting spectral remote sensing data, and the canopy spectral information of winter wheat is collected using a multispectral sensor. The leaf area index is calculated by analyzing the reflectance data of different bands and combining with the vegetation index; the aboveground Dry biomass reflects the dry matter mass of the aboveground part of winter wheat. The aboveground biomass is estimated by the reflectance spectrum of the plant canopy. By using the correlation between vegetation index and aboveground biomass in remote sensing data, the biomass estimation formula can be obtained through statistical regression analysis, thereby realizing non-destructive real-time monitoring of aboveground dry biomass; aboveground fresh biomass refers to the fresh weight of the aboveground part of winter wheat before drying, reflecting the growth vitality and nutritional status of the plant. Through remote sensing monitoring, the aboveground fresh biomass can be inferred from spectral data. The estimation of aboveground fresh biomass is usually modeled in combination with other parameters such as moisture content. There is a strong relationship between the green band reflection intensity and the plant moisture content and biomass. The spectral signal can be linked to the aboveground fresh biomass through data processing and inversion models. The plant moisture content is an important indicator for measuring the moisture status of winter wheat, which directly affects the growth and yield of winter wheat. In spectral remote sensing, the monitoring of plant moisture content mainly depends on the reflection characteristics of the moisture absorption band. There is a strong correlation between the band and the moisture content of the plant, and the corresponding moisture information can be obtained by inverting remote sensing data. The chlorophyll density directly affects the photosynthesis efficiency of the plant. Chlorophyll absorbs light. The spectrum has obvious absorption peaks in the blue light region and the red light region, and has significant changes in the red light region, which provides a basis for remote sensing monitoring. After using the multi-spectral sensor to obtain the spectral reflectance data of the plant canopy, the chlorophyll density can be estimated through the inversion model; the nitrogen is an essential nutrient for plant growth. In remote sensing monitoring, the accumulation of nitrogen can be estimated through the relationship between the spectral characteristics of the plant and the nitrogen content. The red light band and the near-infrared light band in the spectral reflectance are more sensitive to the nitrogen content of the plant. By analyzing the changes in these bands and combining them with the existing nitrogen content data, the accumulation of nitrogen can be estimated.

[0007] As a further solution of the present invention, the spectral data of winter wheat canopy is obtained by spectral remote sensing technology, including the following specific contents: For the growth cycle of winter wheat, the collected spectral data covers different time nodes in order to capture the dynamic changes of winter wheat growth. In ground monitoring, the spectrometer used usually has multiple bands, which can cover visible light, near infrared and short-wave infrared regions. These bands can capture the reflectance spectral characteristics of the winter wheat canopy; in the application of drone or satellite remote sensing, the multispectral sensor carried can collect data over a larger range and at different scales, further improving the spatial resolution and temporal resolution of the data. The multispectral sensor usually collects data in multiple bands according to the characteristics of spectral reflectance to form a spectral data set; since the collected spectral data usually has noise, outliers or background interference, it is necessary to perform certain Preprocessing is performed to improve the quality of data and the accuracy of analysis results. The preprocessing includes: spectral smoothing and denoising, atmospheric correction, background noise removal and standardization. The spectral smoothing and denoising is to remove high-frequency noise through a smoothing algorithm to ensure the smoothness and continuity of the spectral signal. Remote sensing data is often affected by water vapor and aerosols when it is acquired. The atmospheric correction is used to invert and calibrate the remote sensing data through ground measured data. The background noise removal refers to removing the background noise through the difference value method. Since remote sensing data is affected by external factors such as climate and light, directly using data from different time periods or environmental conditions for comparative analysis does not have consistency. The standardization process is used to unify the spectral reflectance of different data sets into the same standard range.

[0008] As a further solution of the present invention, factor analysis technology is introduced to process and analyze the data of six winter wheat growth indicators, including the following specific contents: Since the growth of winter wheat is affected by a variety of factors, spectral data often contains complex information related to winter wheat growth, environmental conditions, etc. The factor analysis method can effectively extract these potential factors that are difficult to directly observe. The potential factors extracted by factor analysis can not only reflect multiple aspects of winter wheat growth, but also reveal the interactions and influence relationships between the multiple aspects; the potential factors include soil fertility, climate temperature and light environment, which will affect the growth of winter wheat, and the influence is manifested through the biophysical indicators of winter wheat.

[0009] As a further solution of the present invention, based on the results of factor analysis, a comprehensive growth index of winter wheat is constructed, including the following specific contents: By extracting six biophysical indicators, namely leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation, combined with factor analysis technology, the potential factors that have an important impact on the growth of winter wheat were extracted, and a comprehensive growth index was constructed based on each indicator. The formula of the comprehensive growth index is: ; In the formula, is the leaf area index, is the aboveground dry biomass, is the aboveground fresh biomass, is the water content of the plant, is the chlorophyll density, is the nitrogen accumulation.

[0010] The technical effects and advantages of the method for establishing a comprehensive spectral monitoring model for winter wheat of the present invention are as follows: The present invention selects six key growth indicators, namely leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation, as model inputs, uses spectral remote sensing technology to obtain multi-band and multi-time node winter wheat canopy spectral data and performs preprocessing, introduces factor analysis technology to process and analyze the growth indicator data to extract potential factors affecting crop growth, constructs a comprehensive growth indicator, combines the spectral data and the comprehensive growth indicator, and establishes a winter wheat comprehensive growth spectral monitoring model based on a support vector machine. The model has the advantages of being able to effectively integrate multiple growth indicator information, overcome the problems of spectral signal ambiguity and complex influencing factors, improve the accuracy of the spectral remote sensing monitoring model, and accurately predict the health value of winter wheat, thereby helping farmers to take field management measures in time to ensure the healthy and normal growth of winter wheat. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The present invention is a flow chart of a method for establishing a comprehensive spectral monitoring model for winter wheat. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] Example 1. See Figure 1 As shown in the flow chart, the embodiment of the present invention provides a method for establishing a comprehensive spectral monitoring model for winter wheat, which comprises the following steps: Step S1, selecting six key winter wheat growth indicators as inputs of the model; Step S2, obtaining winter wheat canopy spectral data by spectral remote sensing technology; Step S3, introducing factor analysis technology to process and analyze the data of six winter wheat growth indicators; Step S4, constructing a comprehensive growth index of winter wheat based on the results of factor analysis; Step S5, combining the spectral data and the comprehensive growth index to establish a winter wheat comprehensive growth spectral monitoring model based on a support vector machine.

[0014] In this embodiment, before data collection, the multispectral sensor is strictly calibrated to ensure its measurement accuracy, and the stability of the multispectral sensor is checked, and the initial state of the multispectral sensor is recorded. During the collection process, the multispectral sensor is regularly calibrated on-site, and the multispectral sensor parameters are adjusted according to a standard light source or a reference plate with known reflectivity. For abnormal data, relevant environmental factors and measurement conditions are recorded on-site in a timely manner to preliminarily judge their rationality. After data collection, data cleaning is performed to remove data points with obvious errors or missing data. Then, statistical methods are used to identify and process outliers. For suspicious data, it is decided whether to retain or modify it based on the data distribution characteristics and actual conditions. At the same time, a data review mechanism is established, and multiple people cross-check the data accuracy to ensure data reliability.

[0015] Furthermore, six key winter wheat growth indicators were selected as the input of the model, including the following specific contents: Six key growth indicators are selected to reflect the growth status of winter wheat and its environmental adaptability. The six key growth indicators include leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation; the leaf area index is an indicator that measures the ratio of plant leaf surface area to soil surface area, reflecting the photosynthetic capacity and overall growth status of the plant. The leaf area index is obtained by inverting spectral remote sensing data, and the canopy spectral information of winter wheat is collected using a multispectral sensor. The leaf area index is calculated by analyzing the reflectance data of different bands and combining with the vegetation index; The dry biomass reflects the dry matter mass of the aboveground part of winter wheat. The aboveground biomass is estimated by the reflectance spectrum of the plant canopy. By using the correlation between the vegetation index and the aboveground biomass in the remote sensing data, the biomass estimation formula can be obtained through statistical regression analysis, thereby realizing non-destructive real-time monitoring of the aboveground dry biomass; the aboveground fresh biomass refers to the fresh weight of the aboveground part of winter wheat before drying, reflecting the growth vitality and nutritional status of the plant. Through remote sensing monitoring, the aboveground fresh biomass can be inferred from spectral data. The estimation of the aboveground fresh biomass is usually modeled in combination with other parameters such as moisture content. Remote sensing data There is a strong relationship between the green band reflection intensity and the plant moisture content and biomass. The spectral signal can be linked to the aboveground fresh biomass through data processing and inversion models. The plant moisture content is an important indicator for measuring the moisture status of winter wheat, which directly affects the growth and yield of winter wheat. In spectral remote sensing, the monitoring of plant moisture content mainly depends on the reflection characteristics of the moisture absorption band. There is a strong correlation between the band and the moisture content of the plant, and the corresponding moisture information can be obtained by inverting remote sensing data. The chlorophyll density directly affects the photosynthesis efficiency of the plant. Chlorophyll absorption The spectrum has obvious absorption peaks in the blue and red light regions, and significant changes in the red light region, which provides a basis for remote sensing monitoring. After using the multi-spectral sensor to obtain the spectral reflectance data of the plant canopy, the chlorophyll density can be estimated through the inversion model; the nitrogen is an essential nutrient for plant growth. In remote sensing monitoring, the accumulation of nitrogen can be estimated through the relationship between the spectral characteristics of the plant and the nitrogen content. The red light band and the near-infrared light band in the spectral reflectance are more sensitive to the nitrogen content of the plant. By analyzing the changes in these bands and combining the existing nitrogen content data, the accumulation of nitrogen can be estimated.

[0016] In this embodiment, the field test layout and sample selection method of winter wheat are specifically as follows: the field test layout adopts a completely randomized block design, and multiple small repeated areas are set to reduce the errors caused by environmental factors such as soil fertility and topography. The area of ​​the area is determined according to the test scale and the growth characteristics of winter wheat to ensure that the plants have sufficient growth space and are easy to manage and operate; when selecting samples, first stratify according to factors such as winter wheat varieties and planting time to ensure that the growth conditions of plants in each layer are relatively consistent; then in each area, multiple sampling points are determined according to the five-point sampling method or the chessboard sampling method, and winter wheat plants with representative growth conditions are selected from each sampling point, covering different growth trends, avoiding the selection of extreme individuals, and ensuring that the sample can accurately reflect the growth of the entire field winter wheat population. At the same time, detailed information such as the location of the sample and the growth environment are recorded to provide a comprehensive and accurate basis for subsequent data analysis and model verification.

[0017] Furthermore, spectral remote sensing technology is used to obtain spectral data of winter wheat canopy, including the following specific contents: For the growth cycle of winter wheat, the collected spectral data covers different time nodes in order to capture the dynamic changes of winter wheat growth. In ground monitoring, the spectrometer used usually has multiple bands, which can cover visible light, near infrared and short-wave infrared regions. These bands can capture the reflectance spectral characteristics of the winter wheat canopy. In the visible light band, the chlorophyll in the leaves of winter wheat mainly absorbs blue light and red light for photosynthesis, and reflects more green light. Therefore, during normal growth, the green light band has a high reflectivity and the leaves are green. When the growth is poor, such as when nitrogen deficiency causes a decrease in chlorophyll, the absorption of blue and red light decreases, the reflection increases, and the reflection of green light decreases. The spectral curve changes, and the leaf morphology and structure affect light scattering and reflection. Thick and compact leaves have low reflectivity. In the near-infrared band, its reflectivity is mainly affected by the internal cell structure of the leaves. The mesophyll cells are closely arranged, with many layers and small gaps, which enhances multiple reflection scattering and has high reflectivity. It is closely related to the biomass of winter wheat. As the biomass increases, the reflectivity increases. For example, during the vigorous growth period of winter wheat, the reflectivity of this band is significantly higher than that of the seedling stage, which can be used to estimate the leaf area index and aboveground dry biomass. In the short-wave infrared band, it is sensitive to the moisture content of winter wheat plants, and has obvious moisture absorption characteristics. When the moisture content is high, the reflectivity at 1450nm and 1950n The m band has strong absorption and low reflectivity, which can be used to monitor the water content of plants. In the application of drone or satellite remote sensing, the multispectral sensor can be used to collect data in a larger range and at different scales, further improving the spatial resolution and temporal resolution of the data. The multispectral sensor usually collects data in multiple bands according to the characteristics of spectral reflectance to form a spectral data set. Since the collected spectral data usually has noise, outliers or background interference, it is necessary to perform certain preprocessing to improve the quality of the data and the accuracy of the analysis results. The preprocessing includes: spectral smoothing and denoising, atmospheric correction , removing background noise and standardization processing. The spectral smoothing and denoising is to remove high-frequency noise through a smoothing algorithm to ensure the smoothness and continuity of the spectral signal; remote sensing data are often affected by water vapor and aerosols when they are acquired. The atmospheric correction inverts and calibrates the remote sensing data through ground measured data; the background noise removal refers to removing background noise through the difference method; since remote sensing data is affected by external factors such as climate and light, directly using data from different time periods or environmental conditions for comparative analysis does not have consistency. The standardization processing is used to unify the spectral reflectances of different data sets into the same standard range.

[0018] Furthermore, factor analysis techniques were introduced to process and analyze the data of six winter wheat growth indicators, including the following specific contents: Since the growth of winter wheat is affected by a variety of factors, spectral data often contains complex information related to winter wheat growth, environmental conditions, etc. The factor analysis method can effectively extract these potential factors that are difficult to directly observe. The potential factors extracted by factor analysis can not only reflect multiple aspects of winter wheat growth, but also reveal the interactions and influence relationships between the multiple aspects; the potential factors include soil fertility, climate temperature and light environment, which will affect the growth of winter wheat, and the influence is manifested through the biophysical indicators of winter wheat.

[0019] Furthermore, based on the results of factor analysis, a comprehensive growth index of winter wheat was constructed, including the following specific contents: By extracting six biophysical indicators, namely leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation, combined with factor analysis technology, the potential factors that have an important impact on the growth of winter wheat were extracted, and a comprehensive growth index was constructed based on each indicator. The formula of the comprehensive growth index is: ; In the formula, is the leaf area index, is the aboveground dry biomass, is the aboveground fresh biomass, is the water content of the plant, is the chlorophyll density, is the nitrogen accumulation.

[0020] Furthermore, combining the spectral data and the comprehensive growth index, a winter wheat comprehensive growth spectral monitoring model based on a support vector machine is established, including the following specific contents: Combining the spectral data and the comprehensive growth index, a winter wheat comprehensive index spectral monitoring model based on support vector machine is established to accurately predict the health value of winter wheat. The health index of winter wheat is used for field management. The formula of the winter wheat comprehensive index spectral monitoring model based on support vector machine is: ; In the formula, is the final predicted health value of winter wheat, is the number of historical samples, For the The influence of the support vector on the final prediction result. For the The comprehensive growth index of samples, It is the comprehensive growth index of winter wheat at present. To adjust and The degree of influence on the final prediction results. is a bias term used to adjust the output so that the predicted value is closer to the actual health value of winter wheat; the final predicted health value of winter wheat can help identify whether the winter wheat is in a state of malnutrition, and farmers can scientifically adjust the amount of fertilizer according to the degree of decline in the health value; through the periodic monitoring of the final predicted health value of winter wheat, farmers can understand the growth trend of winter wheat in real time. If the health value rises steadily, it means that the winter wheat is growing normally. If the health value decreases, adjustment measures need to be taken; in addition, the final predicted health value of winter wheat can also provide information on the best time to apply fertilizer. If the final predicted health value of winter wheat decreases significantly during the jointing stage or the heading stage, it means that the winter wheat is growing weakly during the critical growth period. At this time, field management should be taken in time to effectively improve the growth vitality of winter wheat and ensure normal growth in the later period.

[0021] The present invention selects six key growth indicators, namely leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation, as model inputs, uses spectral remote sensing technology to obtain multi-band and multi-time node winter wheat canopy spectral data and performs preprocessing, introduces factor analysis technology to process and analyze the growth indicator data to extract potential factors affecting crop growth, constructs a comprehensive growth indicator, and then combines the spectral data and the comprehensive growth indicator to establish a winter wheat comprehensive growth spectral monitoring model based on a support vector machine. The model has the advantages of being able to effectively integrate multiple growth indicator information, overcome the problems of spectral signal ambiguity and complex influencing factors, improve the accuracy of the spectral remote sensing monitoring model, and accurately predict the health value of winter wheat, thereby helping farmers to take field management measures in time to ensure the healthy and normal growth of winter wheat.

[0022] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0023] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for establishing a comprehensive spectral monitoring model for winter wheat, characterized in that: The steps include: Step S1, selecting six key winter wheat growth indicators as inputs of the model; Step S2, obtaining winter wheat canopy spectral data by spectral remote sensing technology; Step S3, introducing factor analysis technology to process and analyze the data of six winter wheat growth indicators; Step S4, constructing a comprehensive growth index of winter wheat based on the results of factor analysis; Step S5, combining the spectral data and the comprehensive growth index, establishing a winter wheat comprehensive growth spectral monitoring model based on a support vector machine to accurately predict the health value of winter wheat. The health index of winter wheat is used for field management. The formula of the winter wheat comprehensive growth spectral monitoring model based on a support vector machine is: ; In the formula, is the final predicted health value of winter wheat, is the number of historical samples, For the The influence of the support vector on the final prediction result. For the The comprehensive growth index of samples, It is the comprehensive growth index of winter wheat at present. To adjust and The degree of influence on the final prediction results. is a bias term used to adjust the output so that the predicted value is closer to the actual winter wheat health value.

2. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 1, characterized in that: In step S4, by extracting the six biophysical indices of leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation, combined with factor analysis technology, potential factors that have an important impact on the growth of winter wheat are extracted, and a comprehensive growth index is constructed based on each index. The formula of the comprehensive growth index is: ; In the formula, It is a comprehensive growth indicator. is the leaf area index, is the aboveground dry biomass, is the aboveground fresh biomass, is the water content of the plant, is the chlorophyll density, is the nitrogen accumulation.

3. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 1, characterized in that: In step S1, six key winter wheat growth indicators are selected as inputs of the model. The six key winter wheat growth indicators include leaf area index, aboveground dry biomass, aboveground fresh biomass, plant water content, chlorophyll density and nitrogen accumulation, which are used as indicators reflecting the growth status of winter wheat and its environmental adaptability.

4. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 3, characterized in that: The leaf area index is obtained by inverting spectral remote sensing data, using a multispectral sensor to collect spectral information of the winter wheat canopy, analyzing reflectance data of different bands and calculating it in combination with the vegetation index.

5. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 3, characterized in that: The aboveground dry biomass is estimated by using the plant canopy reflectance spectrum, and the correlation between vegetation index and aboveground biomass in remote sensing data is used to obtain a biomass estimation formula through statistical regression analysis to achieve non-destructive real-time monitoring.

6. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 3, characterized in that: The aboveground fresh biomass is estimated by remote sensing monitoring, combined with water content parameter modeling, using the relationship between the green band reflection intensity of remote sensing data and the water content and biomass of the plants, and linking the data processing and inversion model with the spectral signal.

7. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 3, characterized in that: The plant water content is monitored by the reflectance characteristics of the water absorption band in spectral remote sensing, and the corresponding water information is obtained by inverting the remote sensing data through the correlation between the band and the plant water content.

8. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 3, characterized in that: The chlorophyll density is estimated by using an inversion model after obtaining the plant canopy spectral reflectance data using a multispectral sensor. Its absorption spectrum has obvious absorption peaks in the blue light region and the red light region. The significant changes in the red light region provide a basis for remote sensing monitoring.

9. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 3, characterized in that: The nitrogen accumulation amount is estimated by analyzing the relationship between the plant spectral characteristics and the nitrogen content, analyzing the changes in the red light band and the near-infrared light band in the spectral reflectance, and combining with the existing nitrogen content data for estimation.

10. The method for establishing a comprehensive spectral monitoring model for winter wheat according to claim 1, characterized in that: In step S2, comprehensive spectral data of the winter wheat canopy is obtained by spectral remote sensing technology, and the collected data covers different time nodes in the winter wheat growth cycle. Ground monitoring uses a spectrometer covering visible light, near infrared and short-wave infrared regional bands, and unmanned aerial vehicles or satellite remote sensing collect multi-band data by carrying multi-spectral sensors to form a spectral data set.

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