Evaluation method for nitrogen fixation of leguminous plants based on near infrared spectrum

Abstract

The invention discloses an evaluation method for nitrogen fixation of leguminous plants based on near infrared spectroscopy, and particularly relates to a calculation formula processing method, which comprises the following steps: S01, based on to-be-tested samples of different selected plants, establishing a standard sample set of a PLSR model; s02, determining a target area detected by the to-be-detected sample, and obtaining spectral characteristics of the target area through a remote sensing image; s03, performing near infrared spectrum scanning on the standard sample set in the target area to obtain spectrum data; s04, combining the spectral characteristics with the spectral data to obtain a fusion set of the remote sensing image and the spectral data; and S05, acquiring the nitrogen content ([N]) and the nitrogen isotope ratio (delta15N) in the standard sample set so as to obtain chemical measurement data of the to-be-measured samples corresponding to the different plants. The method realizes efficient and non-destructive detection of the biological nitrogen fixation amount of leguminous plants, and has the advantages of simplicity and convenience in operation, rapidness in detection, reliable result and the like compared with traditional chemical reagents and cumbersome experimental steps.

Description

Technical Field

[0001] The invention relates to a computational processing method, and in particular to an evaluation method for nitrogen fixation of leguminous plants based on near infrared spectroscopy. Background Art

[0002] In agricultural production and ecosystem management, nitrogen is one of the key nutrients that directly affects plant growth and soil fertility. Leguminosae have a unique ability to fix nitrogen biologically. They can convert atmospheric nitrogen into nitrogen compounds that can be absorbed by plants through symbiotic microorganisms, providing an additional nitrogen source for the soil, thereby reducing dependence on chemical fertilizers. However, traditional nitrogen fixation determination methods, such as stable isotope ratio mass spectrometry (IRMS), usually require complex experimental processes, expensive instruments and equipment, and are time-consuming, limiting their application in large-scale agricultural and ecological monitoring.

[0003] Near infrared spectroscopy (NIRS) is a non-destructive, rapid, and reagent-free analytical method that is increasingly being used in agriculture and the environment. It measures the near infrared spectral reflectance of plant samples to infer the chemical composition of plants, such as nitrogen content and nitrogen isotope ratio. Although near infrared spectroscopy has shown good prospects in the detection of nitrogen fixation in legumes, the technology is usually limited to small-scale laboratory environments and is difficult to conduct large-scale, rapid regional assessments.

[0004] Remote sensing technology, especially spectral remote sensing, can efficiently obtain surface information over a large area, and has the advantages of large-scale monitoring and real-time data collection. Common remote sensing image data, such as the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and near-infrared reflectance, have been widely used to monitor vegetation coverage, health status, and growth. However, traditional remote sensing technology is usually difficult to directly obtain the specific nitrogen content and nitrogen fixation capacity of plants, and cannot fully consider the impact of different plant types and environmental factors on nitrogen fixation.

[0005] Existing technologies have not yet been able to effectively combine near-infrared spectral data and remote sensing image data to achieve efficient, accurate, and rapid assessment of the nitrogen fixation of legumes. Therefore, a new method combining near-infrared spectroscopy and remote sensing technology is urgently needed to quickly assess the nitrogen fixation capacity of legumes on a large scale while improving the accuracy and efficiency of the assessment. Summary of the invention

[0006] The purpose of the present invention is to provide a method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy, so as to solve the above problems.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for evaluating nitrogen fixation of leguminous plants based on near infrared spectroscopy, comprising the following steps:

[0008] S01. Establish a standard sample set of PLSR model based on selected samples of different plants to be tested;

[0009] S02, determining the target area for the sample to be tested, and obtaining the spectral characteristics of the target area through remote sensing images;

[0010] S03, performing near infrared spectroscopy scanning on the standard sample set in the target area to obtain spectral data;

[0011] S04, combining the spectral feature with the spectral data to obtain a fusion set of remote sensing images and spectral data;

[0012] S05, obtaining the nitrogen content ([N]) and nitrogen isotope ratio (δ15N) in the standard sample set to obtain chemical measurement data corresponding to the samples to be tested of the different plants;

[0013] S06, combining the corresponding spectral data and the chemical measurement data obtained by the samples to be tested of the same plant in the same time unit, and then combining the spectral features, and using partial least squares regression to perform multiple regression analysis, to construct a model between spectral reflectance and nitrogen source index, and then using Kennard–Stone algorithm to optimize and segment the data set to obtain an optimal model;

[0014] S07. Collect the spectral data of the samples of different plants and the spectral characteristics of the target area in sequence from the center to the periphery of the target area, and input them into the optimal model to calculate the nitrogen content ([N]) and the nitrogen source percentage (%Ndfa) of the plant samples in a certain range of quantities in the target area.

[0015] Preferably, the spectral feature includes any one of NDVI, EVI or near-infrared reflectivity.

[0016] Preferably, during the execution of steps S01 to S06, it is necessary to collect the spectral data and spectral features under four different humidity and temperature conditions: warming water supply, normal temperature water supply, warming drought and normal temperature drought.

[0017] Preferably, in the above step S02, the spectral range is 400 to 2400 nanometers, the spectral resolution is 0.5 nanometers, and the spectral data obtained is an average value of no less than two samples of the same plant to be tested.

[0018] Preferably, the nitrogen content ([N]) and the nitrogen isotope ratio (δ15N) in step S04 are obtained by a stable isotope mass spectrometer.

[0019] Preferably, in step S03, the data acquired by near-infrared spectral scanning needs to be subjected to first-order or second-order derivative, baseline correction or normalization to remove spectral noise and interference of the data.

[0020] Preferably, the standard sample set includes alfalfa and tall fescue as reference standards in addition to the monitored plants.

[0021] In the above technical scheme, the present invention provides a method for evaluating nitrogen fixation in leguminous plants based on near-infrared spectroscopy, which has the following beneficial effects: by fusing near-infrared spectral data with remote sensing image data, a nitrogen content and nitrogen fixation ability prediction model is established, thereby achieving efficient and non-destructive detection of nitrogen fixation in leguminous plants, which is suitable for plant nitrogen fixation assessment at different scales to meet the needs of agriculture and ecosystem management for rapid and accurate detection technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0023] Figure 1 VIP wavelength distribution diagram of plant nitrogen concentration and nitrogen isotopes under greenhouse conditions provided by an embodiment of the present invention;

[0024] Figure 2 VIP wavelength distribution diagram of plant nitrogen concentration and nitrogen isotopes under field conditions provided by an embodiment of the present invention;

[0025] Figure 3 Provide an evaluation diagram of the prediction effect of nitrogen isotopes and nitrogen content of plant samples for the embodiment of the present invention;

[0026] Figure 4 The present invention provides a proposed prediction diagram of the percentage and total amount of Ndfa in leguminous plants under different climatic conditions. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0028] Embodiment 1

[0029] The present invention aims to provide a method for evaluating nitrogen fixation of leguminous plants based on near infrared spectroscopy, comprising the following steps:

[0030] S01. Establish a standard sample set of PLSR model based on selected samples of different plants to be tested;

[0031] S02, determining the target area for the sample to be tested, and obtaining the spectral characteristics of the target area through remote sensing images;

[0032] S03, performing near infrared spectroscopy scanning on the standard sample set in the target area to obtain spectral data;

[0033] S04, combining the spectral features with the spectral data to obtain a fusion set of remote sensing images and spectral data;

[0034] S05. Obtaining the nitrogen content ([N]) and nitrogen isotope ratio (δ15N) in the standard sample set to obtain chemical measurement data of the samples to be tested corresponding to different plants;

[0035] S06. Combine the corresponding spectral data and chemical measurement data of the tested samples of the same plant obtained in the same time unit, and then combine the spectral features and use partial least squares regression to perform multiple regression analysis to build a model between spectral reflectance and nitrogen source indicators, and then use the Kennard–Stone algorithm to optimize the segmentation of the data set to obtain the optimal model;

[0036] S07. Spectral data of samples of different plants and spectral characteristics of the target area are collected from the center to the periphery of the target area, and input into the optimal model to calculate the nitrogen content ([N]) and nitrogen source percentage (%Ndfa) of plant samples in a certain range of numbers in the target area.

[0037] Furthermore, the spectral characteristics include any one of NDVI, EVI or near-infrared reflectance.

[0038] In the above embodiment, during the execution of steps S01 to S06, it is necessary to collect spectral characteristics and spectral data under four different humidity and temperatures, namely, warming water supply, normal temperature water supply, warming drought and normal temperature drought, that is, to calculate the nitrogen content ([N]) and the percentage of nitrogen source (%Ndfa) of the standard sample set under four temperature and humidity conditions.

[0039] It should be noted that, in addition to the monitored plants, the standard sample set in the above embodiment also includes alfalfa and tall fescue as reference standards, that is, each standard sample has a total of 278 samples, including 201 greenhouse samples, including 99 alfalfa samples and 102 tall fescue samples; and there are 77 standard samples in the field (monitored plants), and more than 278 samples were monitored without diseases and insect pests.

[0040] Furthermore, in step S02 of the above embodiment, the spectral range is 400 to 2400 nanometers, the spectral resolution is 0.5 nanometers, and the obtained spectral data is an average value of no less than two samples collected from the same plant.

[0041] Secondly, in step S04, the nitrogen content ([N]) and the nitrogen isotope ratio (δ15N) are obtained by a stable isotope mass spectrometer.

[0042] It should be noted that, in the above embodiment, the data obtained by near-infrared spectrum scanning in step S03 needs to be subjected to first-order or second-order derivative, baseline correction or normalization to remove spectral noise and interference of the data.

[0043] Furthermore, the spectral data analysis in the above embodiment adopts partial least squares regression (PLSR), and the model is optimized by fitting through the R package plantspec, and the data is divided into representative subsets using the Kennard–Stone algorithm, with the calibration set accounting for 80% and the rest for external verification. At the same time, the spatial extrapolation of the model is verified in combination with remote sensing data.

[0044] Experimental Example 1

[0045] The experiment was conducted in the laboratory of Western Sydney University, and the test materials were collected from alfalfa and tall fescue plant samples in the greenhouse and PACE outdoor facilities of Western Sydney University's Hawkesbury campus from 2018 to 2019.

[0046] Both greenhouse and field samples were subjected to four climate combination treatments, namely warming and water supply, normal temperature and water supply, warming and drought, and normal temperature and drought, with a total of 348 samples.

[0047] Cut and dry the plant stem and leaf samples that are free of pests and diseases.

[0048] The dried sample was ground through a 1 mm screen using a laboratory grinder (eg, Foss Cyclotec Mill, Denmark) to a homogeneous powder.

[0049] Load the ground sample into the sample cup or container for spectral analysis, ensuring that the sample is evenly distributed and free of air bubbles.

[0050] The samples were measured using a FOSS XDS fast content analyzer (FOSS Analytical, Hillerød, Denmark) equipped with XDS near-infrared technology, and 348 spectral data sets were collected, of which 278 were used for model construction and 70 were used for model optimization.

[0051] Figure 1 and Figure 2The VIP wavelength distribution of nitrogen concentration and nitrogen isotopes of legumes under greenhouse conditions and field conditions are shown respectively. The VIP curves show high VIP values ​​in the 400-700nm and 2000-2400nm wavelength regions, so the spectral range selected during model optimization was 400 to 2400 nanometers with a resolution of 0.5 nanometers. The samples were loaded and scanned twice, and the average spectrum was taken for calibration and prediction.

[0052] Spectral data (400–2400 nm) of plant samples were collected to evaluate the predictive power of their spectral reflectance for isotope ratio mass spectrometry (IRMS) chemical measurements, including nitrogen content [N] and nitrogen isotope ratio δ15N.

[0053] The model was established using partial least squares regression (PLSR) analysis, which evaluates the relationship between spectral data and target functional traits and optimizes model fit by compressing the prediction matrix into a small number of uncorrelated latent components.

[0054] The plantspec package ( Griffith and Anderson, 2019 ) was used in the R environment to optimize and fit the PLSR model based on the package of the pls package, and the Kennard–Stone algorithm was used to split the data into representative subsets to ensure data representativeness and model stability.

[0055] The chemical / spectral pair data of each PLSR model were divided into a calibration set and a validation set, with 80% used for the calibration set and 20% for external validation to ensure the generalization ability of the model.

[0056] The nitrogen content ([N]) and nitrogen isotope ratio (δ15N) of the samples were measured using a stable isotope mass spectrometer (IRMS).

[0057] The coefficient of determination (R) of the validation data set was used 2 ) and root mean square error (RMSE) were used to evaluate the accuracy of the model. The raw spectral data were subjected to various data transformations (such as first-order derivative, second-order derivative, linear subtraction, constant offset elimination, vector normalization, and maximum / minimum normalization), and the best fitting model was selected using the optimizePLS function. Table 1 shows the partial least squares regression (PLSR) model parameters and statistical indicators for predicting nitrogen isotopes (δ15N) and nitrogen concentrations ([N]) of legumes and grasses in the glasshouse and field experiments.

[0058] Among the optimized models, the PLSR models for δ15N and [N] of greenhouse sample alfalfa and δ15N of tall fescue produced R values ​​of 0.56, 0.48, and 0.58, respectively. 2The root mean square error (RMSE) of δ15N and [N] of field samples was 0.40, 0.29 and 0.71 respectively. 2 The values ​​were 0.94 and 0.99, and the RMSE were 0.44 and 0.08, respectively. The correlation R 2 is 0.54 and the slope is 0.84.

[0059] The R values ​​of the δ15N and [N] of alfalfa and the δ15N model of tall fescue in the greenhouse samples of the calibration model were 2 The values ​​were 0.77, 0.71 and 0.70, and the RMSE values ​​were 0.38, 0.28 and 0.69, respectively. The R 2 The values ​​are 0.93 and 0.99 respectively, and the RMSE values ​​are 0.40 and 0.09 respectively, as shown in the following table:

[0060]

[0061] Models of field data were applied for two species together, n, samplenumber; R 2 , coefficient of determination; RMSE, root mean squar error (i, a theuncertainty in optical spectrooopy predictions); Tot, total dataset; Cal, calibration dataset; Val, validation dataset.

[0062] Figure 3 Figure A shows the correlation between the predicted %Ndfa and the actual measured value, and the correlation coefficient (R 2 ) is 0.54, and the regression slope is 0.84. Although there are certain errors, the overall trend is close to the 1:1 line, indicating that the spectroscopic method has a certain accuracy in predicting the percentage of nitrogen fixation. Figure 3 Figure B shows the relationship between the predicted total Ndfa and the actual measured value, with a higher correlation coefficient (R 2 =0.94), the slope is 1.02, showing a high fitting accuracy, indicating that this method has good accuracy and stability in predicting the total amount of nitrogen fixation. The present invention also calculates the nitrogen content and nitrogen isotope ratio (δ 15 N) were analyzed, such as Figure 4 shown. Figure 4Figures A, B, and C show the predicted nitrogen isotope ratios (δ 15 The results showed that the correlation coefficients (R 2 ) were high and the root mean square error (RMSE) was low, indicating that the spectroscopic method is not only applicable to different plant types but also can accurately predict nitrogen isotope ratios and nitrogen contents.

[0063] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy, characterized in that: The following steps are involved: S01. Establish a standard sample set of PLSR model based on selected samples of different plants to be tested; S02, determining the target area for the sample to be tested, and obtaining the spectral characteristics of the target area through remote sensing images; S03, performing near infrared spectroscopy scanning on the standard sample set in the target area to obtain spectral data; S04, combining the spectral feature with the spectral data to obtain a fusion set of remote sensing images and spectral data; S05, obtaining the nitrogen content ([N]) and nitrogen isotope ratio (δ15N) in the standard sample set to obtain chemical measurement data corresponding to the samples to be tested of the different plants; S06, combining the corresponding spectral data and the chemical measurement data obtained by the samples to be tested of the same plant in the same time unit, and then combining the spectral features, and using partial least squares regression to perform multiple regression analysis, to construct a model between spectral reflectance and nitrogen source index, and then using Kennard–Stone algorithm to optimize and segment the data set to obtain an optimal model; S07. Collect the spectral data of the samples of different plants and the spectral characteristics of the target area in sequence from the center to the periphery of the target area, and input them into the optimal model to calculate the nitrogen content ([N]) and the nitrogen source percentage (%Ndfa) of the plant samples in a certain range of quantities in the target area.

2. The method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy according to claim 1, characterized in that: The spectral feature includes any one of NDVI, EVI or near-infrared reflectivity.

3. The method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy according to claim 1, characterized in that: During the execution of steps S01 to S06, it is necessary to collect the spectral data and spectral features under four different humidity and temperature conditions: warming water supply, normal temperature water supply, warming drought and normal temperature drought.

4. The method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy according to claim 1, characterized in that: In step S02, the spectral range is 400 to 2400 nanometers, the spectral resolution is 0.5 nanometers, and the spectral data obtained is an average value of no less than two samples of the same plant to be tested.

5. The method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy according to claim 1, characterized in that: The nitrogen content ([N]) and the nitrogen isotope ratio (δ15N) in step S04 are obtained by a stable isotope mass spectrometer.

6. The method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy according to claim 1, characterized in that: In step S03, the data obtained by near-infrared spectral scanning needs to be subjected to first-order or second-order derivative, baseline correction or normalization to remove spectral noise and interference of the data.

7. The method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy according to claim 1, characterized in that: In addition to the monitored plants, the standard sample set also includes alfalfa and tall fescue as reference standards.

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