A method for evaluating nitrogen fixation of legumes based on near infrared spectroscopy

Abstract

The application discloses a kind of based on near infrared spectrum to legume nitrogen fixation evaluation method, specifically related to computational formula processing method, including the following steps: S01, based on selected different plants to be measured sample, establish the standard sample set of PLSR model;S02, determine the target area of the to-be-tested sample detection, and obtain the spectral characteristics of target area by remote sensing image;S03, the standard sample set in the target area is scanned by near infrared spectrum, to obtain spectral data;S04, the spectral characteristics is combined with the spectral data, to obtain the fusion set of remote sensing image and spectral data;S05, the nitrogen content ([N]) and nitrogen isotope ratio (delta 15 N) in the standard sample set are acquired, to obtain the chemical measurement data corresponding to the to-be-tested sample of the different plants.The application realizes the efficient, non-destructive detection of the amount of biological nitrogen fixation of legume, compared with traditional chemical reagent and cumbersome experimental steps, has the advantages of simple operation, rapid detection, reliable result etc.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for evaluating nitrogen fixation of legumes based on near-infrared spectroscopy. BACKGROUND

[0002] In agricultural production and ecosystem management, nitrogen is one of the key nutrients that directly affect plant growth and soil fertility. Legumes have a unique ability to fix nitrogen through symbiotic microorganisms, which can convert atmospheric nitrogen into nitrogen compounds that plants can absorb, providing additional nitrogen sources for the soil and reducing dependence on chemical fertilizers. However, traditional methods for measuring nitrogen fixation, such as stable isotope ratio mass spectrometry (IRMS), often require complex experimental procedures, expensive equipment, and take a long time, limiting their application in large-scale agriculture and ecological monitoring.

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

[0004] Remote sensing technology, especially spectral remote sensing, can efficiently acquire surface information over a large area, with the advantages of large-scale monitoring and real-time data acquisition. Common remote sensing image data, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance, have been widely used in vegetation coverage, health status, and growth monitoring. However, traditional remote sensing technology usually cannot directly obtain the specific nitrogen content and nitrogen fixation capacity of plants, and cannot fully consider the influence of different plant types and environmental factors on nitrogen fixation.

[0005] Existing technologies have not effectively combined near-infrared spectroscopy data and remote sensing image data to achieve efficient, accurate, and rapid evaluation of legume nitrogen fixation. Therefore, there is an urgent need for a new method that combines near-infrared spectroscopy technology and remote sensing technology to quickly evaluate the nitrogen fixation capacity of legumes at a large scale while improving the accuracy and efficiency of the evaluation. SUMMARY

[0006] The purpose of the present application is to provide a method for evaluating nitrogen fixation of legumes based on near-infrared spectroscopy to solve the above problems.

[0007] To achieve the above purpose, the present application provides the following technical solution: a method for evaluating nitrogen fixation of legumes based on near-infrared spectroscopy, comprising the following steps:

[0008] S01. Establish a standard sample set for the 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 spectral 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 image 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 test samples of the different plants;

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

[0014] S07. Spectral data of the test samples of different plants and spectral characteristics of the target area are collected sequentially from the center to the periphery of the target area, and the data are input into the optimal model to calculate the nitrogen content ([N]) and nitrogen source percentage (%Ndfa) of several ranges of plant samples in the target area.

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

[0016] Preferably, during the execution of steps S01 to S06, the spectral data and spectral features under four different humidity and temperature conditions, namely, warming water supply, normal temperature water supply, warming drought, and normal temperature drought, need to be collected.

[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 nitrogen isotope ratio (δ15N) in step S04 are obtained by a stable isotope mass spectrometer.

[0019] As preferred, the step S03 requires the data obtained by near infrared spectrum scanning to remove spectral noise and interference of the data by first or second derivative, baseline correction or normalization.

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

[0021] In the above technical solution, the evaluation method for legume nitrogen fixation based on near infrared spectrum provided by the present application has the following beneficial effects: by fusing near infrared spectrum data and remote sensing image data, a nitrogen content and nitrogen fixation capacity prediction model is established, efficient and non-destructive detection of legume nitrogen fixation amount is realized, and the method is suitable for plant nitrogen fixation evaluation under different scales to meet the needs of rapid and accurate detection technology for agricultural and ecological system management. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0023] Figure 1 The VIP wavelength distribution graph of plant nitrogen concentration and nitrogen isotope under greenhouse conditions provided by the embodiment of the present application;

[0024] Figure 2 The VIP wavelength distribution graph of plant nitrogen concentration and nitrogen isotope under field conditions provided by the embodiment of the present application;

[0025] Figure 3 The prediction effect evaluation graph of plant sample nitrogen isotope and nitrogen content provided by the embodiment of the present application;

[0026] Figure 4 The prediction graph of Ndfa percentage and total amount of legume plants under different climate conditions provided by the embodiment of the present application. DETAILED DESCRIPTION

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

[0028] Embodiment one

[0029] The present application aims to provide an evaluation method for legume nitrogen fixation based on near infrared spectrum, which comprises the following steps:

[0030] S01, based on the selected different plant samples to be measured, a standard sample set for establishing a PLSR model is established;

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

[0032] S03, performing near-infrared spectral 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 nitrogen content ([N]) and nitrogen isotope ratio (δ15N) in the standard sample set to obtain chemical measurement data corresponding to the test samples of 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, then combine the spectral characteristics and perform multiple regression analysis using partial least squares regression to construct a model between spectral reflectance and nitrogen source indicators. Then, use the Kennard–Stone algorithm to optimize the segmentation of the data set to obtain the optimal model;

[0036] S07. Spectral data of test samples of different plants and spectral characteristics of the target area are collected sequentially 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 several ranges of plant samples in the target area.

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

[0038] During the execution of steps S01 to S06 in the above embodiment, it is necessary to collect spectral characteristics and spectral data under four different humidity and temperatures: 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, including 201 greenhouse samples, including 99 alfalfa samples and 102 tall fescue samples; and there are 77 standard samples (monitored plants) in the field, 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 the average value of no less than two samples collected from the same plant.

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

[0042] It should be noted that the data obtained by near-infrared spectral scanning in step S03 in the above embodiment needs to be processed by any of first or second 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 Kennard-Stone algorithm is used to divide the data into a representative subset, with the calibration set accounting for 80%, and the rest used for external verification, while the spatial extrapolation of the model is verified in combination with remote sensing data.

[0044] Experimental Example One

[0045] The experiment was carried out in the laboratory of the University of Western Sydney, and the test materials were taken from the alfalfa and tall fescue plant samples in the greenhouse and PACE outdoor facilities of the Hawkesbury campus of the University of Western Sydney, and the sampling time was from 2018 to 2019.

[0046] Both the greenhouse and field samples were subjected to four kinds of climate combination treatments, namely, increased temperature and water supply, normal temperature and water supply, increased temperature and drought, and normal temperature and drought, totaling 348 samples.

[0047] The plant stem and leaf samples without pests and diseases were cut and dried.

[0048] The dried samples were ground into uniform powder by a 1 mm screen using a laboratory grinder (such as Foss Cyclotec Mill, Denmark).

[0049] The ground samples were loaded into sample cups or containers for spectral analysis, ensuring uniform distribution of the samples and no air bubbles.

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

[0051] Figure 1 and Figure 2VIP wavelength distribution of legume nitrogen concentration and nitrogen isotope under greenhouse and field conditions are shown, respectively. The VIP curves show higher VIP values in the wavelength region of 400-700 nm and 2000-2400 nm, so the spectral range selected during the model optimization process is 400 to 2400 nm with a resolution of 0.5 nm. The samples were reloaded and scanned twice, and the average spectrum was used for calibration and prediction.

[0052] Spectral data (400-2400 nm) of plant samples were collected for evaluating the predictive ability of their spectral reflectance to the chemical measurement data (including nitrogen content [N] and nitrogen isotope ratio δ15N) by isotope ratio mass spectrometry (IRMS).

[0053] Partial least squares regression (PLSR) analysis method was used to establish the model. PLSR evaluates the relationship between spectral data and target functional traits by compressing the prediction matrix into a small number of uncorrelated latent components, optimizing the model fitting effect.

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

[0055] The chemical / spectral pair data of each PLSR model were divided into calibration set and validation set, 80% for 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 model accuracy was evaluated using the determination coefficient (R 2 ) and root mean square error (RMSE) of the validation data set. Various data transformations were performed on the original spectral data (such as first derivative, second derivative, straight line subtraction, constant offset elimination, vector normalization, and maximum / minimum normalization), and the optimizePLS function was used to select the best fitting model. Table 1 shows the partial least squares regression (PLSR) model parameters and statistical indicators for predicting legume and gramineous plant nitrogen isotope (δ15N) and nitrogen concentration ([N]) in greenhouse and field experiments.

[0058] Among the optimized models, the PLSR models of δ15N, [N] of greenhouse alfalfa and δ15N of tall fescue produced R 2The R values ​​of δ15N and [N] of field samples were 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 Ndfa percentage correlation R 2 is 0.54 and the slope is 0.84.

[0059] In greenhouse samples of the calibration model, the R values ​​of the δ15N and [N] of alfalfa and the δ15N model of tall fescue 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 values, the correlation coefficient (R 2 ) was 0.54, and the regression slope was 0.84. Although there were certain errors, the overall trend was close to the 1:1 line, indicating that the spectroscopic method has a certain degree of 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), with a slope of 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 The prediction effect of N) was analyzed. Figure 4 shown. Figure 4A, B, and C show the predicted nitrogen isotope ratio (δ 15 N) and nitrogen content (N) of legume (Lucerne) and grass (Fescue), respectively. 2 The results show that the correlation coefficients (R 2 ) of the predicted values and the measured values are high and the root mean square errors (RMSE) are low for different species under greenhouse and field conditions, indicating that the spectral method is not only suitable for different plant types, but also can accurately predict the nitrogen isotope ratio and nitrogen content.

[0063] The above merely describes certain exemplary embodiments of the present application by way of illustration, and it is needless to say that the described embodiments can be modified in various ways without departing from the spirit and scope of the present application for those skilled in the art. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of the claims of the present application.

Claims

1. A method for evaluating nitrogen fixation in legumes based on near-infrared spectroscopy, characterized in that: The following steps are involved: S01. Establish a standard sample set for the 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 spectral 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 image and spectral data; S05. Obtaining the nitrogen content ([N]) and nitrogen isotope ratio in the standard sample set to obtain chemical measurement data corresponding to the test samples of the different plants; S06. Combining the corresponding spectral data and the chemical measurement data obtained for the test samples of the same plant in the same time unit, and then combining the spectral characteristics and performing multiple regression analysis using partial least squares regression to construct a model between spectral reflectance and nitrogen source index, and then using the Kennard–Stone algorithm to optimize and segment the data set to obtain an optimal model; S07. Spectral data of the test samples of different plants and spectral characteristics of the target area are collected sequentially from the center to the periphery of the target area, and the data are input into the optimal model to calculate the nitrogen content ([N]) and nitrogen source percentage (%Ndfa) of several ranges of plant samples in the target area.

2. The method for evaluating nitrogen fixation in leguminous plants based on near infrared spectroscopy according to claim 1, wherein: The spectral characteristics include 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, wherein: 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, wherein: In step S02, the spectral range is 400 to 2400 nanometers, the spectral resolution is 0.5 nanometers, and the spectral data obtained is the 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, wherein: The nitrogen content ([N]) and nitrogen isotope ratio 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, wherein: 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 from the data.

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

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