Method for constructing and estimating photosynthetic capacity index of corn population

Abstract

The invention discloses a corn population photosynthetic capability index construction and estimation method, and relates to the field of agricultural product monitoring, and the method comprises the following steps: S1, obtaining four photosynthetic parameters Pn, Gs, Ci and Tr of corn, S2, shortening the measurement interval of the Pn, Gs, Ci and Tr to one hour by using an Akima spline interpolation method, S3, obtaining a fitting curve of the four parameters, S4, calculating the projection area under the fitting curve, and S5, calculating the photosynthetic capability index of the corn population according to the projection area under the fitting curve. S5, the projection areas are normalized, the weight of each projection area is determined, S6, a new photosynthetic capacity index ODP is calculated, and meanwhile, an ODP index feasibility verification method and an ODP index feasibility estimation method are provided. The method has the advantages that the group photosynthetic capacity can be accurately reflected, the group photosynthetic capacity accords with the actual condition, and the growth state of field corn can be truly reflected. In addition, the index can be quickly and accurately estimated by using the hyperspectrum of the unmanned aerial vehicle, and the photosynthetic capability identification efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of crop monitoring, and in particular to a method for constructing and estimating a photosynthetic capacity index of a corn population. Background Art

[0002] Corn is an important cereal crop that can be used for food, feed and a variety of industrial purposes. It plays an important strategic role in ensuring food security. In addition, corn is a typical C4 plant with stronger photosynthesis than C3 plants, which also shows that photosynthesis has a greater impact on corn yield. Photosynthesis provides the material basis for grain formation, and its strength is an important indicator for measuring crop growth conditions. Therefore, monitoring corn photosynthesis is of great significance for understanding its growth conditions.

[0003] The evaluation of traditional crop photosynthetic capacity mainly relies on gas exchange determination. However, this measurement method is time-consuming and labor-intensive and is not suitable for high-throughput crop photosynthetic research. Multi / hyperspectral sensors can be carried on ground observation platforms, drones or satellite remote sensing platforms to obtain vegetation parameters, making collection more convenient and faster, and have significant advantages in field photosynthesis parameter extraction. Establishing a quantitative model between photosynthetic indicators and spectral vegetation indices can quickly and non-destructively obtain the physiological state of vegetation, accurately reflect the photosynthetic physiological changes of vegetation, and effectively estimate the photosynthetic capacity of vegetation.

[0004] Many indicators can characterize photosynthetic capacity, such as maximum carboxylation rate, maximum electron transfer rate, and maximum carbon dioxide (CO2) assimilation rate. However, the determination of these indicators is usually carried out under specific conditions, including optimal light intensity, sufficient CO2 supply, and suitable temperature, etc., which mainly reflect the maximum capacity of specific steps in photosynthesis. In actual field environments, these conditions are often not met at the same time. Net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO2 concentration (Ci), and transpiration rate (Tr) can also characterize photosynthetic capacity. Among them, Fn refers to the amount of carbon dioxide absorbed by plant photosynthesis minus the amount of carbon dioxide released by respiration under actual environmental conditions; Gs refers to the degree of stomatal opening on plant leaves, which determines the acquisition of O2 and CO2 and the loss of water; Ci refers to the concentration of intracellular CO2, which is one of the important factors affecting the rate of photosynthesis; Tr refers to the amount of water lost by plants through stomata. Appropriate transpiration helps maintain water balance and temperature regulation in plants. Each parameter provides different perspectives and information. These parameters interact with each other and jointly determine the photosynthetic state of plants. Therefore, when using remote sensing to evaluate the photosynthetic capacity of corn, a single parameter such as Pn cannot accurately characterize the photosynthetic capacity of the group, and a comprehensive index needs to be constructed to increase the robustness of the photosynthetic capacity evaluation. In addition, Fn, Gs, Ci and Tr all reflect instantaneous measurements under the environment at that time, which may be affected by short-term environmental changes, resulting in data deviations and limiting the comprehensive evaluation of plant growth and carbon fixation capacity. In view of this, the present invention provides a method for constructing and estimating a photosynthetic capacity index for a corn group. Summary of the invention

[0005] The purpose of the present invention is to solve the problems in the prior art and to propose a method for constructing and estimating a photosynthetic capacity index of a corn population.

[0006] A method for constructing and estimating a photosynthetic capacity index of a corn population comprises the following steps:

[0007] S1: Obtain the four photosynthetic parameters of corn, Pn, Gs, Ci and Tr, and measure them every two hours from 6 am to 6 pm.

[0008] S2: The measurement interval of Pn, Gs, Ci and Tr is shortened to one hour using Akima spline interpolation method.

[0009] S3: Use the sine sum function to fit the Pn, Gs, Ci and Tr values ​​to obtain the fitting curve of the four parameters. The sine sum function formula is:

[0010]

[0011] Where a is the amplitude, b is the frequency, and c is the phase constant of each sine wave. n is the number of terms in the sequence, 1≤n≤8;

[0012] S4: Use the integral method to calculate the projected area under the curve and get Pn area , Gs area 、Ci area and Tr area .

[0013] S5: Normalize the projection area and determine the weight of each projection area using the CRITIC weight method.

[0014] S6: Calculate the new photosynthesis index ODP (One dayphotosynthesis) according to the projection area and weight of each parameter;

[0015] S7: Obtain the 100-kernel weight of kernels, simulate the corn filling process, and use the increase in 100-kernel weight to measure the feasibility of the ODP index.

[0016] S8: Acquire a hyperspectral image of the target area.

[0017] S9: Preprocess the hyperspectral image of the target area and convert the raster image of the target area into hyperspectral reflectance.

[0018] S10: Calculate 17 vegetation indices, use two-dimensional correlation spectral analysis technology to screen sensitive bands, and optimize and improve vegetation indices.

[0019] S11: Competitive adaptive reweighted sampling (CARS) was used to screen vegetation indices and remove multicollinearity.

[0020] S12: Construct an ODP estimation model based on vegetation index.

[0021] S13: The coefficient of determination (R 2 ), root mean square error (RMSE) and mean absolute error (MAE) to evaluate the estimation performance of the model. 2 , RMSE, and MAE calculation formulas are:

[0022]

[0023] Where N is the number of samples, y i and are the measured ODP and the estimated ODP, respectively, is the average value of the measured ODP.

[0024] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of a corn population, in step S1, natural light is used as the light source, three corn plants with the same growth condition, free of pests and diseases, and without damage are randomly selected, and the ear leaves are selected for measurement, and the leaves are kept in place during the measurement process. The average value is calculated and used for subsequent data analysis.

[0025] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of a corn population, in step S5, the projected area is normalized, and the normalized interval is [0, 1]. The CRITIC weight method formula is:

[0026]

[0027] C j =S j ×R j #(7)

[0028]

[0029] Among them, S j is the intra-indicator variability, R j For the conflict between indicators, is the index mean, r ij is the correlation coefficient between indicators i and j, C j represents the amount of information, W j is the final weight.

[0030] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of a corn population, in step S6, the ODP calculation formula is:

[0031] ODP=W1×Pn area +W2×Gs area +W3×Ci area +W4×Tr area #(9)

[0032] Among them, W1, W2, W3 and W4 are Pn area , Gs area 、Ci area and Tr area The weight of .

[0033] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of the corn population, in step S7, 30 plants with the same growth are selected for marking during the corn silking period. Five corn ears are selected 10, 20, 30 and 40 days after flowering, and 100 seeds are taken from the middle of each ear. The seeds are dried in an oven at 105°C for 30 minutes and then further dried at 80°C until the weight is constant. The weight of 500 seeds was measured with an electronic scale to calculate the hundred-grain weight (HGW). The Logistic equation is used to simulate the grain filling process, with the number of days after flowering as the independent variable and the HGW measured every 10 days as the dependent variable. The Logistic equation is:

[0034]

[0035] Among them, t is the number of days after flowering, A is the final 100-grain weight, B is the initial value parameter, and C is the growth rate parameter.

[0036] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of a corn population, in step S8, the UAV flight altitude is 30m, the heading overlap rate and the lateral overlap rate are 70% and 60% respectively, and the reflectivity calibration is performed using a standard white board before the UAV takes off. After the UAV takes off, atmospheric calibration is performed using gray cloth with reflectivity of 20%, 50% and 70% respectively. The image acquisition adopts the automatic exposure mode, and the hyperspectral image acquisition time is 11:00 am on the day of the photosynthetic parameter measurement.

[0037] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of a corn population, in step S9, after image acquisition, the image is subjected to lens calibration, reflectance calibration and atmospheric calibration. The calibrated images are spliced ​​using the position and attitude measurement system (POS), latitude and longitude data, and feature point matching in the overlapping areas of the images to obtain a hyperspectral raster image containing 176 bands. Finally, the canopy reflectance of each plot is extracted. The reflectance curve is smoothed using the Savitzky-Golay method, and the window size is set to 5.

[0038] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of a corn population, in step S11, the sampling times are set to 100,000, and the wavelength variable subset corresponding to the minimum root mean square error is selected as the characteristic wavelength.

[0039] In the above-mentioned method for constructing and estimating the photosynthetic capacity index of a corn population, in step S12, the gradient method is used to divide the training set and the test set in a ratio of 3:1. The specific operation is: first, the samples are arranged from small to large according to the ODP value, and then the second or third sample is selected as the test set in each of the four samples, and the remaining samples are the training set, ensuring that the ODP value of the test set sample is within the ODP value range of the training set sample and is evenly distributed, and the leave-one-out cross validation (LOOCV) is used to further evaluate the estimation performance of the model.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] 1. By measuring the changes in photosynthetic parameters Pn, Gs, Ci and Tr during a day, the present invention proposes a new photosynthetic index ODP to describe the photosynthesis of corn in a day, and further uses drone hyperspectral to predict ODP in order to quickly evaluate the photosynthetic capacity of corn populations.

[0042] 2. Construct a comprehensive indicator to increase the robustness of photosynthetic capacity assessment, avoid the problem of data bias caused by short-term environmental changes, and improve the comprehensive assessment of plant growth and carbon fixation capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of the method for constructing and estimating the photosynthetic capacity index of a population;

[0044] Figure 2 Calculation process diagram of the projection area of ​​the Pn fitting curve;

[0045] Figure 3 Diagram of the dynamic process of corn grain filling;

[0046] Figure 4 Correlation analysis diagram between photosynthetic parameters and 100-grain weight at different times after flowering;

[0047] Figure 5 Two-dimensional correlation spectroscopy analysis results;

[0048] Figure 6 Correlation analysis chart between ODP and vegetation index;

[0049] Figure 7 Comparison of LOOCV results of different VIs screening methods. DETAILED DESCRIPTION

[0050] Reference Figure 1-6 , a method for constructing and estimating a photosynthetic capacity index of a corn population, comprising the following steps:

[0051] S1: The Li-6400 XT portable photosynthesis system was used to collect photosynthetic data (including Pn, Gs, Ci and Tr) of corn 10, 20, 30 and 40 days after flowering. Natural light was used as the light source, and measurements were taken every two hours from 6 am to 6 pm. Three corn plants with the same growth conditions, no pests or damage were randomly selected, and the ear leaves were selected for measurement. The leaves were kept in place during the measurement process. The average value was calculated and used for subsequent data analysis.

[0052] S2: The measurement interval of Pn, Gs, Ci and Tr is shortened to one hour using Akima spline interpolation method.

[0053] S3: Use the sine sum function to fit the Pn, Gs, Ci and Tr values ​​to obtain the fitting curve of the four parameters. The sine sum function formula is:

[0054]

[0055] Where a is the amplitude, b is the frequency, and c is the phase constant of each sine wave. n is the number of terms in the sequence, 1≤n≤8.

[0056] S4: Use the integration tool of Origin2021 software to calculate the projected area under the fitting curve and obtain Pn area , Gsarca 、Ci area and Tr area .

[0057] S5: Normalize the projection area, the normalized interval is [0, 1], and use the CRITIC weight method to determine the weight of each projection area. The CRITIC weight method formula is:

[0058]

[0059] C j =S j ×R j #(4)

[0060]

[0061] Among them, S j is the intra-indicator variability, R j For the conflict between indicators, is the index mean, r ij is the correlation coefficient between indicators i and j, C j represents the amount of information, W j is the final weight.

[0062] S6: Calculate the new photosynthesis index ODP (One day photosynthesis) according to the projection area and weight of each parameter. The ODP calculation formula is:

[0063] ODP=W1×Pn area +W2×Gs area +W3×Ci area +W4×Tr area #(6)

[0064] Among them, W1, W2, W3 and W4 are Pn area , Gs area 、Ci area and Tr area The weight of .

[0065] S7: Obtain the 100-grain weight of the grains, simulate the corn filling process, and use the increase in 100-grain weight to measure the feasibility of the ODP index. Specifically: select 30 plants with the same growth during the corn silking period for marking. Select 5 corn ears 10, 20, 30 and 40 days after flowering, and take 100 seeds from the middle of each ear. The seeds are withered in an oven at 105°C for 30 minutes, and then further dried at 80°C for 24 hours. Measure the weight of 500 seeds with an electronic scale with an accuracy of 0.001g, and calculate the 100-grain weight (HGW). The Logistic equation is used to simulate the grain filling process, with the number of days after flowering as the independent variable and the HGW measured every 10 days as the dependent variable. The Logistic equation is:

[0066]

[0067] Among them, t is the number of days after flowering, A is the final 100-grain weight, B is the initial value parameter, and C is the growth rate parameter.

[0068] S8: Use DJI M600Pro equipped with GaiaSky-mini hyperspectral imager to obtain hyperspectral images of the target area. Before image acquisition, DJI GS Pro was used for route planning and waypoint design. The UAV flight altitude was 30m, and the heading overlap rate and lateral overlap rate were 70% and 60% respectively. Before the UAV took off, a standard white board was used for reflectivity calibration. After the UAV took off, gray cloth with reflectivity of 20%, 50% and 70% was used for atmospheric calibration. The image acquisition adopted automatic exposure mode, and the hyperspectral image acquisition time was 11:00 am on the day of photosynthetic parameter measurement.

[0069] S9: Preprocess the hyperspectral image of the target area. Use SpecView software to calibrate the image, including lens calibration, reflectance calibration, and atmosphere calibration. Use the position and attitude measurement system, latitude and longitude data, and feature point matching in the overlapping area of ​​the image to stitch the calibrated image to obtain a hyperspectral raster image containing 176 bands. Use ENVI 5.6 software to convert the raster image of the target area into hyperspectral reflectance. Use the Savitzky-Golay method to smooth the reflectance curve, and the window size is set to 5.

[0070] S10: Calculate 17 vegetation indices (VIs), use two-dimensional correlation spectral analysis technology to screen sensitive bands, and optimize and improve vegetation indices. The calculation formula of the vegetation index is:

[0071]

[0072] NIR, RedE, R, and G represent the reflectance of near-infrared, red-edge, red, and green bands, respectively.

[0073] S11: The vegetation index is screened using the competitive adaptive reweighted sampling method. The number of Monte Carlo sampling is set to 100,000, and the wavelength variable subset corresponding to the minimum root mean square error is selected as the characteristic wavelength.

[0074] S12: Construct an ODP estimation model based on vegetation index. The gradient method is used to divide the training set and the test set in a ratio of 3:1. The specific operation is as follows: first, the samples are arranged from small to large according to the ODP value, and then the second or third sample is selected as the test set in every 4 samples, and the remaining samples are the training set, ensuring that the ODP value of the test set sample is within the ODP value range of the training set sample and is evenly distributed. Use leave-one-out cross validation (LOOCV) to further evaluate the estimation performance of the model.

[0075] S13: The coefficient of determination (R 2 ), root mean square error (RMSE) and mean absolute error (MAE) to evaluate the estimation performance of the model.

[0076] The R 2 、The calculation formula is:

[0077]

[0078] The RMSE calculation formula is:

[0079]

[0080] The MAE calculation formula is:

[0081]

[0082] Where N is the number of samples, y i and are the measured ODP and the estimated ODP, respectively, is the average value of the measured ODP.

[0083] A flow chart of a method for constructing and estimating a population photosynthetic capacity index is shown below: Figure 1 As shown, it includes data acquisition, hyperspectral image processing, new indicator construction and grain filling process simulation.

[0084] Figure 2a shows the change of daytime Pn on the 10th day after flowering. As the light and temperature increase after sunrise, Pn gradually increases and reaches a peak at 10 am. At noon, due to the strong transpiration caused by high temperature, some stomata are closed, reducing the CO2 supply, and Pn decreases at 12 o'clock. In the afternoon, as the light and temperature decrease, the stomata reopen, the CO2 supply increases, and Pn rises again at 14 o'clock. After that, the light and temperature continue to decrease, and Pn drops to zero after sunset. The Akima method is used to interpolate the daytime Pn data, and the results are shown in the figure. Figure 2 Then, the sine sum function is used to fit the Pn value. When the number of terms is 5, the downward trend at noon can be accurately fitted ( Figure 2 c), finally, calculate the projection area of ​​the fitting curve on the x-axis ( Figure 2 d), the shaded area in the figure represents the net photosynthesis of corn in a day. The larger the projection area, the more organic matter is produced by photosynthesis. Similarly, the changes of Gs, Ci and Tr in a day are fitted. The R2 range of all fittings is between 0.95 and 1, achieving a high fitting accuracy.

[0085] In order to comprehensively utilize Pn_area, Gs_area, Ci_area and Tr_area, the CRITIC weight method was used to calculate the weight of each indicator, and the results are shown in Table 1. The weight distribution of each indicator showed obvious differences. The weights of Pn_area at 10, 20, 30 and 40 days after flowering were 28.241%, 32.747%, 25.478% and 24.844%, respectively, and showed an upward and then downward trend with the progress of grain filling. The weight of Tr_area exceeded 25% and showed a gradual upward trend. Among the four indicators, Pn_area had the highest weight at 10 and 20 days after flowering, while Tr_area had the highest weight at 30 and 40 days after flowering. The weights of Gs_area and Ci_area were relatively low, among which the weight of Ci_area at 20 days after flowering was only 14.831%. Finally, the ODP of each grain filling stage was calculated.

[0086] Table 1 The weights of different photosynthetic parameters at different filling stages

[0087]

[0088] Figure 3 a shows the results of 100-grain weight at different filling stages. At 10 and 20 days after flowering, the differences between different plots were small, with standard deviations of 0.639 and 1.045, respectively. At 30 and 40 days after flowering and after harvest, the differences between different plots were large, with standard deviations of 2.673, 2.198, and 2.391, respectively. After harvest, the maximum value of 100-grain weight was 34.508 g, the minimum value was 25.355 g, and the average value was 30.498 g. Figure 3 b shows the 100-grain weight of a plot in 2021. The Logistic function is used to fit the dynamics of corn grain filling. The R 2 reached 0.997. Then, the fitting curve was differentiated to obtain the corn grain filling rate (GFR) curve ( Figure 3 c) HGW growth curve and GFR curve show that the whole process of corn filling is divided into gradual increase period, rapid increase period and slow increase period. The filling rate is slow in the gradual increase period, and it is faster in the rapid increase period. It reaches the maximum about 20 days after flowering, and the GFR reaches 1.249g 100-grain -1 d -1 , during the slow increase period, the grouting rate slows down again until it drops to 0.

[0089] According to the logistic curve of 100-grain weight growth, the weight growth of grains on the 10th, 20th, 30th and 40th day after flowering was calculated, and the correlation analysis was performed with Pn, Gs, Ci and Tr. The results are as follows: Figure 4As shown. Overall, the correlation between Pn, Gs and Tr was high, and most of them were positively correlated, with the highest correlation coefficients of 0.717, 0.658 and 0.654, respectively. The correlation of Ci was generally low, with the highest correlation coefficient being only 0.513. At 10, 20, 30 and 40 days after flowering, the maximum correlation coefficients between Pn and HGW appeared at 14:00, 18:00, 10:00 and 12:00, respectively. The maximum correlation coefficients between photosynthetic parameters at different filling stages and HGW appeared at different times of the day, which increased the difficulty of evaluating photosynthetic capacity. The weight growth of grains at 10, 20, 30 and 40 days after flowering was further correlated with Pn_area, Gs_area, Ci_area and Tr_area. Compared with Pn and Tr at a single moment, the correlations between Pn_area and Tr_area were significantly improved. The correlation coefficients of Pn_area after 10, 20, 30 and 40 days were 0.759, 0.813, 0.822 and 0.735, respectively, which were 0.105 higher than Pn. The correlation coefficients of Tr_area after 10, 20, 30 and 40 days were 0.668, 0.660, 0.633 and 0.477, respectively, which were 0.109 higher than Tr. Gs_area and Ci_area had a low correlation with HGW, with the highest correlation coefficient being only 0.510, and the correlation did not increase or even decreased compared with Pn and Tr at a single moment. Correlation analysis of ODP and HGW showed that the correlation coefficients of ODP were 0.831, 0.882, 0.856 and 0.833, respectively, 10, 20, 30 and 40 days after flowering. Compared with the individual Pn_area, Gs_area, Ci_area, and Tr_area parameters, r was significantly improved, indicating that the constructed ODP index can better represent the ability of corn to produce organic matter through photosynthesis.

[0090] The spectral sensitive bands were screened using two-dimensional correlation spectral analysis, and the ODP ( Figure 5 The results showed that the spectral responses at different filling stages were different. At 10 days after flowering, the most sensitive bands in the blue, green, red, red edge and near-infrared regions were 450nm, 554nm, 680nm, 744nm and 934nm, respectively; at 20 days after flowering, the most sensitive bands were 442nm, 545nm, 676nm, 735nm and 935nm, respectively; at 30 days after flowering, the most sensitive bands were 450nm, 560nm, 621nm, 742nm and 781nm, respectively; at 40 days after flowering, the most sensitive bands were 450nm, 504nm, 678nm, 681nm and 936nm, respectively.

[0091] Based on the results of two-dimensional correlation spectroscopy analysis, 17 VIs were optimized and then correlated with ODP. The results are shown in Figure 6 As shown in Figure 2, SIPI, VARI, SIPI, and CARI showed the highest correlation at 10, 20, 30, and 40 days after anthesis, with r values ​​of -0.824, 0.886, -0.808, and 0.852, respectively. When the data of the entire grain filling period were analyzed comprehensively, BRI had the highest correlation among all VIs (r = 0.732). In general, the data of the individual grain filling stages showed stronger correlations than the comprehensive data.

[0092] The data and model were evaluated using leave-one-out cross-validation ( Figure 7 ). The results show that the inversion models constructed with all VIs and VIs screened by features as input can achieve high prediction accuracy. Among them, the inversion model screened by CARS features has the highest accuracy, R 2 , RMSE and MAE are 0.82, 111.03 and 89.14 respectively. The inversion model without feature screening has the second best accuracy. 2 It also reached 0.81, with RMSE and MAE of 113.19 and 92.14 respectively.

[0093] The test results show that the photosynthetic capacity index of the population proposed in the embodiment of the present invention is close to the actual situation and can reflect the actual growth situation of corn in the field. In addition, the index can be quickly and accurately estimated by using drones, thereby improving the efficiency of photosynthetic capacity identification.

[0094] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A method for constructing and estimating a corn population photosynthetic capacity index, characterized in that: The following steps are involved: S1: Obtain four photosynthetic parameters of corn: net photosynthetic rate Pn, stomatal conductance Gs, intercellular CO2 concentration Ci and transpiration rate Tr, measured every two hours from 6 am to 6 pm; S2: Use Akima spline interpolation to shorten the measurement interval of net photosynthetic rate Pn, stomatal conductance Gs, intercellular CO2 concentration Ci and transpiration rate Tr to one hour; S3: Use the sine sum function to fit the values ​​of net photosynthetic rate Pn, stomatal conductance Gs, intercellular CO2 concentration Ci and transpiration rate Tr to obtain the fitting curve of the four parameters. The sine sum function formula is: Where a is the amplitude, b is the frequency, c is the phase constant of each sine wave, and n is the number of terms in the sequence, 1≤n≤8; S4: Use the integral method to calculate the projected area under the curve and get Pn area , Gs area 、Ci area and Tr area ; S5: normalize the projection area and determine the weight of each projection area using the CRITIC weight method; S6: Calculate the photosynthesis index ODP according to the projection area and weight of each parameter. The ODP calculation formula is: ODP=W1×Pn area +W2×Gs area +W3×Ci area +W4×Tr area Among them, W1, W2, W3 and W4 are Pn area , Gs area 、Ci area and Tr area The weight of S7: Obtain the 100-grain weight of kernels, simulate the corn filling process, and measure the feasibility of ODP index by the increase of 100-grain weight; S8: Acquire the hyperspectral image of the target area; S9: preprocessing the hyperspectral image of the target area, converting the raster image of the target area into hyperspectral reflectance; S10: Calculate 17 vegetation indices, use two-dimensional correlation spectral analysis technology to screen sensitive bands, and optimize and improve vegetation indices; S11: Use competitive adaptive reweighted sampling method to screen vegetation indices and remove multicollinearity; S12: Constructing ODP estimation model based on vegetation index; S13: The coefficient of determination R was used 2 , root mean square error RMSE and mean absolute error MAE are used to evaluate the estimation performance of the model. 2 , RMSE, and MAE calculation formulas are: Where N is the number of samples, y i and are the measured ODP and the estimated ODP, respectively, is the average value of the measured ODP.

2. A method for constructing and estimating a photosynthetic capacity index of a corn population according to claim 1, characterized in that: In step S1, natural light is used as the light source, and three corn plants with consistent growth conditions, free of pests and diseases, and without damage are randomly selected. The leaves at the ear position are selected for measurement. The leaves are kept in place during the measurement process, and the average value is calculated and used for subsequent data analysis.

3. A method for constructing and estimating a photosynthetic capacity index of a corn population according to claim 1, characterized in that: In step S5, the projection area is normalized, and the normalized interval is [0, 1]. The CRITIC weighting method formula is: C j =S j ×R j Among them, S j is the intra-indicator variability, R j For the conflict between indicators, is the index mean, r ij is the correlation coefficient between indicators i and j, C j represents the amount of information, W j is the final weight.

4. A method for constructing and estimating a photosynthetic capacity index of a corn population according to claim 1, characterized in that: In step S7, 30 plants with the same growth potential were selected for marking at the silking stage of corn, 5 corn ears were selected at 10, 20, 30 and 40 days after flowering, 100 seeds were taken from the middle of each ear, the seeds were dried in an oven at 105°C for 30 minutes, and then further dried at 80°C until the weight was constant, the weight of 500 seeds was measured with an electronic scale, and the 100-grain weight HGW was calculated. The Logistic equation was used to simulate the grain filling process, with the number of days after flowering as the independent variable and the HGW measured every 10 days as the dependent variable. The Logistic equation is: Among them, t is the number of days after flowering, A is the final 100-grain weight, B is the initial value parameter, and C is the growth rate parameter.

5. The method for constructing and estimating a photosynthetic capacity index of a corn population according to claim 1, characterized in that: In step S8, the UAV is flying at an altitude of 30 m, with heading overlap and lateral overlap rates of 70% and 60% respectively. Before the UAV takes off, a standard white board is used for reflectance calibration. After the UAV takes off, gray cloth with reflectances of 20%, 50% and 70% is used for atmospheric calibration. The image is acquired in automatic exposure mode, and the hyperspectral image is acquired at 11:00 a.m. on the day of photosynthetic parameter measurement.

6. A method for constructing and estimating a photosynthetic capacity index of a corn population according to claim 1, characterized in that: In step S9, after image acquisition, the image is calibrated for lens, reflectance and atmosphere. The calibrated image is spliced ​​using the position and attitude measurement system, latitude and longitude data, and feature point matching in the overlapping area of ​​the image to obtain a hyperspectral raster image containing 176 bands. Finally, the canopy reflectance of each plot is extracted, and the reflectance curve is smoothed using the Savitzky-Golay method, with the window size set to 5.

7. A method for constructing and estimating a photosynthetic capacity index of a corn population according to claim 1, characterized in that: In step S11, the sampling times are set to 100000, and the wavelength variable subset corresponding to the minimum root mean square error is selected as the characteristic wavelength.

8. The method for constructing and estimating a photosynthetic capacity index of a corn population according to claim 1, characterized in that: In step S12, the gradient method is used to divide the training set and the test set into a ratio of 3:

1. The specific operation is as follows: first, the samples are arranged from small to large according to the ODP value, and then the second or third sample is selected as the test set in every 4 samples, and the remaining samples are the training set. The ODP value of the test set sample is ensured to be within the ODP value range of the training set sample and is evenly distributed. The leave-one-out cross validation is used to further evaluate the estimation performance of the model.

Images