A method, system, and storage medium for estimating wheat light energy utilization.

Abstract

This invention discloses a method, system, and storage medium for estimating wheat light energy use efficiency (RUE), relating to the field of crop cultivation technology. The invention includes the following steps: collecting vegetation data at different growth stages of wheat; measuring parameters of the vegetation data at different growth stages and calculating RUE; constructing a composite spectral index by using a preferred vegetation index and a two-band ratio optimized combination parameter SR in the form of difference combination, and fitting the composite spectral index to RUE; when the fitting result meets preset conditions, the composite spectral index is used to estimate the RUE to be calculated. This invention constructs a new vegetation index through parameter combination to accurately estimate RUE. The expected results will provide a method for estimating RUE with high accuracy under varying environmental conditions, including rainfed, irrigated, and different nitrogen fertilizer treatments.

Description

Technical Field

[0001] This invention relates to the field of crop cultivation technology, and more specifically to a method, system, and storage medium for estimating the light energy utilization rate of wheat. Background Technology

[0002] Rainfed agriculture refers to agricultural production that relies solely on natural rainfall as its water source. It is primarily found in semi-arid and semi-humid, drought-prone areas, but also in humid regions with abundant rainfall. Rainfed agriculture is an important component of my country's agriculture, a crucial grain-producing region, and has become a strategic area for my country's agricultural and national economic development, representing the greatest potential for agricultural growth. Although water scarcity constrains agricultural development to some extent, northern regions possess abundant land resources and ample sunlight and heat, which are conducive to crop growth and have significant potential for increased yields. Fully utilizing the region's sunlight and heat resources, improving land utilization efficiency, and increasing grain production are crucial for ensuring national food security. Therefore, accurately estimating the correlation between light energy utilization efficiency (RUE) and primary productivity (GPP) and yield under varying environmental conditions is essential.

[0003] Remote sensing technology, applied under a range of crop growth conditions, can rapidly, accurately, and objectively estimate relevant physiological indicators of crop yield, such as photosynthetic rate, leaf area index, and biomass. Ground hyperspectral data has been widely used to estimate the yield utilization (RUE) of different crops. Two main candidate methods exist: chlorophyll fluorescence and vegetation indices. Although there is a good relationship between chlorophyll fluorescence and RUE, obtaining such data from large-scale remote sensing is challenging. Furthermore, NDVI based on remote sensing reflectance data has low accuracy in RUE estimation. A possible alternative is PRI based on two narrow-band reflectance, which has been widely used to estimate APAR dissipation for different crops at different temporal and spatial scales. The relationship between RUE and PRI varies across different scales and is highly susceptible to environmental and other factors. Therefore, the instability of the relationship between PRI and RUE hinders the predictive power of PRI. Thus, addressing these technical challenges is a crucial area of ​​research for those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a method, system and storage medium for estimating wheat light energy utilization rate, in order to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for estimating wheat light energy utilization efficiency includes the following steps:

[0007] Collect vegetation data at different growth stages of wheat;

[0008] Parameters were measured for vegetation data at different growth stages of wheat, and light energy utilization was calculated.

[0009] A composite spectral index was constructed by combining the vegetation index optimization method with the two-band ratio spectral parameter SR in the form of a secondary screening method, and the composite spectral index was fitted with the light energy utilization rate.

[0010] When the fitting results meet the preset conditions, the composite spectral index is used to estimate the light energy utilization rate to be calculated.

[0011] Optional vegetation parameter measurements include biomass measurement, photosynthetically active radiation measurement, and canopy spectral measurement.

[0012] Optionally, the biomass determination steps are as follows: separate the stems and leaves, package them separately, and weigh the fresh weight of the leaves and stems; place them in an oven at 105℃ for 30 minutes to inactivate the leaves and stems, dry them at 70℃ to constant weight, record the dry weight of the leaves and stems, record the dry weight of the aboveground parts as DM, and calculate the biomass.

[0013] Optionally, a linear quantum sensor is used to acquire photosynthetically active radiation (PAR) values. The sensor is placed approximately 10 cm above the wheat canopy in the selected measurement area to collect the total PAR above the canopy (PARin). Then, the sensor is placed approximately 2 cm below the ground inside the canopy to collect the total PAR transmitted through the canopy (PARthr). Finally, the PAR interception rate (iPAR) is calculated.

[0014]

[0015] The photosynthetically active radiation (PAR) absorbed and utilized by the canopy is:

[0016] PAR = TSR × iPAR × 0.5;

[0017] TSR represents the total solar radiation.

[0018] Optionally, the two-band ratio spectral parameter SR screening method is as follows: spectral reflectance data are read every two bands across all spectral bands, arranged in matrix form, and the spectral index constructed by all possible pairwise band combinations is fitted to the RUE equation. Simultaneously, the corresponding coefficient of determination R is calculated. 2 Then draw a contour map and determine R based on the different colors. 2 The fluctuation range was further determined to establish the characteristic band combination.

[0019] A system for estimating wheat light energy utilization includes:

[0020] Data acquisition module: used to collect vegetation data at different growth stages of wheat;

[0021] Actual light energy utilization calculation module: used to measure parameters of vegetation data at different growth stages of wheat and calculate light energy utilization.

[0022] Composite Spectral Index Construction Module: This module is used to construct composite spectral indices by combining vegetation index optimization methods with the two-band ratio spectral parameter SR in the form of a secondary screening method, and then fits the composite spectral indexes with light energy utilization.

[0023] Composite Spectral Index Estimation Module: When the fitting results meet the preset conditions, the composite spectral index is used to estimate the light energy utilization rate to be calculated.

[0024] A computer storage medium storing a computer program, which, when executed by a processor, implements the steps of any one of the methods for estimating wheat light energy utilization.

[0025] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method, system, and storage medium for estimating wheat light energy utilization efficiency (RUE). It constructs a new vegetation index through parameter combination to accurately estimate RUE. The expected result is to provide a method for estimating RUE with high accuracy under varying environmental conditions, including rainfed, irrigated, and different nitrogen fertilizer treatments. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0027] Figure 1 R represents the linear relationship between light energy utilization efficiency and eight conventional vegetation indices. 2 A comparison chart with RMSE;

[0028] Figure 2a A quantitative relationship diagram between RUE and TVI-2 vegetation index;

[0029] Figure 2b A quantitative relationship diagram between RUE and MSR vegetation index;

[0030] Figure 2c A quantitative relationship diagram between RUE and RVI vegetation indices;

[0031] Figure 2d A quantitative relationship diagram between RUE and DDn vegetation index;

[0032] Figure 3a R is the linear relationship between RUE and the normalized (ND) values ​​of any two bands. 2 Contour map;

[0033] Figure 3b R represents the linear relationship between RUE and the ratio (SR). 2 Contour map;

[0034] Figure 4a A quantitative relationship diagram between the optimal two-band combination ND (468, 656) and RUE;

[0035] Figure 4b A quantitative relationship diagram between the optimized two-band combination SR(468,656) and RUE;

[0036] Figure 5a R represents the linear relationship between the difference between the conventional vegetation index DDn and the ratio parameter between any two bands and RUE. 2 Contour map;

[0037] Figure 5b R represents the linear relationship between the difference between the conventional vegetation index MSR(800,680) and the ratio parameter between any two bands and RUE. 2 Contour map;

[0038] Figure 6 A unified relationship diagram between light energy utilization efficiency and the new spectral index MSR-SR;

[0039] Figure 7 This is a flowchart of the method of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] This invention discloses a method for estimating the light energy utilization rate of wheat, such as... Figure 7 As shown, it includes the following steps:

[0042] S1: Collect vegetation data at different growth stages of wheat;

[0043] S2: Parameters were measured for vegetation data at different growth stages of wheat, and light energy utilization was calculated.

[0044] S3: A secondary screening method combining vegetation index optimization method and two-band ratio spectral parameter SR in the form of difference is used to construct a composite spectral index, and the composite spectral index is fitted with light energy utilization rate.

[0045] S4: When the fitting results meet the preset conditions, the composite spectral index is used to estimate the light energy utilization rate to be calculated.

[0046] The experiment of this invention was conducted in the experimental field of Henan Agricultural University (35°51'N, 113°35'E), with sandy loam soil. The tested variety was Yumai 49-198. The previous crop was corn straw returned to the field. The soil pH was 7.8. Three water gradients were set up: W0 (no irrigation throughout the entire growth period), W1 (one irrigation at the jointing stage), and W2 (one irrigation each at the jointing and flowering stages), with each irrigation volume being 750 m³. 3 ha -1 Five nitrogen fertilizer gradients were set for each moisture condition, namely: N0 (0 kg ha) -1 N6 (60kg ha) -1 ), N 12 (120kg ha -1 ), N 18 (180kg ha -1 ), N 24 (240kg ha -1 Half of the fertilizer was applied as base fertilizer, and the remaining half was applied as top dressing during the jointing stage. The experimental plot area was 20.3 m². 2 (7×2.9m), row spacing 20cm. Sampling periods were the jointing stage, booting stage, heading stage, flowering stage, and peak grain-filling stage. Simultaneously, a water-nitrogen coupling field experiment was conducted at the Henan Agricultural University demonstration park (34°51′N, 113°35′E) using Zhoumai 27 as the test variety for technical verification. The soil type was sandy loam. The previous crop was corn straw returned to the field, and the soil pH was 7.8. Three water gradients were set up (no irrigation throughout the entire growth period W0, one irrigation at the jointing stage W1, and one irrigation each at the jointing and flowering stages W2, with each irrigation volume being 75m³). 3 / mu), with 5 nitrogen fertilizer treatments (N0(0kgha)) under each water gradient. -1 N6 (60kg ha) -1 ), N 12 (120kg ha -1 ), N 18 (180kg ha -1 ), N 24 (240kg ha -1Of this, 50% was applied as basal fertilizer, and the remaining 50% was applied as topdressing during the jointing stage. The experimental plot area was 20.3 m². 2 (7×2.9m), with 3 replicates and a row spacing of 20cm. Other management practices were consistent with local high-yield and high-efficiency cultivation practices. Data were collected and samples were taken during important wheat growth stages (jointing stage, booting stage, heading stage, flowering stage, pre-grain filling stage, and peak grain filling stage).

[0047] In S2, biomass determination: After relevant indicators were measured in the field at each important growth stage, 20 representative plants from each plot were selected and brought back to the laboratory. The stems and leaves were separated and packaged separately, and the fresh weight of the leaves and stems (FWY and FWJ) was recorded. They were then placed in an oven at 105℃ for 30 minutes to inactivate the leaves and stems, and then dried at 70℃ to constant weight. The dry weight of the leaves and stems (DMY and DMJ) was recorded, and the aboveground dry weight was recorded as DM. The biomass was then calculated.

[0048] Measurement of photosynthetically active radiation (PAR): A linear quantum optical sensor (LI-191, Li-Cor, Inc.) was used to acquire PAR values. First, the sensor was placed approximately 10 cm above the wheat canopy in the selected measurement area to collect the total PAR above the canopy (PARin). Then, the sensor was placed approximately 2 cm below the ground inside the canopy to collect the total PAR transmitted through the canopy (PARthr). Finally, the canopy PAR interception rate (iPAR) was calculated.

[0049]

[0050] The photosynthetically active radiation (PAR) absorbed and utilized by the canopy is:

[0051] PAR = TSR × iPAR × 0.5;

[0052] Finally, the light energy utilization efficiency (RUE) is calculated using the following formula:

[0053]

[0054] Among them, DM d1 and DM d2 These represent the dry matter weights at d1 (sampling period) and d2 (next sampling period), respectively. PAR di This represents the cumulative daily PAR value between the two sampling periods.

[0055] Canopy spectral acquisition: Simultaneously with wheat water use efficiency measurement, wheat canopy spectral reflectance was collected. A FieldSpec Pro FR2500 Analytical Spectral Devices (ASD) was used at a depth of 1m. 2 Every 10 points within the range were averaged as one data point. Measurements were taken on a clear, cloudless day between 10:00 AM and 12:00 PM. The spectrometer's field of view was 25°, the spectral range was 350-1075 nm, and the sampling interval was 1.6 nm. A 40×40 cm BaSO4 white board was used to calibrate the instrument before sampling and during use.

[0056] This invention analyzed the relationship between 330 conventional spectral parameters and RUE, and selected 8 spectral parameters with relatively good performance. Figure 1 ).from Figure 1 It can be seen that there are 4 spectral parameters and RUE. 2 Values ​​greater than 0.45 are RVI(Nir, Red), TVI-2, MSR(800, 670), DDn, and R. 2 The fitting accuracies for the four spectral parameters are 0.4504, 0.4506, 0.4671, and 0.467, respectively. The fitting accuracies for other spectral parameters are all below 0.4. To further demonstrate the ability of spectral parameters to estimate RUE, the quantitative relationship between the four spectral parameters with estimation accuracies greater than 0.45 and RUE is presented in [the table / document / etc.]. Figures 2a-2d .from Figures 2a-2d It is evident that the spectral parameters RVI(Nir, Red) and TVI-2 exhibit severe saturation in the RUE estimation process, while the other two spectral parameters MSR(800, 670) and DDn perform best, but their estimation accuracy remains low (R 2 <0.5), indicating a large error in field application.

[0057] Furthermore, spectral reflectance data were collected at intervals of two bands across all spectral bands and arranged in matrix form. The spectral indices constructed from all possible pairwise band combinations were fitted to the RUE equation, and the corresponding coefficients of determination R were calculated. 2 And draw a contour map ( Figures 3a-3b R is determined based on the color. 2 A wider fluctuation range further establishes the optimal characteristic band combination. This invention mainly analyzes the normalized band combination (ND) method. Figure 3a ) and ratio method band combination (SR) ( Figure 3bThe results show that the sensitive band ranges with good correlation between the optimized ND and SR parameters and RUE are similar, specifically 420-512nm and 620-688nm. Among these two parameters, the vegetation indices with the best correlation are ND (468,656) and SR (468,656), respectively, and their model accuracy for estimating light energy utilization is RUE. 2 The values ​​are 0.468 and 0.495 respectively, and the linear relationship between them and RUE is further presented in the form of a scatter plot. Figures 4a-4b Comprehensive comparison revealed that the normalized parameter form, besides having lower accuracy in estimating RUE, also suffers from saturation. Therefore, the normalized ND parameter form will no longer be considered in the next step of new parameter construction. In comparison, the ratio vegetation index showed a 6% improvement in estimating wheat RUE compared to the conventional vegetation index, but the model's prediction accuracy is still not ideal (R... 2 <0.5).

[0058] To further improve the accuracy of RUE estimation using remote sensing spectral indices, the aforementioned conventional vegetation index optimization technique was combined with the two-band ratio spectral parameter (SR) screening technique. A novel composite spectral index was optimized and constructed using a random secondary combination of the difference values ​​to accurately track and estimate RUE. The results showed that the highest correlation between the difference between the conventional vegetation index DDn and any two-band SR values ​​and RUE was 0.50. Figure 5a The optimization results of the conventional vegetation index MSR(800,680) and any two bands SR showed that the combination of bands 703nm and 484nm yielded the best results. The final form of the new index is MSR(800,680) minus SR(703,484), which is named MSR-SR. This parameter has a highly significant positive correlation with RUE, and the coefficient of determination R of the linear equation is... 2 =0.622, indicating that the new parameters can fit the dynamic changes of RUE very well. Figures 5a-5b This equation demonstrates a 34.44% improvement in accuracy compared to the conventional optimal vegetation index MSR(800,680), with a significantly reduced estimation error. (See [link to relevant documentation]). Figure 6 .

[0059] A comprehensive analysis of the factors influencing the relationship between spectral parameters and yield effectiveness (RUE) under different growth conditions is beneficial for constructing new spectral parameters, thereby improving the accuracy of RUE estimation. Water and nitrogen fertilizer are two major cultivation practices in intensive crop production. Optimizing these two factors helps regulate plant physiological metabolism, improve crop yield, and thus increase total yield. Therefore, analyzing the key factors affecting crop RUE estimation under different water and nitrogen conditions can strengthen the relationship between spectral indices and RUE, providing a more reliable basis for optimized cultivation control and yield prediction of wheat.

[0060] This embodiment also discloses a system for estimating wheat light energy utilization efficiency, including:

[0061] Data acquisition module: used to collect vegetation data at different growth stages of wheat;

[0062] Actual light energy utilization calculation module: used to measure parameters of vegetation data at different growth stages of wheat and calculate light energy utilization.

[0063] Composite Spectral Index Construction Module: This module is used to construct composite spectral indices by combining vegetation index optimization methods with the two-band ratio spectral parameter SR in the form of a secondary screening method, and then fits the composite spectral indexes with light energy utilization.

[0064] Composite Spectral Index Estimation Module: When the fitting results meet the preset conditions, the composite spectral index is used to estimate the light energy utilization rate to be calculated.

[0065] Finally, this embodiment discloses a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of any one of the methods for estimating wheat light energy utilization.

[0066] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0067] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of estimating the light use efficiency of wheat, characterized by, The method comprises the following steps: Collecting vegetation data of wheat in different growth periods; Measuring parameters of the vegetation data of wheat in different growth periods respectively, and calculating light energy utilization rate; Using a secondary screening method combining vegetation index optimization method and two-band ratio spectral parameter SR in difference form to construct a composite spectral index, and fitting the composite spectral index with the light energy utilization rate; The vegetation index optimization method specifically comprises: analyzing the relationship between conventional spectral parameters and RUE, and selecting the two best spectral parameters MSR and DDn; The two-waveband ratio spectrum parameter SR screening method is specifically as follows: reading the spectrum reflectivity data every 2 wavebands in the whole spectrum waveband range, arranging the spectrum reflectivity data in a matrix form, fitting the spectrum index constructed by all two-waveband combinations with the RUE fitting equation, and simultaneously calculating the corresponding determination coefficient R 2 , drawing a contour map, determining the fluctuation range of R 2 according to the color difference, and further establishing the characteristic waveband combination; When the fitting result meets the preset condition, the composite spectral index is used to estimate the light energy utilization rate to be calculated.

2. The method of estimating light use efficiency of wheat according to claim 1, wherein, The vegetation parameter measurement includes biomass measurement, photosynthetically active radiation measurement and canopy spectral measurement.

3. The method of estimating light use efficiency of wheat according to claim 2, wherein, The steps of biomass measurement are as follows: separate the stems and leaves, and separately package and weigh the leaf and stem pieces; place them in a 105℃ oven to kill green for 30min to inactivate the leaves and stems, and dry them at 70℃ to constant weight, record the dry weight of the leaves and stems, and take the above-ground dry weight as DM, and calculate the biomass.

4. The method of estimating light use efficiency of wheat according to claim 2, wherein, A linear light quantum sensor is used to obtain the value of photosynthetically active radiation, the sensor is placed at a position 10cm above the upper layer of the wheat canopy in the selected measurement area, the total amount of photosynthetically active radiation above the canopy PARin is collected, then the sensor is placed at a position 2cm from the ground inside the canopy to collect the total amount of photosynthetically active radiation that penetrates the canopy PARthr, and finally the canopy photosynthetically active radiation interception rate iPAR is calculated: ； The photosynthetically active radiation absorbed and utilized by the canopy is: PAR=TSR×iPAR×0.5; Wherein, TSR represents the total amount of solar radiation.

5. A system for estimating light use efficiency of wheat, characterized by, It comprises: A data acquisition module for collecting vegetation data of wheat in different growth periods; An actual light energy utilization rate calculation module for measuring parameters of the vegetation data of wheat in different growth periods respectively, and calculating light energy utilization rate; Composite spectral index construction module: for the construction of composite spectral index by using the secondary screening method of vegetation index optimization method combined with two-band ratio spectral parameter SR in difference form, and fitting the composite spectral index with the light energy utilization rate; the vegetation index optimization method is as follows: analyzing the relationship between the conventional spectral parameters and RUE, and selecting the best two spectral parameters MSR and DDn; the two-band ratio spectral parameter SR screening method is as follows: reading the spectral reflectance data every 2 bands in all spectral band ranges, arranging in matrix form, fitting the spectral index constructed by all two-band combinations with the RUE fitting equation, and calculating the corresponding determination coefficient R 2 , and drawing the contour map, determining the fluctuation range of R 2 according to the color difference, and further establishing the characteristic band combination; A composite spectral index light energy utilization rate estimation module: when the fitting result meets the preset condition, the composite spectral index is used to estimate the light energy utilization rate to be calculated.

6. A computer storage medium, characterized in that The computer storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the method for estimating the light energy utilization rate of wheat according to any one of claims 1-4.

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