Simulation Method and Device for Content of Biochemical Components of Crops

Abstract

The present invention relates to the technical field of crop monitoring, and provides a method and device for simulating the content of biochemical components of crops. The method includes: obtaining preset parameter values of the content of biochemical components of each leaf position in the crop; constructing a maximum value distribution of the potential maximum parameter values of each leaf position in the vertical direction of the leaf position; based on the preset parameter values of the content of biochemical components of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, simulating the changes in the predicted parameter values of the content of biochemical components of each leaf position in the vertical direction of the leaf position with the change of growing degree days. The method provided by the present invention simulates the changes in the predicted parameter values of the content of biochemical components of each leaf position in the vertical direction of the leaf position with the change of growing degree days through the preset parameter values of the content of biochemical components of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, realizes the accurate prediction of the content of biochemical components of each leaf position, and provides a new model tool and scientific basis for crop growth monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of crop monitoring, and particularly relates to a method and device for simulating the content of biochemical components of crops. Background Art

[0002] Currently, the coupled maize model constructs a general maize 4D model driven by cumulative growing degree days by integrating a thermal-driven crop growth model and a plant functional-structure algorithm. This model realizes the simulation of maize canopy structure parameters at the organ level, individual level, phenological level, and population level and its computer visualization, effectively solving the problem that existing crop growth models and plant functional-structure models cannot simultaneously meet sensitive temperature response, accurate dynamic simulation, and multi-scale computer visualization.

[0003] However, the coupled maize model can only simulate the changes in maize canopy structure parameters, but cannot simulate the changes in the content of biochemical components during the growth cycle, and thus the prediction accuracy during the maize growth process needs to be further improved. Summary of the Invention

[0004] The present invention provides a method and device for simulating the content of biochemical components of crops, so as to solve the defect that the coupled maize model in the prior art cannot simulate the changes in the content of biochemical components during the growth cycle.

[0005] The present invention provides a method for simulating the content of biochemical components of crops, including:

[0006] Obtaining preset parameter values of the content of biochemical components at each leaf position of the crop, where the preset parameter values include potential maximum parameter values;

[0007] Constructing a maximum value distribution of the potential maximum parameter values at each leaf position in the vertical direction of the leaf position;

[0008] Determining the theoretical physiological accumulated temperature parameters required at each leaf position in each growth cycle;

[0009] Based on the preset parameter values of the content of biochemical components at each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, simulating the changes in the predicted parameter values of the content of biochemical components at each leaf position in the vertical direction of the leaf position with the change of growing degree days.

[0010] According to the method for simulating the content of biochemical components of crops provided by the present invention, the simulating the changes in the predicted parameter values of the content of biochemical components at each leaf position in the vertical direction of the leaf position with the change of growing degree days based on the preset parameter values of the content of biochemical components at each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters includes:

[0011] Construct a reduction factor for the leaf expansion rate of the crop based on the environmental stress factors of the crop.

[0012] Based on the preset parameter values of the biochemical component contents at each leaf position, the maximum value distribution, the theoretical physiological accumulated temperature parameter, and the reduction factor for the leaf expansion rate, simulate the change of the predicted parameter values of the biochemical component contents at each leaf position in the vertical direction of the leaf position with the change of the growing degree days.

[0013] According to a method for simulating the biochemical component content of a crop provided by the present invention, the environmental stress factors include at least one of the water content, nitrogen content, and oxygen content in the growth environment of the crop.

[0014] According to a method for simulating the biochemical component content of a crop provided by the present invention, the construction of the maximum value distribution of the potential maximum parameter values at each leaf position in the vertical direction of the leaf position includes:

[0015] Based on the potential maximum parameter values and empirical parameters of each leaf position under preset environmental conditions, fit to construct the maximum value distribution;

[0016] The empirical parameter is used to regulate the vertical distribution width and skewness degree of the maximum value distribution;

[0017] The maximum value distribution conforms to the basic distribution form of a bell shape and presents an asymmetric distribution.

[0018] According to a method for simulating the biochemical component content of a crop provided by the present invention, the preset parameter values further include the starting value of the biochemical component content and the residual value of the biochemical component content corresponding to the senescence stage.

[0019] According to a method for simulating the biochemical component content of a crop provided by the present invention, each growth cycle includes a leaf emergence cycle and a leaf expansion cycle;

[0020] The leaf expansion cycle is determined based on the potential maximum area of the leaves of the crop.

[0021] The present invention also provides a simulation device for the biochemical component content of a crop, including:

[0022] An acquisition unit, which acquires the preset parameter values of the biochemical component contents at each leaf position in the crop, and the preset parameter values include potential maximum parameter values;

[0023] A vertical profile construction unit, which constructs the maximum value distribution of the potential maximum parameter values at each leaf position in the vertical direction of the leaf position;

[0024] A physiological accumulated temperature determination unit, which determines the theoretical physiological accumulated temperature parameters required for each leaf position in each growth cycle;

[0025] A dynamic simulation unit simulates the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of growing degree days based on the preset parameter values of the biochemical component contents of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters.

[0026] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the simulation method for the biochemical component content of the crop as described in any one of the above is implemented.

[0027] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the simulation method for the biochemical component content of the crop as described in any one of the above is implemented.

[0028] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the simulation method for the biochemical component content of the crop as described in any one of the above is implemented.

[0029] The simulation method and device for the biochemical component content of the crop provided by the present invention, by obtaining the preset parameter values of the biochemical component contents of each leaf position in the crop, where the preset parameter values include potential maximum parameter values; constructing the maximum value distribution of the potential maximum parameter values of each leaf position in the vertical direction of the leaf position; determining the theoretical physiological accumulated temperature parameters required for each leaf position in each growth period; and based on the preset parameter values, maximum value distribution, and theoretical physiological accumulated temperature parameters of the biochemical component contents of each leaf position, simulating the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of growing degree days, realizes the accurate monitoring of the biochemical component contents of each leaf position, and provides a new model tool and scientific basis for the monitoring of crop growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1 is a schematic flow chart of the simulation method for the biochemical component content of the crop provided by the present invention;

[0032] Figure 2 is a schematic diagram of the dynamic changes in the biochemical component content of the leaf position in each growth period provided by the present invention;

[0033] Figure 3 It is a scatter plot of the measured values and predicted values of the hierarchical component contents provided by the present invention;

[0034] Figure 4 It is one of the schematic diagrams showing the changes of the physical and chemical parameters of the maize canopy simulated by the coupled maize model provided by the present invention with the growth season;

[0035] Figure 5 It is another schematic diagram showing the changes of the physical and chemical parameters of the maize canopy simulated by the coupled maize model provided by the present invention with the growth season;

[0036] Figure 6 It is yet another schematic diagram showing the changes of the physical and chemical parameters of the maize canopy simulated by the coupled maize model provided by the present invention with the growth season;

[0037] Figure 7 It is a schematic structural diagram of the simulation device for the biochemical component contents of the crops provided by the present invention;

[0038] Figure 8 It is a schematic structural diagram of the electronic device provided by the present invention. Detailed implementation manners

[0039] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0040] The current coupled maize model has been verified by measured data, proving that the model shows high accuracy in simulating the maize canopy structure parameters, such as leaf length, leaf width, leaf area, leaf insertion height, leaf vein curve, plant height and stem diameter, etc. The parameters at the individual level cover the plant height, leaf number, azimuth angle and zenith angle of the plant. At the population level, the model can simulate parameters such as the leaf area index, leaf inclination angle, azimuth angle distribution and plant density of the maize population. In addition, the main function at the phenological level is to describe the seasonal changes of the above parameters with the growth degree days. It is particularly worth pointing out that the coupled maize model has the ability to perform hierarchical statistics on biophysical parameters such as the leaf area index, leaf inclination angle and azimuth angle distribution, so as to flexibly meet the requirements of the vertical heterogeneous canopy radiation transfer model for input parameters.

[0041] However, the current coupled maize model can only simulate the changes in the canopy structure parameters of maize, but cannot simulate the changes in the content of biochemical components during the growth cycle. Therefore, the accuracy of monitoring during the maize growth process needs to be further improved. To address this problem, the present invention provides a method for simulating the content of biochemical components in crops to achieve the simulation of the changes in the content of biochemical components during the growth cycle. Figure 1 is a schematic flowchart of the method for simulating the content of biochemical components in crops provided by the present invention, as Figure 1 shown, the method includes:

[0042] Step 110, obtaining preset parameter values of the content of biochemical components at each leaf position in the crop, where the preset parameter values include potential maximum parameter values;

[0043] Here, the preset parameter values of the content of biochemical components at each leaf position in the crop can be used to reflect the theoretical values or empirical values corresponding to the content of each biochemical component at the start and end stages of the life cycle of the leaves at each leaf position in the crop, and can include potential maximum parameter values, initial biochemical component values, and residual biochemical component values at senescence. Among them, the content of biochemical components can refer to the content values of biological or chemical components in the crop, and can include chlorophyll content, carotenoid content, equivalent water thickness, and dry matter content, etc. In addition, the potential maximum parameter value here can be used to reflect the maximum value, that is, the highest content value, of the content of different biochemical components at each leaf position during the entire growth cycle.

[0044] Specifically, through comprehensive analysis of the actual measurement data of the crop or literature research, the preset parameter values of each biochemical component at different leaf positions in the crop under suitable environmental conditions can be determined as auxiliary parameters for simulating the content of biochemical components in the crop. For example, when the crop is maize, the potential maximum parameter values, initial biochemical component values, and residual biochemical component values at senescence of the biochemical components of the leaves at different leaf positions of maize can be obtained under suitable nitrogen and water conditions.

[0045] It should be noted that considering the differences in the spatial distribution of conditions such as light, water, and nutrients and the changes in the structure parameters and biochemical components of the crop at different growth stages, the physical and chemical parameters (canopy structure parameters and biochemical component parameters) of the crop show great inhomogeneity. Therefore, by obtaining the preset parameter values of the biochemical components at each leaf position in the crop, accurate and comprehensive dynamic simulation of the biochemical components can be achieved.

[0046] Step 120, constructing a maximum value distribution of the potential maximum parameter values at each leaf position in the vertical direction of the leaf position;

[0047] Here, the vertical direction of the leaf position refers to the direction perpendicular to the ground.

[0048] Specifically, it can be achieved by constructing the maximum value distribution of the potential maximum parameter values of the biochemical component contents at each leaf position in the vertical direction of the leaf position. For example, under suitable nitrogen and water conditions, the maximum value distribution of the potential maximum parameter values of the biochemical component contents at each leaf position within the maize canopy can be constructed in the vertical direction of the leaf position, that is, the vertical distribution profile of the potential maximum parameter values of the biochemical component contents is constructed. In one embodiment, constructing the maximum value distribution of the potential maximum parameter values at each leaf position in the vertical direction of the leaf position can be expressed by the following formula, as shown below:

[0049] ;

[0050] In the formula, represents the potential maximum value of each biochemical component content of the leaf corresponding to leaf position i under optimal nitrogen and water conditions; represents the maximum value of; represents the leaf order when, that is, the inflection point position of the bell-shaped curve; represents the i-th leaf position; a, b, and c are empirical parameters used to regulate the vertical distribution width and skewness degree of the variable.

[0051] Table 1 is the vertical profile table of the potential maximum values of the biochemical component contents of maize under optimal nitrogen and water conditions, as shown in Table 1 below:

[0052] Table 1. Vertical Profile Numerical Table of the Potential Maximum Values of the Biochemical Component Contents of Maize under Optimal Nitrogen and Water Conditions

[0053]

[0054] In Table 1, Cab represents the content of chlorophyll a and b, with the full name of Content of Chlorophyll a and b; Car represents the content of carotenoid, with the full name of Content of Carotenoid; Cw represents the equivalent water thickness, with the full name of Equivalent Water Thickness; Cdm represents the dry matter content, with the full name of Content of Dry Matter; TKN represents the total Kjeldahl nitrogen content, with the full name of Total Kjeldahl Nitrogen. It should be noted that the maximum number of leaves of this genotype of maize is 20, and the maximum number of leaves affects the position of the inflection point of the bell-shaped curve and the magnitude of the relevant parameters of the physiological accumulated temperature.

[0055] Step 130, determine the theoretical physiological accumulated temperature parameters required for each leaf position in each growth cycle;

[0056] Here, the theoretical physiological accumulated temperature parameter refers to the fact that crops require certain temperature or heat conditions during their growth and development. It should be noted that when all other conditions required for crop growth and development are met, within a certain temperature range, the air temperature and the development rate are positively correlated, and a certain total temperature needs to be accumulated to complete its development period.

[0057] Specifically, the theoretical physiological accumulated temperature parameters required for each leaf position of the crop in each growth cycle can be calculated, such as calculating the theoretical physiological accumulated temperature parameters required for the leaf eclosion cycle and the leaf expansion cycle.

[0058] Step 140, based on the preset parameter values of the biochemical component contents of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, simulate the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of growing degree days.

[0059] Here, the growing degree days can be used to represent the effective heat required for crop growth, and can be obtained by calculating the sum of the difference between the daily average air temperature above a certain reference temperature (such as the biological lower limit temperature) and the reference temperature within a certain period of time (such as one day). Thus, the monitoring of the biochemical component contents of each leaf position of the crop within a certain period of time can be realized.

[0060] Specifically, the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of growing degree days can be simulated through the preset parameter values of the biochemical component contents of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters. For example, a mathematical model can be constructed through the preset parameter values of the biochemical component contents of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters to complete the simulation of the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of growing degree days. Finally, the changes in the biochemical component contents of different leaf positions with the physiological accumulated temperature can be represented by a piecewise function.

[0061] The method provided by the embodiment of the present invention realizes the accurate monitoring of the biochemical component contents of each leaf position by obtaining the preset parameter values of the biochemical component contents of each leaf position in the crop, where the preset parameter values include the potential maximum parameter values; constructing the maximum value distribution of the potential maximum parameter values of each leaf position in the vertical direction of the leaf position; determining the theoretical physiological accumulated temperature parameters required for each leaf position in each growth cycle; and simulating the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of growing degree days based on the preset parameter values of the biochemical component contents of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, providing a new model tool and scientific basis for the monitoring of crop growth.

[0062] It should be noted that environmental stress has a significant impact on the growth and development of maize, not only delaying the growth process of maize, shortening the leaf lifespan, accelerating the senescence process of maize, but also affecting the vertical distribution of the biochemical parameters of the maize canopy. The current coupled maize model does not consider the response mechanism of crops to environmental stress, that is, in the growth monitoring of maize, the impact of environmental stress on the growth process of maize is not considered. To address this issue, in order to further improve the monitoring accuracy of crops, based on any of the above embodiments, step 140 includes:

[0063] Based on the environmental stress factors of the crop, construct a reduction factor for the leaf expansion rate of the crop;

[0064] Based on the preset parameter values of the biochemical component contents at each leaf position, the maximum value distribution, the theoretical physiological accumulated temperature parameter, and the reduction factor for the leaf expansion rate, simulate the changes in the predicted parameter values of the biochemical component contents at each leaf position in the vertical direction of the leaf position with the change of the growing degree days.

[0065] Here, the environmental stress factors can be used to reflect the impact on the crop leaves when the water content, nitrogen content, and oxygen content in the crop growth environment are in shortage. In addition, the reduction factor for the leaf expansion rate here can be used to reflect the response mechanism of the leaf expansion rate to the growth environment stress.

[0066] Specifically, the reduction factor for the leaf expansion rate of the crop can be constructed through the environmental stress factors of the crop to reflect the response mechanism of the crop to the growth environment stress. The reduction factor for the leaf expansion rate here can be expressed by the following formula:

[0067] ;

[0068] In the formula, represents the reduction factor for the leaf expansion rate; , and respectively represent the daily stress factors on the crop due to the shortage of water, nitrogen, and oxygen; , are the response coefficients for adjusting the leaf expansion rate respectively; GDD (Growing Degree Days) represents the growing degree days; represents the GDD when the last leaf of the crop begins to expand and grow, which is used to divide the vegetative period and reproductive period of maize. When maize is in the vegetative period, , ; when maize is in the reproductive period, , . Thus, the leaf expansion rate can be calculated through the following formula, as shown in the following formula:

[0069] ;

[0070] In the formula, represents the leaf expansion rate of the i-th leaf position at the current growing degree day; represents the leaf expansion rate of the i-th leaf position at the previous growing degree day; represents the leaf expansion rate reduction factor at the current growing degree day.

[0071] It should be noted that by introducing the leaf expansion rate reduction factor of the crop, the response of the crop to environmental stress is represented, providing a new perspective for deeply understanding the response mechanism of the crop to environmental stress, and further providing a more accurate model tool for crop remote sensing monitoring.

[0072] Furthermore, through the preset parameter values, maximum value distributions, theoretical physiological accumulated temperature parameters, and leaf expansion rate reduction factors of the biochemical component contents of each leaf position, the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of the growing degree day can be simulated. For example, the changes in the predicted parameter values of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of the growing degree day here can be represented by the following formula, as shown in the following formula:

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] Among them, ;

[0079] In the formula, , , , and respectively represent the contents of chlorophyll a and b in the i-th leaf at the accumulated temperature GDD ( ), the content of carotenoids ( ), the equivalent water thickness ( ), the dry matter content (Cdm), and the Kjeldahl nitrogen content (TKN); it should be noted that in the following interpretations of the formula parameters, the contents of chlorophyll a and b ( ), the content of carotenoids ( ), the equivalent water thickness ( ), the dry matter content (Cdm) and the Kjeldahl nitrogen content (TKN) are collectively referred to as the biochemical component content; , , , and are the potential maximum values of the biochemical component contents of the i-th leaf under the optimal nitrogen and water conditions, respectively; , , , and represent the initial values of the biochemical component contents of the i-th leaf, and their values are , , , and 20% of; , , , and are the residual values of the biochemical component contents of the i-th leaf after senescence, and are 0 , 0 , 80% of and 80% of; and are empirical parameters used to adjust the change rate of biochemical parameters in the leaves at different growth and development stages. is the water and fertilizer abundance adjustment factor, under the optimal nitrogen and water conditions . Here, assuming that the influence of environmental stress on canopy structure parameters and biochemical components is synchronous and consistent, then .

[0080] is the theoretical physiological accumulated temperature parameter of the i-th leaf position at different growth stages; and are the GDDs when the i-th leaf emerges from eclosion to the tip and begins to senesce, respectively; and are the GDDs when the leaf area expansion and senescence of the i-th leaf reach 100% of the potential maximum leaf area, respectively. Additionally, more precisely, it can be represented by and represent the GDDs when the leaf area expansion and senescence of the i-th leaf reach 50% of the potential maximum leaf area, respectively.

[0081] In one embodiment, the dynamic changes of the biochemical component contents in the leaves at different growth and development stages can be obtained by fitting the parameter reference values obtained from the measured data. Among them, the parameter reference values include the contents of chlorophyll a and b at each leaf position when the maximum number of maize leaves is 20 ( ), carotenoid content ( ), equivalent water thickness ( ), dry matter content (Cdm), and the necessary parameters in the dynamic model of the vertical profile of Kjeldahl nitrogen content (TKN). Figure 2 is a schematic diagram of the dynamic changes in the biochemical component content of leaf positions provided by the present invention in each growth cycle. As Figure 2 shown, the horizontal axis represents the growing degree days GDD. As the corn grows and develops, its corresponding growing degree days also increase. TimeSn represents the growth and development time of the corn. The left vertical axis represents the content of chlorophyll a and b ( ), and carotenoid content ( ), and the right vertical axis represents the equivalent water thickness ( ), dry matter content (Cdm). The measurement units and ranges of the two vertical axes are different.

[0082] As can be seen from Figure 2 , Figure 2 shows the dynamic changes in the biochemical component content in the 15th leaf (the rod leaf when the maximum number of leaves is 20) at different stages of growth and development, showing a similar trend of growth curve. Specifically, in the leaf expansion phase, the biochemical component content of the leaf gradually increases; in the leaf functional phase, the biochemical component content of the leaf basically remains stable with small fluctuations; while in the leaf senescence phase, the biochemical component content of the leaf rapidly decreases.

[0083] It should be noted that the method provided by the embodiments of the present invention can construct a reduction factor for the leaf expansion rate of the crop through the environmental stress factors of the crop, and can also introduce the constructed reduction factor for the leaf expansion rate into the existing coupled corn model to represent the response of the corn canopy structure parameters to environmental stress, thereby making the simulation of the corn canopy structure parameters by the existing coupled corn model more accurate and closer to the actual situation.

[0084] It should also be noted that here, the effect of accelerating corn senescence under environmental stress can be simulated by introducing a reduction factor for leaf lifespan. The reduction factor for leaf lifespan reduces the daily leaf lifespan. The reduction factor for leaf lifespan can be expressed by the following formula, as shown below:

[0085] ;

[0086] represents the reduction factor for leaf lifespan; , They are the response coefficients of leaf lifespan to environmental stress factors. Thus, the leaf lifespan can be calculated through the following formula, as shown below:

[0087] ;

[0088] In the formula, represents the leaf lifespan of the i-th leaf position; is the value of the previous day; is the asymptote of the Gaussian curve describing the crop leaf lifespan.

[0089] Compared with the existing coupled maize model, the simulation method for the biochemical component content of the crop provided by the embodiments of the present invention proposes a coupled maize model that comprehensively considers the growth and stress characteristics of maize. It can not only achieve the dynamic simulation of the vertical profile of the biochemical components in the maize canopy, but also improve the response mechanism of physical and chemical parameters to environmental stress, providing a new model tool and scientific basis for crop growth monitoring. At the same time, the improved coupled maize model can finely depict the maize canopy structure and optical characteristics, thereby establishing a connection between the maize coupled model and the remote sensing radiation transfer model, providing a brand-new idea for remote sensing monitoring of crop growth and early warning of stress.

[0090] Based on any of the above embodiments, the environmental stress factors include at least one of the water content, nitrogen content, and oxygen content in the growth environment of the crop.

[0091] It should be noted that the specific parameter values included in the environmental stress factors here can be determined according to the type of crop. For example, when the type of crop is maize, the environmental stress factors here include the water content, nitrogen content, and oxygen content in the growth environment of the crop.

[0092] Based on any of the above embodiments, step 120 includes:

[0093] Fitting based on the potential maximum parameter values and empirical parameters of each leaf position under preset environmental conditions to construct the maximum value distribution;

[0094] The empirical parameters are used to regulate the vertical distribution width and skewness degree of the maximum value distribution;

[0095] The maximum value distribution conforms to the basic distribution form of a bell shape and shows an asymmetric distribution.

[0096] Specifically, constructing the maximum value distribution of the potential maximum parameter values of each leaf position in the vertical direction of the leaf position can be expressed by the following formula, as shown below: ;

[0097] In the formula, represents the potential maximum value of the content of each biochemical component of the leaf corresponding to leaf position i under optimal nitrogen and water conditions; represents the maximum value of; represents the phyllotaxy at this time, that is, the inflection point position of the bell-shaped curve; represents the i-th leaf position; a, b, and c are empirical parameters used to regulate the vertical distribution width and skewness of the variable.

[0098] Based on any of the above embodiments, the preset parameter values further include the starting value of the biochemical component content and the residual value of the biochemical component content corresponding to the senescence stage.

[0099] Based on any of the above embodiments, each growth cycle includes a leaf emergence cycle and a leaf expansion cycle;

[0100] The leaf expansion cycle is determined based on the potential maximum area of the leaves of the crop.

[0101] Among them, the leaf expansion cycle can include 50% of the potential maximum area of the leaf and 100% of the potential maximum area.

[0102] To verify the simulation method for the content of biochemical components of the crop provided by the embodiments of the present invention, in one embodiment, the accuracy of the coupled maize model in dynamically simulating the vertical profile of maize canopy components can be tested based on the measured data of the content of biochemical components during the maize growth cycle. Due to the stratified assay method, the biochemical components of maize leaves in 30 plots in 2023 were determined in the laboratory, and the average value of the components in each layer of each plot was used as the measured value. At the same time, the simulated vertical profile of the components was weighted and averaged by leaf area layer by layer, and this was used as the predicted value. Figure 3 is a scatter plot of the measured and predicted values of the stratified component content provided by the present invention. Among them, when simulating the maize scenario, the following parameters were set: genotype characteristics, including the maximum number of maize leaves Nt being 20; biochemical parameters: under optimal nitrogen and water conditions, the maximum chlorophyll content Chlmax is 75 ug / cm 2 , the maximum carotenoid content Carmax is 13.4 ug / cm 2 , the maximum equivalent water thickness Cwmax is 0.0148 g / cm 2 , the maximum dry matter content Cdmmax is 0.00441 g / cm 2 .

[0103] It should be noted that at GDD of 357 and 447 At that time, the average heights of corn stalks (excluding leaves) were 9.9 cm and 23.9 cm respectively. Therefore, the stratified component contents were divided into 1 layer (357 GDD - all, 447 GDD - all), and the stratified component contents are represented by dots in the scatter plot; at GDD of 683 At that time, the average height of the corn stalk was 77.8 cm, so the stratified component content was divided into 2 layers (683 GDD - top, 683 GDD - bottom), which are represented by triangles in the scatter plot; at GDD of 1031 At that time, the average height of the corn stalk was 235.2 cm, so the stratified component content was divided into 3 layers (1031 GDD - top, 1031 GDD - middle, 1031 GDD - bottom), which are represented by squares in the scatter plot; at GDD of 1452 At that time, the average height of the corn stalk was 240.5 cm, so the stratified component content was divided into 3 layers (1452 GDD - top, 1452 GDD - middle, 1452 GDD - bottom), which are represented by rhombuses in the scatter plot. In addition, the blue solid line in the figure represents the regression line of the measured value and the predicted value, and the black dashed line represents the 1:1 diagonal line.

[0104] As Figure 3 shown, Figure 3 In a, the ordinate represents the predicted values of the component contents of chlorophyll a and b in each layer, and the abscissa represents the actual values of the component contents of chlorophyll a and b in each layer. The linear equation of the regression line is y = 0.7602x + 13.386. Similarly, Figure 3 In b, the ordinate represents the predicted values of the component contents of carotenoids in each layer, and the abscissa represents the actual values of the component contents of carotenoids in each layer. The linear equation of the regression line is y = 0.9962x - 0.0154; Figure 3 In c, the ordinate represents the predicted values of the equivalent water thickness in each layer, and the abscissa represents the actual values of the equivalent water thickness in each layer. The linear equation of the regression line is y = 0.9775x + 0.0004; Figure 3 In d, the ordinate represents the predicted values of the dry matter content in each layer, and the abscissa represents the actual values of the dry matter content in each layer. The linear equation of the regression line is y = 0.3826x + 0.0023. Among them, RMSE (Root Mean Square Error) represents the root mean square error between the measured values and the predicted values of the leaf biochemical components; NRMSE (Normalized Root Mean Square Error) represents the normalized root mean square error between the measured values and the predicted values of the leaf biochemical components.

[0105] ByFigure 3 It can be seen that the NRMSE between the measured values and predicted values of the leaf biochemical components ranges from 0.034 to 0.109, indicating that the method provided by the embodiments of the present invention has high prediction accuracy for leaf biochemical components. It should be noted that for each component, the method provided by the embodiments of the present invention generally overestimates low values and underestimates high values, especially in the scatter plots of chlorophyll content ( Figure 3 a) and dry matter content ( Figure 3 d), which is more obvious. In addition, for the prediction of the low carotenoid content range ( Figure 3 b), the method provided by the embodiments of the present invention also has an underestimation phenomenon. From the perspective of growth stage and vertical stratification, during the vegetative stage (357, 447, and 683 ), the vertical heterogeneity of the maize canopy is not obvious, and both the measured values and predicted values of the canopy biochemical component contents show an increasing trend, which is consistent with the assumption when constructing the vertical profile. As it enters the functional stage (1031 ), the vertical heterogeneity of the maize canopy begins to appear and becomes very significant during the reproductive stage (1452 ). Taking 1452 as an example, the pigment content shows a bell-shaped distribution in the vertical direction. The middle ear leaf group is in the functional stage, and the leaf pigment content reaches the peak. The upper grain leaf group is in the growth stage, and the leaf pigment content continues to increase, while the leaf pigment content of the lower stem leaf group decreases significantly; the dry matter content first increases and then remains basically stable during the leaf development process, showing an increasing trend from top to bottom in the vertical direction.

[0106] In addition, Figures 4 to 6 shows a schematic diagram of the coupled maize model provided by the present invention simulating the changes of the physical and chemical parameters of the maize canopy during the growing season. Specifically, the coupled maize model can be used to simulate the vertical profiles of the biophysical and biochemical parameters of the maize canopy at six time phases with GDD being 400, 600, 800, 1000, 1200, and 1400 respectively. These six time phases correspond to V6, V10, V14, VT, R2, and R4 of the maize growth and development process in sequence. Among them, when simulating the maize scenario, the scenario information is set as follows: the number of columns is 4, the number of rows is 8, the plant spacing PlantSpacing is 25.6 cm, and the row spacing RowSpacing is 65 cm (about 60000 plants / hm 2 ); the gene phenotype parameters and biochemical parameters are consistent with those in Figure 3 .

[0107] Among them, Figure 4 is one of the schematic diagrams of the coupled maize model provided by the present invention simulating the changes of the physical and chemical parameters of the maize canopy during the growing season. As shown in Figure 4 , Figure 4-a1 to f1 show a three-dimensional view of the corn three-dimensional scene. Figure 4 -a2 to f2 show a top view of the corn three-dimensional scene. For any three-dimensional view of the corn three-dimensional scene, the Z-axis of the three-dimensional coordinate system represents the height of the corn, with the unit of cm; the X-axis and Y-axis respectively represent the corresponding positions of the corn in the x-axis direction and y-axis direction in the actual scene, with the unit of cm. Additionally, for any top view of the corn three-dimensional scene, the X-axis and Y-axis in the two-dimensional coordinate system respectively represent the corresponding positions of the corn in the x-axis direction and y-axis direction in the actual scene, with the unit of cm.

[0108] Figure 5 It is the second schematic diagram showing the variation of the physical and chemical parameters of the corn canopy simulated by the coupled corn model provided by the present invention with the growth season. Figure 5 -a3 to f3 and Figure 5 -a4 to f4 respectively count the distribution of the leaf azimuth angle and leaf inclination angle of the corn canopy scene. LAA represents the leaf azimuth angle, and LIA represents the leaf inclination angle, with the unit of degree. As Figure 5 shown, as the growth and development process progresses, with the number of corn plants in the scene remaining unchanged, the number of leaves increases, and the overall distribution of the leaf azimuth angle gradually tends to a uniform distribution. At the same time, because the lower-layer leaves are relatively flat and the middle- and upper-layer leaves are more upright, the overall leaf inclination angle gradually increases during the vegetative period and stabilizes during the reproductive period.

[0109] Figure 6 It is the third schematic diagram showing the variation of the physical and chemical parameters of the corn canopy simulated by the coupled corn model provided by the present invention. As Figure 6 shown, Figure 6 -a5 to f5 reflect the contributions of the leaves at each leaf position to the total leaf area index LAI (Leaf Area Index) and the green leaf area index GreenLAI during different growth stages of the corn. It can be seen that the simulation data can well reproduce the bell-shaped distribution characteristics of the leaf areas of each leaf position of the corn. At the same time, the total LAI gradually increases during the vegetative period and stabilizes during the reproductive period; GreenLAI gradually increases during the vegetative period and gradually decreases during the reproductive period, and the proportion of the green leaf area gradually decreases throughout the growth period. Among them, the ordinate represents the leaf area index LAI, with the unit of ( / ) and the abscissa represents the leaf position number.

[0110] Additionally, Figure 6 -a6 to f6 and Figure 6-a7 to f7 respectively show the vertical profiles of chlorophyll content Cab, carotenoid content Car, equivalent water thickness Cw, and dry matter content Cdm at different growth stages of maize. Among them, Chl represents Chlorophyll, the chlorophyll; the vertical axis represents the content of each biochemical component, and the horizontal axis represents the leaf sequence number (Leaf sequence).

[0111] Based on any one of the above embodiments, Figure 7 is a schematic structural diagram of a simulation device for the content of biochemical components of crops provided by the present invention, as Figure 7 shown, the device includes:

[0112] An acquisition unit 710, which acquires the preset parameter values of the content of biochemical components at each leaf position of the crop, and the preset parameter values include potential maximum parameter values;

[0113] A vertical profile construction unit 720, which constructs the maximum value distribution of the potential maximum parameter values at each leaf position in the vertical direction of the leaf position;

[0114] A physiological accumulated temperature determination unit 730, which determines the theoretical physiological accumulated temperature parameters required for each leaf position in each growth cycle;

[0115] A dynamic simulation unit 740, based on the preset parameter values of the content of biochemical components at each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, simulates the changes in the predicted parameter values of the content of biochemical components at each leaf position in the vertical direction of the leaf position with the change of growing degree days.

[0116] The device provided by the embodiment of the present invention realizes accurate monitoring of the content of biochemical components at each leaf position by acquiring the preset parameter values of the content of biochemical components at each leaf position of the crop, where the preset parameter values include potential maximum parameter values; constructing the maximum value distribution of the potential maximum parameter values at each leaf position in the vertical direction of the leaf position; determining the theoretical physiological accumulated temperature parameters required for each leaf position in each growth cycle; and simulating the changes in the predicted parameter values of the content of biochemical components at each leaf position in the vertical direction of the leaf position with the change of growing degree days, providing a new model tool and scientific basis for the monitoring of crop growth.

[0117] Based on any one of the above embodiments, the dynamic simulation unit is specifically used for:

[0118] Based on the environmental stress factors of the crop, constructing a reduction factor for the leaf expansion rate of the crop;

[0119] Based on the preset parameter values of the biochemical component contents at each leaf position, the maximum value distribution, the theoretical physiological accumulated temperature parameter, and the leaf expansion rate reduction factor, the changes in the predicted parameter values of the biochemical component contents at each leaf position in the vertical direction of the leaf position with the change of the growing degree days are simulated.

[0120] Based on any of the above embodiments, the environmental stress factor includes at least one of the water content, nitrogen content, and oxygen content in the growth environment of the crop.

[0121] Based on any of the above embodiments, the vertical profile construction unit is specifically configured to:

[0122] Based on the potential maximum parameter values and empirical parameters of each leaf position under preset environmental conditions, the maximum value distribution is constructed by fitting.

[0123] The empirical parameter is used to regulate the vertical distribution width and skewness degree of the maximum value distribution.

[0124] The maximum value distribution conforms to the basic distribution form of a bell shape and presents an asymmetric distribution.

[0125] Based on any of the above embodiments, the preset parameter values further include the starting value of the biochemical component content and the residual value of the biochemical component content corresponding to the senescence stage.

[0126] Based on any of the above embodiments, each growth cycle includes a leaf emergence cycle and a leaf expansion cycle.

[0127] The leaf expansion cycle is determined based on the potential maximum area of the leaves of the crop.

[0128] Figure 8 The schematic diagram of the physical structure of an electronic device is exemplified, such as Figure 8As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communications interface 820, and the memory 830 complete communication with each other through the communication bus 840. The processor 810 may call the logical instructions in the memory 830 to execute a method for simulating the biochemical component content of crops. The method includes: obtaining preset parameter values of the biochemical component content of each leaf position in the crops, where the preset parameter values include potential maximum parameter values; constructing a maximum value distribution of the potential maximum parameter values of each leaf position in the vertical direction of the leaf position; determining the theoretical physiological accumulated temperature parameters required for each leaf position in each growth period; based on the preset parameter values of the biochemical component content of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, simulating the change of the predicted parameter values of the biochemical component content of each leaf position in the vertical direction of the leaf position with the change of growing degree days.

[0129] In addition, when the logical instructions in the above-mentioned memory 830 are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0130] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the simulation method for the biochemical component content of crops provided by the above-mentioned various methods. The method includes: obtaining preset parameter values of the biochemical component content of each leaf position in the crop, where the preset parameter values include potential maximum parameter values; constructing a maximum value distribution of the potential maximum parameter values of each leaf position in the vertical direction of the leaf position; determining the theoretical physiological accumulated temperature parameters required for each leaf position in each growth period; and based on the preset parameter values of the biochemical component content of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, simulating the changes in the predicted parameter values of the biochemical component content of each leaf position in the vertical direction of the leaf position with the change of growth degree days.

[0131] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the simulation method for the biochemical component content of crops provided by the above-mentioned various methods. The method includes: obtaining preset parameter values of the biochemical component content of each leaf position in the crop, where the preset parameter values include potential maximum parameter values; constructing a maximum value distribution of the potential maximum parameter values of each leaf position in the vertical direction of the leaf position; determining the theoretical physiological accumulated temperature parameters required for each leaf position in each growth period; and based on the preset parameter values of the biochemical component content of each leaf position, the maximum value distribution, and the theoretical physiological accumulated temperature parameters, simulating the changes in the predicted parameter values of the biochemical component content of each leaf position in the vertical direction of the leaf position with the change of growth degree days.

[0132] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating the content of biochemical components of crops, characterized in that: include: Obtaining preset parameter values ​​of biochemical component contents at various leaf positions in the crop, wherein the preset parameter values ​​include potential maximum parameter values; Constructing the maximum value distribution of the potential maximum parameter value of each leaf position in the vertical direction of the leaf position; Determining the theoretical physiological accumulated temperature parameters required for each leaf position in each growth cycle; Based on the preset parameter values ​​of the biochemical component contents of each leaf position, the maximum value distribution and the theoretical physiological accumulated temperature parameter, the change of the predicted parameter values ​​of the biochemical component contents of each leaf position in the vertical direction of the leaf position with the change of growing degree days is simulated; The preset parameter values ​​of the biochemical component contents of the leaf positions, the maximum value distribution and the theoretical physiological accumulated temperature parameters are used to simulate the changes of the predicted parameter values ​​of the biochemical component contents of the leaf positions in the vertical direction of the leaf positions as the growing degree days change, including: constructing a leaf expansion rate reduction factor of the crop based on the environmental stress factor of the crop; Based on the preset parameter values ​​of the biochemical component content of each leaf position, the maximum value distribution, the theoretical physiological accumulated temperature parameter and the leaf expansion rate reduction factor, the change of the predicted parameter value of the biochemical component content of each leaf position in the vertical direction of the leaf position with the change of the growing degree day is simulated, which is expressed as follows: ； ； ； ； ； in, ； In the formula, , , , and Respectively represent the chlorophyll a and b contents of the i-th leaf at accumulated temperature GDD ( )、Carotenoid content( )、Equivalent water thickness( ), dry matter content (Cdm) and Kjeldahl nitrogen content (TKN); The biochemical component content includes: the content of chlorophyll a and b ( )、Carotenoid content( )、Equivalent water thickness( ), dry matter content (Cdm) and Kjeldahl nitrogen content (TKN); , , , and are the potential maximum values ​​of the contents of each biochemical component in the i-th leaf under the optimum nitrogen and water conditions; , , , and represents the initial value of the content of each biochemical component in the i-th leaf; , , , and is the residual value of each biochemical component content of the i-th leaf after senescence; and It is an empirical parameter used to regulate the rate of change of the content of endochemical components in leaves at different growth and development stages; It is the regulating factor of water and fertilizer abundance; is the theoretical physiological accumulated temperature parameter of the i-th leaf position at different growth stages; and are the growth degree days of leaf i from eclosion to tip emergence and onset of senescence, respectively; and are the growing degree days when the leaf area expansion and senescence of leaf i reach 100% of the potential maximum leaf area, respectively; The environmental stress factor includes at least one of water content, nitrogen content and oxygen content in the growth environment of the crop; The blade expansion rate reduction factor is expressed by the following formula: ； In the formula, represents the leaf expansion rate reduction factor; when the effects of environmental stress on canopy structural parameters and biochemical components are synchronous and consistent, ; , as well as represent the daily stress factors on crops due to shortage of water, nitrogen and oxygen respectively; , They are the response coefficients regulating the leaf expansion rate; The GDD refers to when the last leaf of a crop begins to expand and grow, and is used to divide the crop's vegetative period and reproductive period.

2. The method for simulating the content of biochemical components of crops according to claim 1, characterized in that: The constructing of the maximum value distribution of the potential maximum parameter value of each leaf position in the vertical direction of the leaf position includes: Based on the potential maximum parameter value of each leaf position under the preset environmental conditions and the empirical parameters, the maximum value distribution is constructed; The empirical parameters are used to adjust the vertical distribution width and skewness of the maximum value distribution; The maximum value distribution conforms to the bell-shaped basic distribution form and presents an asymmetric distribution.

3. The method for simulating the content of biochemical components of crops according to claim 1, characterized in that: The preset parameter values ​​also include the starting value of the biochemical component content and the residual value of the biochemical component content corresponding to the aging stage.

4. The method for simulating the content of biochemical components of crops according to claim 1, characterized in that: Each growth cycle includes a leaf eclosion cycle and a leaf expansion cycle; The leaf expansion period is determined based on the potential maximum area of ​​the leaves of the crop.

5. A device for simulating the content of biochemical components of crops, characterized in that: include: An acquisition unit, for acquiring preset parameter values ​​of the biochemical component content at each leaf position in the crop, wherein the preset parameter values ​​include a potential maximum parameter value; A vertical profile construction unit is used to construct the maximum value distribution of the potential maximum parameter value of each leaf position in the vertical direction of the leaf position; A physiological accumulated temperature determination unit is used to determine the theoretical physiological accumulated temperature parameters required for each leaf position in each growth cycle; A dynamic simulation unit simulates the change of the predicted parameter value of the biochemical component content of each leaf position in the vertical direction of the leaf position as the growing degree day changes based on the preset parameter value of the biochemical component content of each leaf position, the maximum value distribution and the theoretical physiological accumulated temperature parameter; The preset parameter values ​​of the biochemical component contents of the leaf positions, the maximum value distribution and the theoretical physiological accumulated temperature parameters are used to simulate the changes of the predicted parameter values ​​of the biochemical component contents of the leaf positions in the vertical direction of the leaf positions as the growing degree days change, including: constructing a leaf expansion rate reduction factor of the crop based on the environmental stress factor of the crop; Based on the preset parameter values ​​of the biochemical component content of each leaf position, the maximum value distribution, the theoretical physiological accumulated temperature parameter and the leaf expansion rate reduction factor, the change of the predicted parameter value of the biochemical component content of each leaf position in the vertical direction of the leaf position with the change of the growing degree day is simulated, which is expressed as follows: ； ； ； ； ； in, ； In the formula, , , , and Respectively represent the chlorophyll a and b contents of the i-th leaf at accumulated temperature GDD ( )、Carotenoid content( )、Equivalent water thickness( ), dry matter content (Cdm) and Kjeldahl nitrogen content (TKN); The biochemical component content includes: the content of chlorophyll a and b ( )、Carotenoid content( )、Equivalent water thickness( ), dry matter content (Cdm) and Kjeldahl nitrogen content (TKN); , , , and are the potential maximum values ​​of the contents of each biochemical component in the i-th leaf under the optimum nitrogen and water conditions; , , , and represents the initial value of the content of each biochemical component in the i-th leaf; , , , and is the residual value of each biochemical component content of the i-th leaf after senescence; and It is an empirical parameter used to regulate the rate of change of the content of endochemical components in leaves at different growth and development stages; It is the regulating factor of water and fertilizer abundance; is the theoretical physiological accumulated temperature parameter of the i-th leaf position at different growth stages; and are the growth degree days of leaf i from eclosion to tip emergence and onset of senescence, respectively; and are the growing degree days when the leaf area expansion and senescence of leaf i reach 100% of the potential maximum leaf area, respectively; The environmental stress factor includes at least one of water content, nitrogen content and oxygen content in the growth environment of the crop; The blade expansion rate reduction factor is expressed by the following formula: ； In the formula, represents the leaf expansion rate reduction factor; when the effects of environmental stress on canopy structural parameters and biochemical components are synchronous and consistent, ; , as well as represent the daily stress factors on crops due to shortage of water, nitrogen and oxygen respectively; , They are the response coefficients regulating the leaf expansion rate; The GDD refers to when the last leaf of a crop begins to expand and grow, and is used to divide the crop's vegetative period and reproductive period.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for simulating the content of biochemical components of crops according to any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for simulating the content of biochemical components of crops according to any one of claims 1 to 4 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for simulating the content of biochemical components of crops according to any one of claims 1 to 4 is implemented.

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