CN116862701A

CN116862701A - Method and device for constructing crop growth model

Info

Publication number: CN116862701A
Application number: CN202310814195.2A
Authority: CN
Inventors: 钱彬祥; 黄文江; 郭安廷; 叶回春
Original assignee: Aerospace Information Research Institute of CAS
Current assignee: Aerospace Information Research Institute of CAS
Priority date: 2023-07-04
Filing date: 2023-07-04
Publication date: 2023-10-10

Abstract

The application discloses a method and a device for constructing a crop growth model, which construct organ level based on organ level parameters; constructing an individual level based on the individual level parameter; constructing a season Xiang Shuiping based on the season phase level parameters; constructing population levels based on the population level parameters; in the embodiment of the application, the thermal driving equation adopted by the crop growth model is in an exponential form, so that the crop growth model is more sensitive to temperature change, and therefore, the sensitivity of the crop growth model to temperature response is improved, and the crop growth model can timely adjust the growth form of corn according to the change of the environmental temperature.

Description

Method and device for constructing crop growth model

Technical Field

The application relates to the field of model construction, in particular to a method and a device for constructing a crop growth model.

Background

The crop canopy structure parameters have important significance for ecological remote sensing, crop phenotype and grain safety, and when estimating the crop canopy structure parameters, the influence of temperature on crop growth needs to be accurately summarized.

Currently, the effect of temperature on the growth of crops (e.g. maize) is determined by an individual or group level model, a leaf or organ level model, and an enzyme or cell level model, that is, the above model determines the effect of temperature on crops by simulating the growth morphology of crops, however, since the above model does not take into account the effect of external environmental temperature on maize, the growth morphology of maize cannot be adjusted according to the change of environmental temperature.

Therefore, how to adjust the growth morphology of corn according to the change of the ambient temperature becomes a problem to be solved in the art.

Disclosure of Invention

The application provides a method and a device for constructing a crop growth model, and aims to adjust the growth form of corn according to the change of the ambient temperature.

In order to achieve the above object, the present application provides the following technical solutions:

a method of constructing a crop growth model, comprising:

constructing an organ level based on the organ level parameters;

constructing an individual level based on the individual level parameter;

constructing a season Xiang Shuiping based on the season phase level parameters;

constructing population levels based on the population level parameters;

a crop growth model is constructed based on the organ level, the individual level, the quaternary phase level, and the population level.

Optionally, the organ level parameters at least comprise corn stalk and vein curves;

the constructing an organ level based on the organ level parameter comprises:

geometrically processing the corn stalks to obtain geometrically processed corn stalks;

calculating to obtain the maximum blade width according to the preset maximum length of the blade;

determining the blade size according to the preset maximum length of the blade and the maximum width of the blade;

Normalizing the length of the blade segment according to the preset maximum length of the blade to obtain normalized She Duanchang;

calculating to obtain the leaf Duan Kuan according to the normalized leaf segment length and the preset maximum leaf length;

normalizing the vein curve to obtain a target vein curve;

according to a preset blade azimuth angle, calculating to obtain the blade azimuth angle;

organ levels are constructed based on the geometrically-derived corn stalks, the leaf dimensions, the leaf Duan Kuan, the target vein curve, and the leaf azimuth angle.

Optionally, the constructing the individual level based on the individual level parameter includes:

calculating to obtain the maximum blade length according to the maximum value of the preset total blade number, the minimum value of the preset total blade number and the minimum length of the preset maximum blade length;

determining a maximum blade width based on the maximum blade length;

calculating a leaf area of the maximum leaf based on the maximum leaf length and the maximum leaf width;

calculating the leaf position of the maximum leaf according to the maximum value of the preset total leaf number;

calculating potential leaf areas of all the blades according to the leaf areas of the maximum blades and the leaf positions of the maximum blades;

Calculating to obtain the sum of the leaf areas of the blades according to the potential leaf areas of the blades;

calculating the height of the insertion point according to the sum of the blade areas of the blades, the preset maximum number of blades and the preset blade height;

an individual level is constructed based on the leaf area of the largest leaf, the sum of the leaf areas of the leaves, and the insertion point height.

Optionally, the constructing the quaternary phase level based on the quaternary phase level parameter includes:

calculating the maximum height of the corn stalks according to the preset maximum height;

calculating to obtain the maximum diameter of the corn stalks according to the preset maximum diameter;

determining the initial rate of the blade tip according to a preset development rate function;

determining the starting quantity of the blades according to the starting speed of the blade tips;

calculating to obtain the occurrence rate of the blade tip according to the preset development rate function;

determining the number of blade occurrences based on the rate of blade tip occurrences;

determining a leaf expansion rate based on a preset maximum expansion rate;

determining the cumulative growth days of the crop based on a pre-established nonlinear temperature function;

calculating a blade expansion rate of the blade based on the blade expansion rate, the nonlinear function and a pre-calculated potential area of the blade;

Calculating to obtain the expansion area of the blade according to the potential area of the blade and the expansion rate of the blade;

calculating to obtain the aging rate of the blade based on the potential area of the blade and the nonlinear function;

calculating the aging area of the blade based on the potential area of the blade and a preset aging rate;

calculating the green leaf area based on the leaf unfolding area and the leaf aging area;

an individual level is constructed based on the maximum height of the corn stalk, the maximum diameter of the corn stalk, the starting number of leaves, the number of leaves present, the cumulative crop growth day, the leaf expansion of the leaves, the leaf senescence area, and the green leaf area.

Optionally, the constructing the population level based on the population level parameter includes:

establishing three-dimensional coordinates of the corn leaves or stems based on a preset average plant height, a preset average column spacing, a preset average row spacing, a preset average stem zenith angle and a preset azimuth angle;

calculating the group leaf area based on a pre-calculated leaf expansion area;

based on the three-dimensional coordinates of the corn leaf or stalk and the population leaf area, a population level is constructed.

Optionally, the method further comprises:

inputting preset parameters into a crop growth model to obtain a growth result output by the crop growth model; the growth results include at least leaf length, leaf width, leaf area of the leaf, insertion point height, and vein profile.

Optionally, the method further comprises:

calculating root mean square error and standard root mean square error of the blade length and the preset blade length;

and when the root mean square error is in a leaf error range and the standard root mean square error is in a standard leaf range, determining that the leaf length calculated by the crop growth model is accurate.

Optionally, the method further comprises:

calculating root mean square error and standard root mean square error of the blade width and the preset blade width;

and when the root mean square error is within a leaf width error range and the standard root mean square error is within a standard leaf width range, determining that the leaf width calculated by the crop growth model is accurate.

Optionally, the method further comprises:

calculating root mean square error and standard root mean square error of the leaf area of the leaf and the preset leaf area;

and when the root mean square error is in a leaf area error range and the standard root mean square error is in a standard leaf area range, determining that the leaf area of the leaf calculated by the crop growth model is accurate.

Optionally, the method further comprises:

calculating the root mean square error and the standard root mean square error of the height of the insertion point and the height of the preset insertion point;

and when the root mean square error is in a height error range and the standard root mean square error is in a standard height range, determining that the insertion point calculated by the crop growth model is highly accurate.

Optionally, the method further comprises:

calculating root mean square error and standard root mean square error of the vein curve and the preset vein curve;

and when the root mean square error is in a curve error range and the standard root mean square error is in a standard curve range, determining that the vein curve calculated by the crop growth model is accurate.

A device for constructing a crop growth model, comprising:

an organ level construction unit for constructing an organ level based on the organ level parameter;

an individual level construction unit for constructing an individual level based on the individual level parameter;

a quaternary phase level construction unit for constructing a quaternary Xiang Shuiping based on the quaternary phase level parameter;

a population level construction unit for constructing a population level based on the population level parameters;

a model building unit for building a crop growth model based on the organ level, the individual level, the quaternary phase level, and the population level.

According to the technical scheme provided by the application, the organ level is constructed based on the organ level parameters; constructing an individual level based on the individual level parameter; constructing a season Xiang Shuiping based on the season phase level parameters; constructing population levels based on the population level parameters; in the embodiment of the application, the thermal driving equation adopted by the crop growth model is in an exponential form, so that the crop growth model is more sensitive to temperature change, and therefore, the sensitivity of the crop growth model to temperature response is improved, and the crop growth model can timely adjust the growth form of corn according to the change of the environmental temperature.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for constructing a crop growth model according to an embodiment of the present application;

Fig. 2 is a schematic diagram of a corn stalk and corn leaf according to an embodiment of the present application;

FIG. 3 is a flow chart of a method for constructing organ level according to an embodiment of the present application;

fig. 4 is a schematic diagram of a corn stalk geometric treatment according to an embodiment of the present application;

FIG. 5 is a schematic view of a corn leaf according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a vein curve according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a planar vein curve according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a pulse curve angle of the present application;

FIG. 9 is a schematic diagram of a corn individual according to an embodiment of the present application;

FIG. 10 is a flow chart of a method for constructing individual levels according to an embodiment of the present application;

FIG. 11 is a schematic view of the maximum length, maximum width and potential leaf area as a function of leaf sequence provided by an embodiment of the present application;

FIG. 12 is a schematic view of the variation of insertion point height with phyllotaxis provided by an embodiment of the present application;

FIG. 13 is a schematic diagram showing the cumulative daily change of corn with crop provided by an embodiment of the present application;

FIG. 14 is a flow chart of a method for constructing quaternary phase level according to an embodiment of the present application;

FIG. 15 is a schematic view showing the variation of the maximum height of the corn stalks with the cumulative growth of the crops according to the embodiment of the application;

FIG. 16 is a schematic view of the maximum diameter of a corn stalk according to the embodiment of the present application as a function of the cumulative growth of the crop;

FIG. 17 is a schematic diagram of the variation of tip initiation rate, tip appearance rate blade initiation number and blade appearance number with cumulative crop growth days provided by an embodiment of the present application;

FIG. 18 is a graph showing the variation of leaf expansion rate with leaf sequence provided by an embodiment of the present application;

FIG. 19 is a graph showing the cumulative growth days and leaf expansion as a function of leaf sequence provided by an embodiment of the present application;

FIG. 20 is a schematic diagram of a nonlinear function according to an embodiment of the present application as a function of ambient temperature;

FIG. 21 is a schematic view of a blade deployment area according to an embodiment of the present application as a function of blade order;

FIG. 22 is a schematic view showing the change of leaf senescence area with leaf sequence according to the embodiment of the present application;

FIG. 23 is a schematic view showing the change of green leaf area with leaf sequence according to the embodiment of the present application;

FIG. 24 is a graph showing the cumulative daily change of corn groups with crop growth provided by an embodiment of the present application;

FIG. 25 is a flowchart of a method for constructing a population level according to an embodiment of the present application;

FIG. 26 is a flowchart of a blade length verification method according to an embodiment of the present application;

FIG. 27 is a graph showing a comparison of predicted and measured blade lengths according to an embodiment of the present application;

FIG. 28 is a flowchart of a blade width verification method according to an embodiment of the present application;

FIG. 29 is a graph showing a comparison of predicted and measured blade widths according to an embodiment of the present application;

FIG. 30 is a flow chart of a leaf area verification method provided by an embodiment of the present application;

FIG. 31 is a graph showing a comparison of predicted and measured values of leaf area provided by an embodiment of the present application;

FIG. 32 is a flowchart of a method for verifying the height of an insertion point according to an embodiment of the present application;

FIG. 33 is a graph showing a comparison of predicted and measured values of the height of an insertion point according to an embodiment of the present application;

FIG. 34 is a flowchart of a method for verifying a vein curve according to an embodiment of the present application;

FIG. 35 is a graph showing the comparison of the predicted and measured values of the vein curve according to the embodiment of the present application;

fig. 36 is a schematic diagram of an architecture of a device for constructing a crop growth model according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that the method for constructing a crop growth model provided by the embodiment of the application is particularly applied to constructing a corn growth model, wherein a corn individual consists of a plurality of plant organs including root systems, stems, leaves, ears, fruits and the like. In the embodiment of the application, the non-reproductive organs of the aerial parts of two corns, namely the stems and the leaves, are mainly considered. Corn stalks and leaves=affected by internal factors such as gene type and external factors such as climate, water and fertilizer stress, the embodiment of the application only considers the cumulative growth days (Growing Degree Days, GDD) of crops under ideal conditions _8℃ ) Influence on cornstalk.

As shown in fig. 1, a flowchart of a method for constructing a crop growth model according to an embodiment of the present application includes:

S101: organ levels are constructed based on the organ level parameters.

Among them, organ level parameters include, but are not limited to: corn stalks, leaf sequences of leaves, leaf lengths, leaf widths, leaf sizes, leaf vein curves, stalk bottom and top diameters, leaf heights, and leaf azimuth angles.

The leaf sequence of the leaves is essentially ordered in the order of growth and development of the leaves, i.e. the order of the leaves from near the ground to the top of the stalks. The blade leaf sequence closest to the ground is 1, and the highest blade leaf sequence is N _t (i.e., the maximum of the total number of blades).

Specifically, as shown in fig. 2, a schematic diagram of corn stalks and corn leaves is shown.

Optionally, in another embodiment of the present application, a specific implementation of step S101, as shown in fig. 3, includes the following steps:

s301: and carrying out geometric treatment on the corn stalks to obtain the geometrically-treated corn stalks.

It will be appreciated that the cornstalk may be represented by a plurality of stacked cones (frustums ofa cone). In order to facilitate computer visualization of the corn model, the round bench can be simplified by a regular hexagonal bench, at this time, the top diameter (stem top diameter) and the bottom diameter (stembottom diameter) of the ith stalk can be respectively represented by the diameters of the circumscribed circles of the upper and lower bottom surfaces of the regular hexagonal bench, and the length (the height ofthe ith corn stalk) of the ith stalk can be represented by the height of the regular hexagonal bench. When the top diameter (stem top diameter) and the bottom diameter (stem bottom diameter) of the stalk are close, the regular hexagonal prism (regularhexagonal frustum pyramid) may be further replaced by regular hexagonal prism.

Specifically, as shown in fig. 4, the corn stalks are geometrically processed, so that the corn stalks are simplified into a plurality of superimposed regular hexagonal prisms, and the regular hexagonal prisms are cut into 12 triangular grids (S1-S12) so as to facilitate the visualization of a computer.

S302: and calculating the maximum blade width according to the preset maximum length of the blade.

The specific implementation process for calculating the maximum blade width according to the preset maximum length of the blade is as follows: the calculation is performed using a blade width calculation formula, the concrete expression form of which is shown in formula (1).

In the case of the formula (1),for the maximum width of the ith leaf, +.>The maximum length of the blade is preset for the ith blade, the length and width ratio of the blade is the ratio coefficient of the length and width of the blade, and the i is the blade sequence.

It should be noted that, in the embodiment of the present application, the value of the length-width proportionality coefficient of the blade may be 0.119.

S303: and determining the blade size according to the preset maximum length and the maximum width of the blade.

The blade size is determined according to the preset maximum length and the maximum blade width, that is, the blade size is determined by the preset maximum length and the maximum blade width, and specifically, the maximum blade widths calculated by different preset maximum blade lengths are different, so that the blade sizes are different.

S304: and carrying out normalization treatment on the blade segment length according to the preset maximum length of the blade to obtain the normalized blade segment length.

Wherein, after spreading the corn leaf, there is an obvious central symmetry axis, namely the vein. The distance from any point on the vein to the stem and leaf node is the length of the leaf segment.

It should be noted that, according to the preset maximum length of the blade, the specific implementation process of normalizing the length of the blade segment is as follows: and (5) carrying out normalization processing on the leaf segment length by using a leaf segment length calculation formula, wherein the concrete expression form of the She Duanchang calculation formula is shown as a formula 2.

l*＝l/l _max (2)

In formula (2), l is normalized She Duanchang, l is She Duanchang, l _max The maximum length of the blade is preset.

S305: and calculating according to the normalized blade segment length and the preset maximum blade length to obtain the blade Duan Kuan.

Wherein She Duankuan is the distance between the two points of intersection of the straight line perpendicular to the vein and the edge of the leaf, which is made at any point on the vein.

It should be noted that, according to the specific implementation process of normalizing the blade segment length and presetting the maximum length of the blade, the method comprises the following steps: the calculation was performed using a She Duankuan calculation formula, and the leaf Duan Kuan calculation formula was embodied as shown in formula (3).

w＝(α*l ^* +β*l ^* +γ)*l _max (3)

In the formula (3), w is leaf Duan Kuan, alpha, beta and gamma are empirical coefficients, and the values of alpha, beta and gamma are related to the leaf length-width proportionality coefficient and the leaf phyllometer at the highest position.

Alternatively, in the embodiment of the present application, when the blade sequence of the highest blade is 20 and the value of the length-width proportionality coefficient of the blade is 0.119, the value of α may be wie0.2788, the value of β may be 0.2288, and the value of γ is 0.05.

It is emphasized that the maize leaf shape (leaf shape) is the geometry after spreading the leaf on a flat surface. Blade shape is related to blade length (blade length) and blade width (blade width), and from blade tongue to blade tip, the blade width changes with blade length to determine the shape of the blade, so that the corn blade shape can be determined according to the blade vein, the blade segment length, the blade Duan Kuan and the normalized blade segment length.

The corn leaf shape shows obvious difference along with She Weigao DEG, the leaf can be divided into a bottom layer, a middle layer and an upper layer according to the leaf shape characteristics, the bottom layer leaf is short and thin, the middle layer leaf is long and wide, and the upper layer leaf base is wide and rapidly narrowed.

Specifically, as shown in FIG. 5, the blades are divided by P1 to P11In ten equal divisions, when the normalized blade segment length is 1, it is the blade width at the tip position, where w=0, α= - (β+γ), and when the normalized blade segment length is 0, it is the blade width at the blade tongue position, where w= (γ×l) _max ) Meanwhile, in order to visualize the corn leaf, the corn leaf is cut into 19 triangular meshes, i.e., S1 to S19.

S306: and normalizing the vein curve to obtain a target vein curve.

The vein curve is a curve formed by naturally bending veins, as shown in fig. 6.

It should be noted that, the specific implementation process of the normalization processing for the vein curve is as follows: and carrying out normalization processing on the vein curve by using a normalization calculation formula, wherein the specific expression form of the normalization calculation formula is shown as formula (4), formula (5) and formula (6).

Wherein-arca in formula (6) _i Obtained by the formula (7), valuespace in the formula (6) _i Obtained by the formula (8).

-arca _i ＝-0.0646*i ² +0.7276*i-3.5258 (7)

Valuespeace _i ＝1.085*e ^(-0.0906*i) (8)

In the formula (4), the formula (5), the formula (6), the formula (7) and the formula (8), x _i ^* For the i-th vein curve span (i.e., the vein curve projects a lower line segment on the X-axis), z _i ^* Is the ith vein curve height (i.e. the vein curve projects a lower line segment on the Z axis), and Z ^* The maximum point is the peak of the vein curve, x _mid Is half-width of the vein curve (i.e. the distance from the projection point of the apex of the vein curve on the X-axis to the stem and leaf node (0, 0)) -arca _i To calculate the intermediate parameters of the curvature of the i veins of the leaf segment, valuespace _i For the normalized intermediate variable of the blade width of the blade section i, e is the base of the natural logarithm.

Specifically, as shown in FIG. 7, assuming that the leaf vein curves are in the same vertical plane in space, the leaf veins are projected on a plane, and the leaf vein curves of corn are simulated by using the secondary leaf vein curves, it is clear from the figure that the leaf of the 1 st leaf has the greatest degree of bending, namely x _max ^* Close to 1, the vein curve is close to a symmetrical quadratic curve, and the bending degree of the 5 th leaf to the 10 th leaf is smaller by x _max ^* E (0.4,0.7), the vein curve is a part of a quadratic curve, and in the embodiment of the application, the included angle between the connecting line of the tongue and the tip and the vertical line of the stalk is defined as the opening angle A of the vein curve _lc As an index of accuracy of examining the vein curve, the angle of the vein curve is shown in fig. 8.

S307: and calculating the azimuth angle of the blade according to the preset azimuth angle of the blade.

Wherein the blade azimuth angle indicates an orientation of each blade vein centerline in a spatial coordinate system.

It can be understood that corn is a typical leaf-crop, the leaves alternately grow on two sides of the stalk, and leaf azimuth angles of the two sides are different by about 180 degrees, so that leaf azimuth angles of the whole plant can be obtained by only determining azimuth angles of any leaf, and therefore, the leaf azimuth angles can be calculated according to preset leaf azimuth angles.

It should be noted that, according to the preset blade azimuth angle, the specific implementation process of calculating the blade azimuth angle is as follows: the azimuth angle calculation formula is used for calculation, and the concrete expression form of the azimuth angle calculation formula is shown as a formula (9).

Azi _j ＝Azi _i +180*(j-i)(9)

Azi in the formula (9) _i Is a preset blade azimuth angle (i.e. blade azimuth angle with blade order i), and Azi _j =θ is knownBlade azimuth measurement, azi _j Is the blade azimuth angle (i.e., blade azimuth angle with order j).

S308: organ levels were constructed based on the geometrically derived maize stalks, leaf size, leaf Duan Kuan, target vein curve, and leaf azimuth.

It should be noted that, based on the geometrically-formed cornstalk, leaf size, leaf Duan Kuan, target vein curve, and leaf azimuth, organ levels are constructed so that the stalk and leaf can be learned later through the organ levels.

S102: individual levels are constructed based on individual level parameters.

Among the individual corn parameters include, but are not limited to: the maximum number of leaves, the maximum stalk height, the sum of leaf areas of the leaves, the height of the insertion point of each leaf, the maximum leaf width, the maximum leaf length, the leaf area of the maximum leaf, and the height and diameter of each stalk.

It will be appreciated that individuals of maize are composed of a variety of plant organs, and thus individual levels are constructed based on individual level parameters, i.e., the process of combining plant organs according to certain laws.

Specifically, as shown in fig. 9, a schematic diagram of a corn individual is shown.

Alternatively, in another embodiment of the present application, a specific implementation of step S102, as shown in fig. 10, includes the following steps:

s1001: and calculating the maximum blade length according to the maximum value of the preset total blade number, the minimum value of the preset total blade number and the minimum length of the preset maximum blade length.

The specific implementation process for calculating the maximum blade length according to the maximum value of the preset total blade number, the minimum value of the total blade number and the minimum length of the preset maximum blade length comprises the following steps: the calculation is performed using a length calculation formula, the concrete expression form of which is shown in formula (10).

In the formula (10), L _max For maximum blade length, N _t For the maximum value of the preset total blade number L _{max_min} For the minimum length of the preset maximum blade length, k is an empirical parameter, N _g Is the minimum value of the preset total blade number, wherein N _g Maize plant growth occurs when certain temperature and photoperiod conditions result in minimal leaf primordial development.

S1002: based on the maximum blade length, a maximum blade width is determined.

It should be noted that, based on the maximum blade length, the specific implementation process of determining the maximum blade width is: when N is _t ＝N _g When L _max ＝L _{max_min} Based on the maximum blade length L _max The maximum blade width W is calculated by the formula (1) _max 。

S1003: based on the maximum blade length and the maximum blade width, the blade area of the maximum blade is calculated.

The specific implementation process for calculating the leaf area of the maximum leaf based on the maximum leaf length and the maximum leaf width comprises the following steps: the calculation is performed using a leaf area calculation formula, the concrete expression form of which is shown in formula (11).

Ae _max ＝L _max *0.75*W _max *e ^{(-1.17+0.047*Nt)} (11)

In equation (12), ae _max The leaf area of the largest leaf, e, is the base of the natural logarithm.

Specifically, nt is set to Ng at the time of initialization, but may be changed according to the time required for the plant to reach the propagation stage, taking into consideration the influence of photoperiod and temperature on the number of final leaves; when nt=ng, L _max ＝L _{max_min} ＝115cm ² ，Ae _max Up to a maximum of 1057cm ² When Nt and Ng differ, the potential area of the largest blade (i.e., the blade area of the largest blade when fully deployed) can also be calculated by equation (11); ae when nt=18or 19andng=16or 17 _max Respectively up to 750cm ² And 800cm ² 。

S1004: and calculating the leaf position of the maximum leaf according to the maximum value of the preset total leaf number.

The specific implementation process for calculating the leaf position of the maximum leaf according to the maximum value of the preset total leaf number comprises the following steps: the calculation is performed by using a leaf position calculation formula, and the concrete expression form of the leaf position calculation formula is shown as a formula (12).

N _m ＝5.93+0.33*N _t (12)

In formula (12), N _m Is the leaf position of the largest leaf.

In order to accurately describe the attribute of the maximum leaf of the whole plant, the leaf position of the maximum leaf needs to be calculated.

S1005: and calculating potential leaf areas of the blades according to the leaf areas of the maximum blades and the leaf positions of the maximum blades.

The specific implementation process for calculating the potential leaf area of each blade according to the leaf area of the maximum blade comprises the following steps: the calculation is performed using a latent leaf area formula, the concrete expression of which is shown in formula (13).

In the formula (13), a is calculated by the formula (14), and b is calculated by the formula (15).

a＝-10.61+0.25*N _t (14)

b＝-5.99+0.27*N _t (15)

In equation (13), ae _i For potential area of ith blade, nl _i Is the leaf sequence of the ith leaf.

By the formula (16), ae is based on _i The maximum length and maximum width of the blade when the ith blade is fully extended (the maximum length and maximum width of the blade also satisfy the proportional relationship in equation (1)).

In formula (16), l _imax Is the maximum length of the blade when the ith blade is fully extended.

Specifically, as shown in fig. 11, when nt=80, ng=18 and L _max Maximum length, maximum width, and potential leaf area per leaf as a function of leaf sequence at =115 cm.

S1006: and calculating the sum of the leaf areas of the blades according to the potential leaf areas of the blades.

The specific implementation process for calculating the sum of the leaf areas of the blades according to the potential leaf areas of the blades comprises the following steps: calculation is performed by using an area sum formula, and the concrete expression form of the area sum formula is shown as a formula (17)

In equation (17), ae _sum Ae is the sum of the blade areas of the blades _{sum_max} Is the maximum value of the sum of the blade areas of the blades.

The total leaf area of the leaves varies with the cumulative growth day of the crop.

S1007: and calculating to obtain the height of the insertion point according to the sum of the blade areas, the preset maximum number of blades and the preset blade height.

The height of the insertion point is the distance between the connecting node of the blade and the stem and the horizontal ground, and is a parameter obviously related to the leaf sequence.

It should be noted that, according to the sum of the leaf areas of the leaves, the preset maximum number of the leaves and the preset leaf height, the specific implementation process of calculating the height of the insertion point is as follows: the calculation is performed using an insertion point calculation formula, the concrete expression of which is shown in formula (18).

In the formula (18), a _h Calculated by equation (19).

a _h ＝3.7-1.10*N _t -0.36*Ae _{sum_max} (19)

In formula (18), h _li For the insertion point height of the ith blade, h ₁ For the first blade insertion point height, h in the present embodiment ₁ Can not change greatly and can set h ₁ For an average value of 1.5cm, h _i At the top height, a _h As an intermediate parameter, h _Nt Is the N _t Height of top of each stalk, and h _Nt As the cumulative growth of the crop changes.

Specifically, as shown in FIG. 12, N is _t ＝20,Ae _{sum_max} ＝7766.5CM ² A plot of the insertion point height of each blade as a function of the phyllotaxis.

S1008: individual levels are constructed based on the leaf area of the largest leaf, the sum of leaf area of the leaf, and the insertion point height.

The organ level is constructed based on the leaf area of the maximum leaf, the sum of leaf areas of the leaf and the height of the insertion point, so that the recombination of the organs such as the stem, the leaf and the like in a single time phase (a certain time in the growth and development process) of the corn can be realized, and a static corn model is constructed.

S103: the season Xiang Shuiping is constructed based on the season phase level parameters.

Among the quaternary phase level parameters include, but are not limited to: the maximum height of the corn stalk, the maximum diameter of the corn stalk, the initial number of leaves, the number of leaves appearing, the cumulative growth day of crops, the leaf expansion rate of the leaves, the leaf senescence area and the green leaf area.

It can be understood that the growth form of corn changes with the change of the cumulative growth degree day of crops, and the growth process of corn generally comprises three stages, namely an occurrence stage, a development stage and a maturation stage, wherein the growth speed is slow in the occurrence stage; in the development stage, the growth speed is accelerated; in the mature stage, the growth speed tends to be gentle; the growth morphology of the corn in three stages can be known through the quaternary phase level, and specifically, as shown in fig. 13, a schematic diagram of the cumulative daily change of the corn individual along with the crop is shown.

It should be noted that the three-dimensional structure of the crop is continuously changed along with the growth period of the crop, a growth equation (describing the growth characteristics of the physical and chemical parameters of the crop changing along with time) of the crop is added into the original static corn model, that is, the quaternary phase level is added on the basis of the individual level, so that the three-dimensional structure information of the corn in the whole growth period can be accurately described.

Alternatively, in another embodiment of the present application, a specific implementation of step S103, as shown in fig. 14, includes the following steps:

s1401: and calculating the maximum height of the cornstalk according to the preset maximum height.

The specific implementation process for calculating the maximum height of the corn stalks according to the preset maximum height comprises the following steps: the calculation is performed using a maximum height formula, the concrete expression of which is shown in formula (20).

In formula (20), H _max To preset maximum height, h _max In the embodiment of the application, the value of alpha can be 0.008, the value of beta can be 342.1 and H _max Can be 250.3cm, GDD _8℃ The range of values can be (0,2500) day DEG C.

Specifically, assume H _max When the value of (2) is 250.3cm, the maximum height of the corn stalk changes along with the daily change of the accumulated growth degree of the crop, as shown in figure 15.

It should be noted that, equation (20) is a Peel growth equation of the height of the stalk, and the Peel curve (Pearl Curvl) is a curve similar to an S-shape.

S1402: and calculating the maximum diameter of the corn stalks according to the preset maximum diameter.

The specific implementation process for calculating the maximum diameter of the corn stalk according to the preset maximum diameter comprises the following steps: the calculation is performed using a maximum diameter formula, the concrete expression of which is shown in formula (21).

In the formula (21), phi _stem Is the maximum diameter phi of the cornstalk _max In order to preset the parameters of maximum diameter, delta and gamma are more than 0, in the embodiment of the application, the value of delta can be 0.007048, and the value of gamma can be 8.495, phi _max Can take a value of 2.454cm.

Specifically, suppose φ _max When the value of (2) is 2.454cm, the maximum diameter of the corn stalk changes along with the daily change of the cumulative growth degree of the crop, as shown in FIG. 16.

It should be noted that, equation (21) is a Peel growth equation of the diameter of the stalk, and the Peel curve (Pearl Curvl) is a curve similar to an S-shape.

S1403: and determining the tip initiation rate according to a preset development rate function.

Wherein, the specific expression form of the preset development rate function is shown in the formula (22).

In formula (22), T is an input variable representing the average ambient air temperature per hour, T _opt Optimal temperature for leaf initiation or growth to reach maximum development rate, T _ceil For the maximum bearing temperature of the blade to develop and leave, R _max For maximum development rate reached by leaf initiation or growth, when t=t _opt When R (T) =r _max The growth and development speed reaches the peak value; when t=t _ceil When R (T) =0, growth and development stopped.

It will be appreciated that the growth of the leaf comprises four processes: the four processes of initiation, appearance, expansion and aging are different in development rate, and meanwhile, the optimal temperatures of different biological enzymes in different development processes are different, so that each development process has different optimal temperatures and highest bearing temperatures, and the influence of the growth temperature on the expansion rate of the blade can be measured through a preset development rate function.

It should be noted that, according to the preset development rate function, the specific implementation process of determining the tip start rate is as follows: the calculation is performed using an initial rate formula, the concrete form of which is shown in formula (23).

In formula (23), R _LTIR For tip initiation rate, R _{max_LTIR} For maximum tip start rate, T _{opt_LTIR} Optimum temperature for tip onset, T in the present embodiment _{opt_LTIR} The value of (2) can be 30.5 ℃, and the highest bearing temperature of the blade tip is 42.7 ℃.

Alternatively, the GDD may be calculated by equation (25).

In the formula (24), GDD is the cumulative growth day, T of the crop _i To average the ambient air temperature per hour, when T _i Instead of averaging the ambient air temperature per hour, equation (24) and equation (26) are modified accordingly, where T _i Can be calculated by equation (25).

T _i ＝(T _imax -T _imin )/2(25)

In formula (25), T _imax At maximum ambient air temperature per hour, T _imin Is an ambient air temperature minimum per hour.

S1404: and determining the starting quantity of the blades according to the starting speed of the blade tips.

The specific implementation process for determining the initial number of the blades according to the initial rate of the blade tips comprises the following steps: the calculation is performed using a starting quantity formula, the concrete form of which is shown in formula (26).

In equation (26), LTIR is the blade start number.

S1405: and calculating to obtain the occurrence rate of the blade tip according to a preset development rate function.

The specific implementation process for calculating the occurrence rate of the blade tip according to the preset development rate function is as follows: the calculation is performed using a rate of occurrence formula, the concrete form of which is shown in formula (27).

In formula (27), R _LTAR For the tip appearance rate, R _{max_LTAR} For maximum rate of blade tip occurrence, T _{opt_LTAR} For optimum blade tip temperature, T in the present embodiment _{opt_LTAR} The value of (2) can be 32.1 ℃, the highest bearing temperature of the blade tip is 43.7 ℃, R _{max_LTAR} May take the value of 0.53 leave/day.

S1406: and determining the number of the blades according to the blade tip appearance rate.

The specific implementation process for determining the occurrence quantity of the blades according to the occurrence rate of the blade tips comprises the following steps: the calculation is performed using the number of occurrences formula, the concrete expression of which is shown in formula (28).

In equation (28), LTIR is the number of blade occurrences.

Specifically, as shown in fig. 17, the mathematical relationship between the tip start rate and the tip appearance rate, which varies with the change of the ambient temperature, and the mathematical relationship between the blade start number and the blade appearance number, which varies with the change of the cumulative growth day of the crop, are shown.

S1407: the rate of blade expansion is determined based on a preset maximum expansion rate.

It will be appreciated that the blade expansion rate ke _i Is commonly affected by the phyllotaxis i, the total leaf number and the ambient temperature.

The specific implementation process for determining the blade expansion rate based on the preset maximum expansion rate comprises the following steps: the expansion rate formula is calculated, wherein the expansion rate formula is embodied as shown in formula (29).

In equation (29), ke _i To the leaf expansion rate, ke _{i_max} For a preset maximum expansion rate (i.e. maximum rate of blade expansion under optimal temperature conditions), this can be represented by a Gaussian curve, ke _{i_max} Can be calculated by the formula (30), T in the embodiment of the application _{opt_ke} Can take the value of 18.8 ℃ and T _{cile_ke} The value of (2) may be 42.7 ℃.

In formula (30), k ₀ Is the lower asymptote of the Gaussian curve, W _k Variance of Gaussian curve, k _x Amplitude, k of Gaussian curve _x Can be calculated by the formula (31), W _k Can be calculated by the formula (32), k is in the embodiment of the application ₀ Can be 0.026, k _x The value of (2) may be 0.174.

W _k ＝N _t /8.18(32)

When the leaf order is larger than 1 (the mathematical expected value of the gaussian curve), the k is _{i_max} Reaching peak value k ₀ +k _x And then rapidly decrease and tend to relax.

Specifically, as shown in fig. 18, the blade expansion rate is a change curve under the influence of both the blade sequence and the ambient temperature.

S1408: the cumulative growth days are determined based on a pre-established nonlinear temperature function.

The specific implementation process for determining the accumulated growth degree day based on the pre-established nonlinear temperature function comprises the following steps: the calculation is performed using the cumulative growth day formula, and the concrete expression form of the cumulative growth day formula is shown in formula (33).

In formula (33), te _i Cumulative growth day for the ith leaf to reach 50% of final area, tt _i For the thermal time from feathering to tip appearance of the ith blade, f (T) is a pre-established nonlinear temperature function, specifically, as shown in FIG. 19, is the cumulative growth day te _i Blade expansion rate ke _{i_max} Variation curve with leaf sequence.

Wherein the nonlinear temperature function is embodied as shown in formula (34).

In formula (34), T _b For calculating the reference temperature, T, of the cumulative growth days in the blade growth function _pk T is the ambient temperature, which is the maximum temperature of the potential leaf area, in the embodiment of the application T _b The value of (C) can be 8 ℃, T _pk Can be taken as a value of (a)Is 18.7 ℃. When the ambient temperature t=t _b +T _pk When f (T) reaches a maximum value of 1; when the ambient temperature T<T _b at=8deg.C, f (T) is 0; when the ambient temperature T>T _b at=8deg.C, f (T) is a function value, specifically, a curve of a nonlinear function with ambient temperature as shown in FIG. 20.

The nonlinear temperature response of the growth temperature to the expansion rate of the blade is measured by using a peak exponential function (i.e., nonlinear temperature function), and f (T) takes the larger of 0 and the peak exponential function.

In equation (33), tt _i Can be calculated by the formula (35).

tt _i ＝(i-2)*PHY+tt ₂ (35)

In equation (35), tt ₂ For the cumulative growth day from feathering to growth of the 2 nd leaf, PHY is the leaf heat separation (i.e., the difference in cumulative growth days when two adjacent leaves grow), where PHY can be calculated by equation (36).

S1409: the blade expansion rate of the blade is calculated based on the blade expansion rate, the nonlinear function and the pre-calculated potential area of the blade.

The specific implementation process for calculating the blade expansion rate of the blade based on the blade expansion rate, the nonlinear function and the pre-calculated potential area of the blade comprises the following steps: the calculation is performed using a leaf expansion ratio formula, the concrete expression of which is shown in formula (37).

In formula (37), R _Ai For the expansion of the ith leaf, i.e., the first derivative of the leaf area spread, t is the temperature treatment (i.e., the cumulative growth day of corn from sowing), where the leaf area spreadCan be calculated by equation (38).

S1410: and calculating the expansion area of the blade according to the potential area of the blade and the expansion rate of the blade.

The specific implementation process for calculating the unfolding area of the blade according to the potential area of the blade and the expansion rate of the blade is as follows: the calculation is performed using an extended area formula, the concrete form of which is shown in formula (38).

In equation (38), LA _i For the i-th blade deployment area, the potential blade area of the i-th blade is the maximum of the blade deployment area, LA when t is large enough (blade i has been fully extended) _i ＝Ae _i 。

Specifically, suppose N _t When =20, as shown in fig. 21, the variation curve of the leaf area with the leaf sequence and the cumulative growth day of the crop is shown.

S1411: based on the potential area of the blade and the nonlinear function, the aging rate of the blade is calculated.

The specific implementation process for calculating the aging rate of the blade based on the potential area of the blade and the nonlinear function comprises the following steps: the aging rate formula is used for calculation, and the concrete expression form of the aging rate formula is shown as a formula (39).

In equation (39), SA _i For the area of aging of the leaf, rs _i For the ith leaf senescence rate (i.e., the first derivative of leaf senescence area), ks _i Control of aging Rate for ith tablet, ts _i Cumulative growth days for crops when the ith leaf reached 50% of its maximum area, wherein ks _i The calculation formula of (2) is the same as formula (29).

In formula (39), ts _i Can be calculated by equation (40).

ts _i ＝LL _i +te _i (40)

In equation (40), LL _i For the life of the ith blade, LL _i Can be calculated by equation (41).

In formula (41), L ₀ Is the lower asymptote of the Gaussian curve, L _x Amplitude of Gaussian curve, LN _L Node position of blade with longest service life, W ₁ For mathematical expectations of gaussian curves, in an embodiment of the present application, L _x The value of (2) may be 850 or 500, wherein LN _L Can be calculated by the formula (42), W ₁ Can be calculated by equation (43).

LN _L ＝3.59+0.498*N _t (42)

W ₁ ＝1/3*N _t (43)

The SA is that _i Can be calculated by equation (44).

S1412: based on the potential area of the leaf and the preset aging rate, the aging area of the leaf is calculated.

The specific implementation process for calculating the aging area of the blade based on the potential area of the blade and the preset aging rate comprises the following steps: the aging area formula is used for calculation, and the concrete expression form of the aging area formula is shown as a formula (44).

Specifically, suppose N _t At =20, as shown in fig. 22, the leaf senescence area is a curve of change in leaf sequence and cumulative growth day of crops.

S1413: based on the leaf spreading area and the leaf senescence area, the green leaf area is calculated.

The specific implementation process for calculating the green leaf area based on the leaf unfolding area and the leaf aging area comprises the following steps: the green leaf area formula is used for calculation, the specific expression form of the green leaf area formula is shown as a formula (45),

GA _i ＝LA _i +SA _i (45)

in the formula (45), GA _i Green leaf area for the ith leaf.

The photosynthetic effective green leaf area can be known from the green leaf area.

Specifically, suppose N _t When =20, as shown in fig. 23, the change curve of the green leaf area with the leaf sequence and the cumulative growth day of the crop is a symmetrical bell-shaped curve with high middle and low sides.

S1414: the individual level is constructed based on the maximum height of the corn stalk, the maximum diameter of the corn stalk, the starting number of leaves, the number of leaves present, the cumulative growth day of the crop, the leaf expansion rate of the leaves, the leaf senescence area, and the green leaf area.

It should be noted that, three-dimensional structural information of corn in the whole growth cycle can be accurately described through individual level.

S104: population levels are constructed based on population level parameters.

Among them, population level parameters include, but are not limited to: three-dimensional coordinates of corn leaves or stems, group leaf area, average plant height, leaf inclination angle, row spacing (plant spacing), plant stem zenith angle and azimuth angle.

It should be noted that, by counting the variation of the growth morphology of each corn in the population at the population level, specifically, as shown in fig. 24, a schematic diagram of the cumulative daily variation of each corn in the corn population along with the crop is shown.

Alternatively, in another embodiment of the present application, a specific implementation of step S104, as shown in fig. 25, includes the following steps:

S2501: based on a preset average plant height, a preset average column spacing, a preset average row spacing, a preset average stalk zenith angle and a preset azimuth angle, establishing three-dimensional coordinates of corn leaves or stalks.

Wherein, the three-dimensional coordinates of corn leaf or stem are: the coordinates of the corn leaf or stalk in the X axis, the coordinates of the corn leaf or stalk in the Y axis, and the coordinates of the corn leaf or stalk in the Z axis.

It should be noted that, assuming that the corn group is on the same plane, the three-dimensional coordinates of the corn leaf or stalk are expressed in the form of formula (46), formula (47) and formula (48).

x _up ＝z _low +H×cos(Zen _stem ) (48)

In the formulas (46), (47) and (48), H is the preset average plant height, d _col For a preset average Column distance d _row To preset the average Row distance (Row distance), zen _stem For presetting average stalk zenith angle (zenith angle), azi _stem For a preset Azimuth Angle (Azimuth Angle), the ground three-dimensional coordinate of the center point of the bottom of the corn stalk in the ith row and the jth row isThe ground three-dimensional coordinate of the top center point of the corn stalk in the kth column and the first row is

S2502: and calculating the area of the group leaves based on the pre-calculated area of the leaves.

Wherein, the leaf area of the population is one of the most representative corn population statistical parameters, which is defined as the multiple of the total plant leaf area occupied by the land area per unit land area.

It should be noted that, assuming that the corn population is formed by alpha column and beta row and the growth vigor is uniform, the specific implementation process of calculating the population leaf area based on the pre-calculated leaf expansion area is as follows: the calculation is performed using a group leaf area formula, the concrete expression of which is shown in formula (49).

In formula (49), LAI is the area of the leaves of the population, N _average Is the average maximum number of blades.

S2503: based on the three-dimensional coordinates of the maize leaves or stems, population levels are constructed.

It should be noted that, based on the three-dimensional coordinates of the corn leaves or stems and the area of the leaves of the group, the group level is constructed so as to count the structural parameters of the corn in the corn group through the group level later.

S105: crop growth models are constructed based on organ level, individual level, quaternary phase level, and population level.

It will be appreciated that by constructing a crop growth model, i.e., also a corn growth model, based on organ levels, individual levels, quaternary phase levels, and population levels, and inputting conventional statistical parameters (e.g., cumulative crop growth days, maximum plant height, maximum leaf number, etc.) into the corn growth model, parameters such as plant height, leaf number, leaf size, and curvature of the corn at that time can be calculated.

Optionally, after the crop growth model is constructed, the structural parameters of the corn may be obtained through the crop growth model. In another embodiment of the present application, there is provided a method for obtaining a growth result, comprising the steps of:

Wherein the preset parameters include, but are not limited to: cumulative growth day, maximum plant height, maximum leaf number of crops.

It should be noted that, there may be a difference between the growth result output by the crop growth model and the actual measurement value, so that the deviation between the simulated value and the actual measurement value of the corn growth method driven by the crop cumulative growth degree day; secondly, simplifying structural parameters in the corn scene computer visualization process.

Specifically, the growth results of six growth periods of the same variety are verified as practical measurement values, and the cumulative growth days of crops in the six growth periods are respectively: 580. 680, 739, 904, 995 and 1192.

Optionally, in order to verify whether the leaf length output by the crop growth model is accurate, in another embodiment of the present application, a leaf length verification method is provided, as shown in fig. 26, including the steps of:

S2601: and calculating the root mean square error of the blade length and the preset blade length and the standard root mean square error.

The range of the blade length can be: (66.429,80.178) cm.

The average value range of the preset blade length can be: (66.850,79.364) cm.

It will be appreciated that the range of blade length values is greater than the average range of blade lengths, and 9.869% is extended, as can be seen, the blade length is close to the preset blade length.

It should be noted that the root mean square error and the standard root mean square error of the calculated blade length and the preset blade length are common knowledge of those skilled in the art, and are not described herein.

S2602: when the root mean square error is within the leaf error range and the standard root mean square error is within the standard leaf range, the leaf length calculated by the crop growth model is determined to be accurate.

Wherein, the value of blade error range can be: [1.264,4.895] cm, the values for the standard blade range may be: [0.016,0.063] cm.

It will be appreciated that when the root mean square error is within 1.264,4.895 cm and the standard root mean square error is within 0.016,0.063 cm, both are less than 0.1, the leaf length calculated for the determined crop growth model is accurate.

Specifically, assuming that the leaf length is the predicted value, as shown in FIG. 27, the regression equation slope of the leaf length of six growth periods and the preset leaf length is [0.860,1.127 ]]Fluctuation in range, very close to 1: the ideal slope 1 of the 1 line, the data points in the scatter diagram are more evenly scattered on two sides of the 1:1 line. The leaf length predictions for the 6 growth phases can be divided into two categories depending on whether the slope of the fit equation is greater than 1. The first type of fit equation slope is greater than 1, including two sets of results, gdd=580 (a in fig. 27) and 995day· (e in fig. 27). To fit straight lines and 1: the length of the blade at the 1-line Crossing point cross point is taken as a boundary, and the prediction result exists that the length of the blade is less than l _cp And for blade lengths greater than l _cp Is an overestimation of the blades of (a). When gdd=580 (a in fig. 27), the predicted value of the model is lower for leaves with a leaf length of less than 70cm in the plant, and higher for leaves with a leaf length of more than 70cm in the plant. The second type of fit equation slope is less than 1, including four sets of results gdd= 680,739,904 and 1192day· (b in fig. 27, c in fig. 19, d in fig. 19, f) in fig. 19). The second type of prediction results are opposite to the first type of prediction results, and the existence of the prediction results with the length of the blade being less than l _cp And for blade lengths greater than l _cp Is an underestimation of the blades of (a). For example, when gdd=680 day· (b in fig. 27), the model predictive value is higher for leaves with leaf lengths below 30cm in the plant and lower for leaves with leaf lengths above 30cm in the plant. Only when fitting straight lines and 1: the blade length at the 1-line intersection point cross point is close to the average value of the samples, and the slope of the fitting straight line is close to 1, so that the better prediction precision is shown. When gdd=680 day· ℃ the preset blade length average is 66.9cm close to this point i _cp =70, the slope of the fitted line is 1.127 and is close to 1, and the prediction accuracy of the model is high.

In fig. 27, a scatter diagram shows measured values and predicted values of leaf length in each leaf sequence, and a diagonal line is a 1:1 line (x shows a statistical meaning at 0.05; x shows a statistical meaning at 0.01).

Optionally, in order to verify whether the length of the leaf outputted by the crop growth model is accurate, in another embodiment of the present application, a leaf width verification method is provided, as shown in fig. 28, including the steps of:

s2801: and calculating the root mean square error and the standard root mean square error of the blade width and the preset blade width.

The range of the blade width can be: (7.905,9.228) cm.

The range of values of the preset blade width can be: (8.503,8.949) cm.

It will be appreciated that the blade width is enlarged 196.637% compared to the result of the blade length prediction, and it can be seen that the model exhibits greater randomness in the accuracy of the prediction of the blade width.

S2802: when the root mean square error is within the leaf width error range and the standard root mean square error is within the standard leaf width range, the leaf width calculated by the crop growth model is determined to be accurate.

Wherein, the leaf width error range can be [1.264,4.895] cm, and the standard leaf width range can be [0.016,0.063] cm, all less than 0.1.

It will be appreciated that the blade width calculated by the crop growth model is determined to be accurate when the root mean square error is within [1.264,4.895] cm and the standard root mean square error is within [0.016,0.063] cm.

Specifically, assuming that the leaf width is the predicted value and the preset leaf width is the measured value, as shown in fig. 29, the slope of the regression equation of the measured value and the predicted value of the leaf width of 6 growing period corn fluctuates within the range of [0.470,1.558], the data points in fig. 29 are more scattered compared to the scatter plot in fig. 27, i.e., the precision of the leaf width prediction of 6 growing periods by the crop growth model is lower than the prediction precision of the leaf length because there is a significant variability in the leaf aspect ratio between different plants and different leaf positions, whereas the leaf aspect ratio in the crop growth model is the average of the sample statistics. The corn leaf shape varies with She Weigao degrees, the bottom leaf is short and thin, the middle leaf is long and wide, the upper leaf base is wide and narrows rapidly, so that the leaf aspect ratio gradually increases with increasing leaf order (from bottom to top), however, the leaf aspect ratio input in the crop growth model is the average value of all sample leaf aspect ratios of 0.119, and if the measured leaf aspect ratio of each leaf is equal to the fixed value input by the model of 0.119, then the accuracy of leaf width prediction by the crop growth model (fig. 29) will be equal to the accuracy of leaf length prediction by the crop growth model (fig. 27).

The scatter plot shows the blade width and the preset blade width for each leaf sequence, and the diagonal line is a 1:1 line (indicating a statistical meaning at 0.05; indicating a statistical meaning at 0.01).

Optionally, in order to verify whether the leaf area of the leaf outputted by the crop growth model is accurate, in another embodiment of the present application, there is provided a leaf area verification method, as shown in fig. 30, including the steps of:

s3001: and calculating the root mean square error of the leaf area of the leaf and the preset leaf area and the standard root mean square error.

It will be appreciated that the blade area of the blade is dependent on the expansion rate of the blade, rather than the blade length and the blade width, and that the blade length and width are calculated from the blade area and the blade length-width Ratio (leaf aspect Ratio).

S3002: when the root mean square error is in the leaf area error range and the standard root mean square error is in the standard leaf area range, the leaf area of the leaf calculated by the crop growth model is determined to be accurate.

Wherein the leaf area error range can be [17.950,42.359 ]]cm ² The standard leaf area range can be [0.045,0.073 ]]cm ² Less than 0.1.

It should be noted that when the root mean square error is [17.950,42.359 ] ]cm ² In the interior, and the standard root mean square error is [0.045,0.073]]cm ² And when the leaf area of the leaf calculated by the crop growth model is determined, the leaf area is accurate.

Specifically, assuming that the leaf area of the leaf is a predicted value, and the preset leaf area is an actual measured value, as shown in fig. 31, the slopes of regression equations of the measured values and the predicted values of the lengths of 6 maize leaves in the growing period fluctuate within the range of [0.863,1.161], which is very close to 1: the ideal slope 1 of the 1 line, the data points in the scatter diagram are more evenly scattered on two sides of the 1:1 line. Wherein, the standard root mean square error of the gdd=580, 739, 904, 995 and 1192day· (a, c, d, e, f in fig. 31) for the 5 growth periods is between [0.045,0.073], less than 0.1, indicating that the prediction accuracy of the model is good; the standard root mean square error of the growth period of gdd=680 day· (b in fig. 31) was between 0.111, slightly greater than 0.1, indicating a better prediction accuracy of the model.

Meanwhile, the change in the simulated maize full leaf area and leaf area spread with the cumulative growth days of the crop is plotted in g in fig. 31 (simulated whole maize leaf area and rate ofleaf area expansion variation with GDDs). We can find that: when the corn is in the middle stage of growth and development, GDD is more than or equal to 350 and less than or equal to 900day DEG C, the leaf expansion rate RAi is more than or equal to 5cm ² day ^-1 ·℃ ^-1 The growth of the blade is rapid; 350 when the corn is in the early or late stage of growth and development>GDDday °C or GDD>At 900day DEG C, the leaf expansion rate RAi<5cm ² day ^-1 ·℃ ^-1 The leaves grow slower. Gdd=580, 680 and 739day ℃ (a, b, c in fig. 31) belong to the middle stage of rapid maize growth and gdd=904, 995 and 1192day ℃ (d, e, f in fig. 31) belong to the later stage of slow maize growth, so that it can be well explained that the accuracy of the leaf area prediction by the crop growth model for the last 3 growth periods is higher than that for the first 3 growth periods. Notably, GDDs, while an important parameter in driving maize growth in crop growth models, plants' maximum leaf number is determined by affecting the cumulative growth day t at which leaf i reaches 50% of its final area _ei Thermal time t from feathering to tip appearance of blade i _ti And the leaf heat interval PHY, and the minimum value of the maximum leaf length and the leaf length-width proportionality coefficient of a corn variety can have great influence on the leaf area prediction accuracy by changing the leaf shape.

Optionally, in order to verify whether the height of the insertion point output by the crop growth model is accurate, in another embodiment of the present application, there is provided an insertion point height verification method, as shown in fig. 32, including the steps of:

S3201: and calculating the root mean square error of the height of the insertion point and the height of the preset insertion point and the standard root mean square error.

The root mean square error of the calculated insertion point height and the preset insertion point height is common knowledge of those skilled in the art, and will not be described herein.

S3202: when the root mean square error is within the height error range and the standard root mean square error is within the standard height range, determining that the calculated insertion point of the crop growth model is highly accurate.

The value of the height error range may be: [2.287,4.084] cm, the standard height range may be: [0.021,0.055] cm, all less than 0.1.

Alternatively, the crop growth model has the phenomenon that the prediction accuracy of the leaf insertion point height of the last 3 growth periods is higher than that of the leaf area of the first 3 growth periods.

It should be noted that the high-precision prediction of the leaf insertion point height has important significance in researching the biophysical parameters and biochemical parameters of different leaf layers of corn, the vertical distribution of photosynthetic effective radiation of plants and the like.

Specifically, assuming that the insertion point height is a predicted value and the preset insertion point height is a measured value, as shown in fig. 33, the regression equation slope of the measured value and the predicted value of the 6 growth period blade insertion point heights fluctuates within the range of [0.960,1.035], very close to 1: the ideal slope 1 of the 1 line, the data points in the scatter diagram are more evenly scattered on two sides of the 1:1 line. The model predicts very well the leaf insertion point height for 6 growth periods. The potential blade insertion point heights are plotted in fig. 12, respectively, and we can see that the blade insertion point height is a variable with increasing blade order and gradually increasing slope, which is consistent with the gradual dispersion of the scatter plot with increasing blade insertion point height in fig. 33. Meanwhile, the change rule of the simulated corn leaf height or stem height along with the cumulative growth degree day of the crops is respectively drawn in fig. 15, the change of the leaf insertion point height and the whole plant height in the early and later stages of the corn growth and development period is slow, the leaf insertion point height and the whole plant height in the middle of the growth and development period are rapid, and the phenomenon that the prediction precision of the leaf insertion point height of the crop growth model in the later 3 growth periods is higher than the prediction precision of the leaf area in the first 3 growth periods can be well explained.

The scatter plots (a to f in fig. 33) show the actual measurement value and the predicted value of the leaf area in each leaf sequence, and the oblique lines are 1:1 lines (x indicates a statistical meaning at 0.05; x indicates a statistical meaning at 0.01).

Optionally, in order to verify whether the vein curve outputted by the crop growth model is accurate, in another embodiment of the present application, a vein curve verification method is provided, as shown in fig. 34, including the steps of:

s3401: and calculating the root mean square error and the standard root mean square error of the vein curve and the preset vein curve.

The vein curve is a skeleton of corn leaves which are unfolded in a three-dimensional space, and is a part which describes the most basic form of the leaves. The vein curve is affected by gravity, the space trend of the vein curve shows obvious oblique parabolic curve characteristics, the vein curve is assumed to be a part of a quadratic curve, and the included angle between the connecting line of the tongue and the tip and the vertical line of the stalk is defined as the opening angle A of the vein curve _lc (i.e., blade azimuth angle).

It will be appreciated that the accuracy of the crop growth model prediction of the 6 growth stages of the angle of the leaf vein curve (angle ofleaf curvature, alc) is very good, and the accuracy of the leaf vein curve angle of the Alc prediction does not exhibit significant regularity as the growth stages vary, unlike the leaf area and the leaf insertion point height.

S3402: when the root mean square error is in the curve error range and the standard root mean square error is in the standard curve range, the vein curve calculated by the crop growth model is determined to be accurate.

The value of the curve error range may be: [1.258,22.460], the standard curve range can be: [0.030,0.046].

It is understood that when the root mean square error is within [1.258,22.460] and the standard root mean square error is within [0.030,0.046], both are less than 0.1, and the calculated vein curve of the crop growth model is determined to be accurate.

Specifically, assuming that the vein curve is a predicted value and the vein curve is a measured value, as shown in fig. 35, the slopes of the regression equations of the measured values and the predicted values of the opening angles of the vein curves at 6 periods fluctuate within the range of [0.9339,1.0300], which is very close to 1: the ideal slope 1 of the 1 line, the data points in the scatter diagram are more evenly scattered on two sides of the 1:1 line.

Meanwhile, drawing a simulated blade curve angle and a rate of change of the blade curve angle along with the blade sequence in g in fig. 35, wherein the key input parameters of the crop growth model are gdd=1200 day·DEG C, N _t =20. We can find that: the opening angle of the blade curve does not change linearly with the increase of the leaf sequence, wherein the opening angle of the blade curve of the 1 st to 5 th sheets rises gradually and rapidly; the blade curve opening angles of the 6 th to the 9 th sheets show gradually increasing trend and rapidly rise; after the 10 th leaf, the leaf curve opening angle shows a gradual and gradual rising trend. The above rule has a good indication of the phenomenon of low-order data point sparseness and high-order data point compaction in the a-f scatter diagram in fig. 35. It is worth noting that although the prediction accuracy of the crop growth model to the leaf curve opening angle does not show obvious regularity along with the growth period change, the leaf curve opening angle is commonly influenced by leaf sequences and the cumulative growth degree of crops.

In summary, constructing an organ level based on the organ level parameters; constructing an individual level based on the individual level parameter; constructing a season Xiang Shuiping based on the season phase level parameters; constructing population levels based on the population level parameters; in the embodiment of the application, the thermal driving equation adopted by the crop growth model is in an exponential form (namely formula 22, formula 23, formula 27 and formula 34) so as to be more sensitive to temperature change, thus improving the sensitivity of the growth model to temperature response and timely adjusting the growth form of corn according to the change of the environmental temperature.

As shown in fig. 36, an architecture diagram of a device for constructing a crop growth model according to an embodiment of the present application includes: organ level construction unit 3601, individual level construction unit 3602, quaternary phase level construction unit 3603, population level construction unit 3604, and model construction unit 3605.

An organ level constructing unit 3601 for constructing an organ level based on the organ level parameter; the organ level parameters include at least corn stalk and vein curves.

The organ level construction unit 3601 specifically functions to: geometrically processing the corn stalks to obtain geometrically processed corn stalks; calculating to obtain the maximum blade width according to the preset maximum length of the blade; determining the blade size according to the preset maximum length and the maximum width of the blade; normalizing the length of the blade segment according to the preset maximum length of the blade to obtain normalized She Duanchang; calculating to obtain a leaf Duan Kuan according to the normalized leaf segment length and the preset maximum length of the leaf; normalizing the vein curve to obtain a target vein curve; calculating to obtain a blade azimuth according to a preset blade azimuth; organ levels were constructed based on the geometrically derived maize stalks, leaf size, leaf Duan Kuan, target vein curve, and leaf azimuth.

An individual level construction unit 3602 for constructing an individual level based on the individual level parameter.

The individual level construction unit 3602 specifically functions to: calculating to obtain the maximum blade length according to the maximum value of the preset total blade number, the minimum value of the preset total blade number and the minimum length of the preset maximum blade length; determining a maximum blade width based on the maximum blade length; calculating the leaf area of the maximum leaf based on the maximum leaf length and the maximum leaf width; calculating the leaf position of the maximum leaf according to the maximum value of the preset total leaf number; calculating potential leaf areas of all the blades according to the leaf areas of the maximum blades and the leaf positions of the maximum blades; calculating to obtain the sum of the leaf areas of the blades according to the potential leaf areas of the blades; calculating to obtain the height of the insertion point according to the total area of the blades, the maximum number of preset blades and the preset blade height; individual levels are constructed based on the leaf area of the largest leaf, the sum of leaf area of the leaf, and the insertion point height.

A quaternary phase level construction unit 3603 for constructing a quaternary Xiang Shuiping based on the quaternary phase level parameter.

The quaternary phase level construction unit 3603 specifically: calculating the maximum height of the cornstalks according to the preset maximum height; calculating to obtain the maximum diameter of the corn stalks according to the preset maximum diameter; determining the initial rate of the blade tip according to a preset development rate function; determining the initial quantity of the blades according to the initial speed of the blade tips; calculating to obtain the occurrence rate of the blade tip according to a preset development rate function; determining the occurrence number of the blades according to the occurrence rate of the blade tips; determining a leaf expansion rate based on a preset maximum expansion rate; determining the cumulative growth degree day of the crop based on a pre-established nonlinear temperature function; calculating the expansion rate of the blade based on the expansion rate of the blade, a nonlinear function and a pre-calculated potential area of the blade; calculating to obtain the expansion area of the blade according to the potential area of the blade and the expansion rate of the blade; calculating to obtain the aging rate of the blade based on the potential area of the blade and a nonlinear function; calculating the aging area of the blade based on the potential area of the blade and a preset aging rate; calculating to obtain green leaf area based on leaf unfolding area and leaf aging area; the individual level is constructed based on the maximum height of the corn stalk, the maximum diameter of the corn stalk, the starting number of leaves, the number of leaves present, the cumulative growth day of the crop, the leaf expansion rate of the leaves, the leaf senescence area, and the green leaf area.

A population level construction unit 3604 for constructing a population level based on the population level parameters.

The population level construction unit 3604 specifically: establishing three-dimensional coordinates of corn leaves or stems based on a preset average plant height, a preset average column spacing, a preset average row spacing, a preset average stem zenith angle and a preset azimuth angle; calculating to obtain a group leaf area based on a pre-calculated leaf unfolding area; based on the three-dimensional coordinates of the maize leaves or stems, population levels are constructed.

A model construction unit 3605 for constructing a crop growth model based on organ level, individual level, quaternary phase level, and population level.

In summary, constructing an organ level based on the organ level parameters; constructing an individual level based on the individual level parameter; constructing a season Xiang Shuiping based on the season phase level parameters; constructing population levels based on the population level parameters; in the embodiment of the application, the thermal driving equation adopted by the crop growth model is in an exponential form, so that the crop growth model is more sensitive to temperature change, and therefore, the sensitivity of the crop growth model to temperature response is improved, and the crop growth model can timely adjust the growth form of corn according to the change of the environmental temperature.

Preferably, in combination with the content shown in fig. 36, the construction apparatus further includes: and an output unit.

The output unit is used for inputting preset parameters into the crop growth model to obtain a growth result output by the crop growth model; the growth results include at least leaf length, leaf width, leaf area of the leaf, insertion point height, and vein profile.

Preferably, in combination with the content shown in fig. 36, the construction apparatus further includes: a length error calculation unit and a length determination unit.

And the length error calculation unit is used for calculating the root mean square error and the standard root mean square error of the blade length and the preset blade length.

And the length error comparison unit is used for determining that the calculated blade length of the crop growth model is accurate when the root mean square error is in the blade error range and the standard root mean square error is in the standard blade range.

Preferably, in combination with the content shown in fig. 36, the construction apparatus further includes: a width error calculation unit and a width determination unit.

And the width error calculation unit is used for calculating the root mean square error and the standard root mean square error of the blade width and the preset blade width.

And the width error comparison unit is used for determining that the blade width calculated by the crop growth model is accurate when the root mean square error is in the range of the leaf width error and the standard root mean square error is in the range of the standard leaf width.

Preferably, in combination with the content shown in fig. 36, the construction apparatus further includes: leaf area error calculation unit, leaf area determination unit.

And the leaf area error calculation unit is used for calculating the root mean square error and the standard root mean square error of the leaf area of the leaf and the preset leaf area.

And the leaf area determining unit is used for determining that the leaf area of the leaf calculated by the crop growth model is accurate when the root mean square error is in the leaf area error range and the standard root mean square error is in the standard leaf area range.

Preferably, in combination with the content shown in fig. 36, the construction apparatus further includes: a height error calculation unit and a height determination unit.

A height error calculation unit for calculating the root mean square error and standard root mean square error of the height of the insertion point and the height of the preset insertion point;

and the height determining unit is used for determining that the calculated inserting point of the crop growth model is highly accurate when the root mean square error is in the height error range and the standard root mean square error is in the standard height range.

Preferably, in combination with the content shown in fig. 36, the construction apparatus further includes: a curve error calculation unit and a curve determination unit.

A curve error calculation unit for calculating the root mean square error of the vein curve and the preset vein curve and the standard root mean square error;

And the curve determining unit is used for determining that the vein curve calculated by the crop growth model is accurate when the root mean square error is in the curve error range and the standard root mean square error is in the standard curve range.

In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended 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 constructing a crop growth model, comprising:

constructing an organ level based on the organ level parameters;

constructing an individual level based on the individual level parameter;

Constructing population levels based on the population level parameters;

2. The method of claim 1, wherein the organ level parameters comprise at least corn stalk, vein curves;

the constructing an organ level based on the organ level parameter comprises:

normalizing the vein curve to obtain a target vein curve;

3. The method of claim 1, wherein the constructing an individual level based on the individual level parameter comprises:

determining a maximum blade width based on the maximum blade length;

4. The method of claim 1, wherein the constructing a quaternary phase level based on a quaternary phase level parameter comprises:

determining a leaf expansion rate based on a preset maximum expansion rate;

5. The method of claim 1, wherein the constructing population levels based on population level parameters comprises:

calculating the group leaf area based on a pre-calculated leaf expansion area;

6. The method as recited in claim 1, further comprising:

7. The method as recited in claim 6, further comprising:

8. The method as recited in claim 6, further comprising:

9. The method as recited in claim 6, further comprising:

10. The method as recited in claim 6, further comprising:

11. The method as recited in claim 6, further comprising:

12. A device for constructing a crop growth model, comprising: