CN116756921A - High-light-efficiency plant type characteristic determination method and system based on canopy photosynthetic model - Google Patents
High-light-efficiency plant type characteristic determination method and system based on canopy photosynthetic model Download PDFInfo
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Abstract
The invention provides a high light effect plant type characteristic determining method and system based on a canopy photosynthetic model, comprising the following steps: determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of corn canopy leaves and area index distribution functions of corn canopy leaves of a plurality of groups of corn of different plant types respectively based on a Beta distribution model; simulating various corn canopy structures based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function, and constructing a canopy photosynthetic model; determining the light energy utilization efficiency corresponding to various corn canopy structures respectively based on a canopy photosynthetic model; and determining the canopy structure characteristics corresponding to the high light efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures. The canopy photosynthetic model-based high-light-efficiency plant type characteristic determination method provided by the invention can determine the canopy structural characteristics corresponding to the high-light-efficiency plant type, and provides a reference basis for improving the light energy utilization efficiency of the corn canopy.
Description
Technical Field
The invention relates to the technical field of agricultural information, in particular to a high-light-efficiency plant type characteristic determining method and system based on a canopy photosynthetic model.
Background
Canopy photosynthesis is a key process for determining crop yield, and improving crop canopy light energy utilization efficiency (Radiation Use Efficiency, RUE) by synergistically optimizing crop plant type and leaf nitrogen content vertical distribution is a practical and important way for realizing crop yield increase in the future. The field test and theoretical analysis method are widely used for crop light energy efficient utilization type variety identification, and a crop high light efficiency canopy structure index system is provided initially, so that data support is provided for crop high light efficiency breeding.
At present, the research on the high-light-efficiency plant type of the corn is in a qualitative description stage, and the excellent performance of the high-light-efficiency plant type of the corn on canopy photosynthesis and light distribution is not verified by field experiments or numerical simulation researches, so that the canopy structural characteristics and the vertical distribution of leaf nitrogen of the high-light-efficiency plant type are difficult to be defined, and the design of the high-light-efficiency plant type is lack of quantitative basis.
Disclosure of Invention
The invention provides a high-light-efficiency plant type characteristic determining method and system based on a canopy photosynthetic model, which are used for solving the problem that the canopy structural characteristic and the vertical distribution of leaf nitrogen of a high-light-efficiency plant type are difficult to clearly cause the lack of quantitative basis in the prior art.
The invention provides a high light effect plant type characteristic determining method based on a canopy photosynthetic model, which comprises the following steps:
based on a Beta distribution model, determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of a plurality of groups of corn canopy leaves and a plurality of groups of corn canopy leaf area index distribution functions, wherein the average leaf inclination probability distribution, the vertical distribution data of nitrogen content of a plurality of groups of corn canopy leaves correspond to the corn of different plant types respectively;
simulating various corn canopy structures based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function, and constructing a canopy photosynthetic model;
based on the canopy photosynthetic model, determining the light energy utilization efficiency corresponding to the various corn canopy structures respectively;
and determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
In some embodiments, the canopy photosynthetic model determines the respective corresponding light energy utilization efficiencies of the plurality of corn canopy structures by:
determining the carbon dioxide assimilation amount of each sub-canopy in a target corn canopy in a target period based on the instantaneous photosynthesis rate of the sub-canopy in the target corn canopy at different times in the target period; the target corn canopy is any one of the multiple corn canopy structures, each sub-canopy is determined after layering the target corn canopy, each sub-canopy comprises a male leaf and a female leaf, each sub-canopy has the same average leaf inclination probability distribution, and different sub-canopy has different leaf area indexes;
And determining the light energy utilization efficiency of the target corn canopy in the target period based on the carbon dioxide assimilation quantity of the target corn canopy and the PAR interception quantity of the target corn canopy in the target period.
In some embodiments, the instantaneous photosynthesis rate of each sub-canopy in the target corn canopy at different times within the target period is determined by:
determining a first instantaneous photosynthesis rate of the cationic leaf of each sub-canopy at different moments in the target period and a second instantaneous photosynthesis rate of the anionic leaf of each sub-canopy at different moments in the target period based on the maximum carbon dioxide assimilation rate of the leaf of each sub-canopy at saturated light intensity;
based on the first instantaneous photosynthesis rate and the second instantaneous photosynthesis rate, an instantaneous photosynthesis rate of each sub-canopy in the target corn canopy at a different time within a target period is determined.
The invention also provides a high light effect plant type characteristic determining system based on the canopy photosynthetic model, which comprises the following steps:
the first determining module is used for determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of corn canopy leaves and area index distribution functions of corn canopy leaves of a plurality of groups of corn with different plant types respectively based on the Beta distribution model;
The processing module is used for simulating various corn canopy structures and constructing a canopy photosynthetic model based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function;
the second determining module is used for determining the light energy utilization efficiency corresponding to the various corn canopy structures respectively based on the canopy photosynthetic model;
and the third determining module is used for determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the high light effect plant type characteristic determining method based on the canopy photosynthetic model when executing the program.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a high light efficiency plant type characteristic determination method based on a canopy photosynthetic model as described in any one of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a high light efficiency plant type characteristic determination method based on a canopy photosynthetic model as described in any one of the above.
According to the canopy photosynthetic model-based high-light-efficiency plant type characteristic determination method and system, quantitative characterization of average leaf inclination distribution, she Dan vertical distribution and leaf area index vertical distribution is realized through the Beta distribution model; and the canopy structure characteristics of the high-light-efficiency plant type with high light energy utilization are determined in the virtual corn canopy structure, so that the commonality characteristics of the high-light-efficiency plant type can be acquired more accurately and comprehensively, and a reference basis is provided for improving the light energy utilization efficiency of the corn canopy.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a high light efficiency plant type characteristic determining method based on a canopy photosynthetic model;
FIG. 2 is a schematic diagram of the average leaf inclination probability distribution of the high light efficiency plant type characteristic determination method based on the canopy photosynthetic model;
FIG. 3 is a schematic diagram of vertical distribution of nitrogen content of canopy leaves of the high light effect plant type characteristic determination method based on a canopy photosynthetic model;
FIG. 4 is a schematic diagram of the canopy leaf area index distribution of the canopy photosynthetic model-based high light efficiency plant type characteristic determination method provided by the invention;
FIG. 5 is a schematic diagram showing the change of the light energy utilization efficiency of the high light efficiency plant type characteristic determination method based on the canopy photosynthetic model;
FIG. 6 is a schematic structural diagram of a high light efficiency plant type characteristic determining system based on a canopy photosynthetic model provided by the invention;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The method and system for determining the characteristics of the high-light-efficiency plant type based on the canopy photosynthetic model are described below with reference to fig. 1 to 7.
FIG. 1 is a schematic flow chart of a method for determining plant type characteristics with high light efficiency based on a canopy photosynthetic model. Referring to fig. 1, the method for determining the high light efficiency plant type characteristics based on the canopy photosynthetic model provided by the invention comprises the following steps: step 110, step 120, step 130 and step 140.
Step 110, determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of a plurality of groups of corn canopy leaves and a plurality of groups of corn canopy leaf area index distribution functions, which are respectively corresponding to a plurality of groups of corn of different plant types, based on a Beta distribution model;
step 120, simulating various corn canopy structures based on average leaf inclination probability distribution, corn canopy leaf nitrogen content vertical distribution data and corn canopy leaf area index distribution functions, and constructing a canopy photosynthetic model;
130, determining the light energy utilization efficiency corresponding to various corn canopy structures based on a canopy photosynthetic model;
and 140, determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
The implementation main body of the high-light-efficiency plant type characteristic determining method based on the canopy photosynthetic model can be electronic equipment, a component in the electronic equipment, an integrated circuit or a chip. The electronic device may be a mobile electronic device or a non-mobile electronic device. By way of example, the mobile electronic device may be a cell phone, tablet computer, notebook computer, palm computer, vehicle mounted electronic device, wearable device, ultra-mobile personal computer (ultra-mobile personal computer, UMPC), netbook or personal digital assistant (personal digital assistant, PDA), etc., and the non-mobile electronic device may be a server, network attached storage (Network Attached Storage, NAS), personal computer (personal computer, PC), television (TV), teller machine or self-service machine, etc., without limitation of the present invention.
The technical scheme of the invention is described in detail below by taking a method for determining the characteristics of the high-light-efficiency plant type based on the canopy photosynthetic model, which is provided by the invention, as an example by executing a computer.
In the related technology, corn plant type breeding is mainly judged by field experiments and expert experience, and the quantification degree is insufficient; although the crop canopy photosynthetic model is preliminarily applied, the vertical distribution of the leaf inclination angle and the leaf area is not considered, and the accuracy and the reliability of the optimized result are insufficient; when the existing canopy photosynthetic model is used for designing the high-light-efficiency plant type, cooperative optimization of leaf inclination angle, leaf area and She Dan and vertical distribution thereof is lacking, and canopy structural characteristics of the high-light-efficiency plant type cannot be systematically and comprehensively described.
It should be noted that the light energy utilization efficiency (Radiation Use Efficiency, RUE) refers to the efficiency of converting photosynthetic effective radiation captured by the crop canopy per unit area into photosynthetic assimilation products;
the leaf inclination angle refers to an included angle between the normal line of a fitting plane of the leaf and the vertical direction, and is taken as an important crop structural parameter, is closely related to the spectral reflectivity of crop canopy and the radiation transmission process, and is also a basic parameter of a crop three-dimensional radiation transmission model.
Leaf nitrogen content refers to the proportion of nitrogen element in the leaf molecule. The plant nutrient solution can influence the stable metabolic process on leaves, branches and stems in photosynthesis, is an important element of biosynthesis substances, determines the level of plant nutrient requirements, and can influence the adaptability of plants to external environments. At lower light intensities, both photosynthetic rate and chlorophyll content are on the rise as leaf nitrogen content increases.
Leaf Area Index (LAI) refers to the Leaf Area per unit of ground and is one of the most prominent features of vegetation canopy. Or the ratio of the sum of plant leaf areas to the land area over a certain land area.
Beta distribution is a continuous probability distribution defined in the (0, 1) interval, which can be understood as a probability density distribution of the probability of occurrence of an event.
The high light efficiency plant type design refers to a method for improving the light energy conversion efficiency of the canopy by improving the plant type and optimizing the light receiving form of the plant so as to lead the light nitrogen to be distributed in a coordinated manner.
In actual implementation, the corn canopy structure parameters are digitally characterized based on the Beta distribution model. The corn canopy structure parameters include average leaf inclination angle, canopy leaf nitrogen content, canopy leaf area index and the like.
1. Average leaf inclination angle
The probability of the leaf inclination angle of the crop is subjected to Beta distribution, and the average leaf inclination probability distribution corresponding to a plurality of groups of corns with different plant types can be generated by utilizing a Beta distribution model.
For example: corn can be classified into three types according to plant types, namely compact type, flat type, semi-compact type and the like.
As shown in fig. 2, using the Beta distribution model, a 28-group average leaf angle distribution covering flat to compact corn can be generated, with the average values in turn:
13.30°、14.96°、16.94°、18.92°、21.38°、23.94°、26.52°、29.34°、31.65°、33.84°、36.49°、39.06°、41.65°、43.74°、46.27°、48.35°、50.94°、53.51°、56.16°、58.35°、60.66°、63.48°、66.06°、68.62°、71.08°、73.06°、75.04°、76.70°。
2. vertical distribution of nitrogen content in canopy leaves
In practical implementation, a Gamma distribution model, an Exponential distribution model and a Beta distribution model can be used for simulating a plurality of groups of vertical distribution data of the nitrogen content of the corn canopy, and the three simulation results are compared with standard errors of measured data.
In the invention, the standard error under the Beta distribution model is minimum, namely the compliance degree of the vertical distribution data of the nitrogen content of the corn canopy to Beta distribution is highest.
For example: 12 groups of vertical distribution data of the nitrogen content of the corn canopy are extracted from the related technology, and are respectively simulated by using a Gamma distribution model, an Exponential distribution model and a Beta distribution model pair.
As shown in Table 1, table 1 is used to indicate how well the vertical distribution of nitrogen content in the canopy of corn is subjected to Gamma distribution, exponential distribution and Beta distribution, respectively.
TABLE 1
The 28 sets of canopy leaf nitrogen content vertical distributions were generated using the Beta distribution model such that the locations where leaf nitrogen content peaks appear covered from top of canopy to bottom of canopy, as shown in figure 3.
3. Vertical distribution of canopy leaf area index
Assuming that any corn canopy is divided into a plurality of layers, the cumulative leaf area index of each layer can be quantitatively expressed by using a Beta growth equation as follows:
LAI is the cumulative leaf area index reached at the bottom of the corn canopy; n is the layering number of corn canopy layers; z e Is the distance between the bottom of the canopy and the top of the canopy; z m For canopy from top to bottom, z is the distance from any position in the corn canopy to the top of the canopy.
In some embodiments, the LAI is set to a total of 5 levels of 3, 4, 5, 6 and 7, and at each LAI level, z is set m Set to a total of 3 heights of 0.4, 0.5 and 0.6, forming a total of 15 sets of leaf area index-like corn canopy structures, as shown in fig. 4.
Referring to FIG. 4, five graphs each represent 3 different canopy structures at 5 leaf area levels generated based on the beta growth equation. Maximum LAI position a in each graph: the maximum leaf area is 40% of the canopy height, namely the index of the middle and upper leaf area is the maximum; maximum LAI position b: the maximum leaf area is 50% of the split position, namely the maximum index of the middle leaf area; maximum LAI position c: the leaf area is at most 60% split, i.e. the mid-lower leaf area index is at most.
Based on the average leaf inclination probability distribution, the vertical distribution data of the nitrogen content of the corn canopy leaf and the corn canopy leaf area index distribution function, performing feature combination, and simulating various corn canopy structures. And constructing a canopy photosynthetic model on the basis of considering canopy leaf inclination probability distribution, leaf area index distribution and corn canopy nitrogen content vertical distribution obeying Beta distribution.
Based on the canopy photosynthetic model, the light energy utilization efficiency of each simulated maize canopy structure can be determined.
In actual implementation, a large number of virtual corn canopy structures can be simulated, high-light-efficiency plant types capable of efficiently utilizing light energy are searched from the large number of virtual corn canopy structures by utilizing an exhaustion algorithm, and common characteristics of the high-light-efficiency plant types are determined, wherein the common characteristics can be used as reference bases for the corn high-light-efficiency canopy structure characteristics or the improved corn plant types.
For example: the corn leaves have large inclination angles, the plant types are more compact, the light transmittance under the close planting condition is better, the light interception condition at the middle and lower parts is better, the degree of matching with the leaves with strong photosynthetic performance is high, the photosynthetic products of the corn kernels are more sufficiently supplied, and the light energy utilization efficiency of the canopy is expected to be further improved.
According to the canopy photosynthetic model-based high-light-efficiency plant type characteristic determination method, quantitative characterization of average leaf inclination distribution, she Dan vertical distribution and leaf area index vertical distribution is realized through the Beta distribution model; and the canopy structure characteristics of the high-light-efficiency plant type with high light energy utilization are determined in the virtual corn canopy structure, so that the commonality characteristics of the high-light-efficiency plant type can be acquired more accurately and comprehensively, and a reference basis is provided for improving the light energy utilization efficiency of the corn canopy.
In some embodiments, the canopy photosynthetic model determines the light energy utilization efficiency of each of the plurality of corn canopy structures by:
determining the carbon dioxide assimilation amount of the target corn canopy in the target period based on the instantaneous photosynthesis rates of each sub canopy in the target corn canopy at different moments in the target period; the target corn canopy is any one of a plurality of corn canopy structures, each sub canopy is determined after layering the target corn canopy, each sub canopy comprises a cationic leaf and a anionic leaf, each sub canopy has the same average leaf inclination probability distribution, and different sub canopies have different leaf area indexes;
and determining the light energy utilization efficiency of the target corn canopy in the target period based on the carbon dioxide assimilation amount of the target corn canopy in the target period.
The following description will be made with any one of a variety of corn canopy structures as the target corn canopy.
In practical implementation, the LAI of the target corn canopy is divided into several sub-canopy layers, for example, may be divided into 10 sub-canopy layers. The leaf area index of each sub-canopy can be calculated from the corn canopy leaf area index distribution function in the above embodiment.
Taking the example that the range of the leaf inclination angle is 0-90 degrees, dividing 0-90 degrees into 9 leaf inclination angle grades with equal intervals, namely dividing one leaf inclination angle grade every 10 degrees of the leaf inclination angle, and assuming that each sub-canopy has the same average leaf inclination angle probability distribution.
It will be appreciated that the different sub-canopy layers have different leaf area indices, each sub-canopy layer in turn being divided into a cationic leaf that receives both direct and scattered light and a anionic leaf that receives only scattered light.
The instantaneous photosynthesis rate of each sub-canopy at different times within the target period is calculated separately. Where the rate of photosynthesis refers to the rate at which photosynthesis fixes carbon dioxide (or generates oxygen). The instantaneous photosynthesis rates of each sub-canopy at different times within the target period are integrated into the entire target maize canopy, and the canopy photosynthesis rates of the target maize canopy at different times within the target period can be determined.
By photosynthesis rate of canopy of target corn canopy at different times within target periodIntegrating the rate to obtain the carbon dioxide CO of the target corn canopy in the target period 2 Assimilation amount. Finally, based on CO of the target corn canopy in the target period 2 And (5) assimilating amount, and determining the light energy utilization efficiency of the target corn canopy in the target period.
In some embodiments, prior to determining the light energy utilization efficiency of the target corn canopy within the target period, the method further comprises:
determining extinction coefficients of direct PAR corresponding to the sun-generated leaves of each sub-canopy at different moments based on average projection parameters of all leaves of the target corn canopy in the sunlight beam direction and sun altitude angles at different moments in the target period;
the extinction coefficient of the direct PAR corresponding to the cationic leaf of each sub-canopy at different times is determined by:
wherein ,for the extinction coefficient of the direct PAR corresponding to the positive leaf of each sub-canopy at time t in the target period, O av Is an average projection parameter; beta t Is the solar altitude at time t within the target period;
determining PAR interception amounts of the target corn canopy at different moments in the target period based on direct PAR and scattered PAR of each canopy at different moments in the target period;
PAR interception amounts of the target corn canopy at different moments in a target period are determined by the following formula:
wherein Δidir n,t As the difference between the direct PAR of two adjacent sub-crowns at time t, deltaIdiff n,t For the difference between the scattered PAR of two adjacent sub-crowns at time t；
Determining the PAR interception amount of the target corn canopy in the target period based on the PAR interception amounts of the target corn canopy at different moments in the target period;
The PAR interception of the target corn canopy in the target period is determined by the following formula:
the present step is described below by taking the target period as an example of the time t of day, where t may be any time of day. In actual practice, the extinction coefficient of the direct photosynthetically active radiation (Photosynthetically Active R adiation, PAR) corresponding to the cationic leaf of each sub-canopy at time tThe method comprises the following steps:
wherein ,βt Is the solar altitude at time t in the day; o (O) av Is the average projection parameter of all leaves of the target corn canopy in the direction of the sunlight beam. Assuming that all leaves in the target maize canopy have a uniform azimuthal direction, O av,a It can be calculated as:
wherein ,βL Is the leaf inclination angle; a is the number of preset leaf inclination angle grades, and in the invention, the number of the preset leaf inclination angle grades can be divided into 9 leaf inclination angle grades; beta La Is the leaf inclination angle of the leaf of the a-th preset leaf inclination angle grade corresponding to the leaf respectively; o (O) av,a A second average projection parameter of the leaf with the preset leaf inclination angle grade a in the sunlight beam direction is set; average projection parameter O of all leaves of target corn canopy in sunlight beam direction av By the following methodAnd (3) determining:
wherein ,fa The ratio of the leaf with the a-th preset leaf inclination angle grade to all the leaves of the target corn canopy is obtained.
In actual implementation, idir n,t For the direct PAR (μmol photons m) per unit floor area of the nth sub-canopy at time t 2 s -1 )，Idiff n,t For the scattered PAR (μmol photons m) per unit ground area of the nth sub-canopy at time t in the day 2 s -1 ) The calculation is as follows:
wherein ,Kdiff The extinction coefficient of the scattered PAR is 0.7;the extinction coefficient of the direct PAR corresponding to the sun-generated leaf of each sub-canopy at the time t in the day; l (L) n Idir is the leaf area index of the nth sub-canopy 0,t As direct PAR, idiff per unit ground area on top of canopy 0,t Is the scattered PAR per unit area of ground on top of the canopy.
The interception amount of PAR of the whole target corn canopy at the time t in one day is calculated as follows:
ΔIdir n,t ＝Idir n,t -Idir n+1,t (7)
ΔIdiff n,t ＝Idiff n,t -Idiff n+1,t (8)
wherein Δidir n,t The difference between the direct PAR of the nth sub-canopy on the unit ground area at the time t and the direct PAR of the (n+1) th sub-canopy on the unit ground area at the time t; ΔIdiff n,t The difference between the scattered PAR per unit floor area of the nth sub-canopy at time t and the scattered PAR per unit floor area of the (n+1) th sub-canopy at time t.
The daily PAR interception amount of the whole target corn canopy in one day is calculated as follows:
wherein 4.55 is used to carry out the reaction of. Mu. Mol.m -2 ·s -1 Conversion to J.m -2 ·s -1 。
In some embodiments, the instantaneous photosynthesis rate of each sub-canopy in the target corn canopy at different times within the target period is determined by:
Determining a first instantaneous photosynthesis rate of the cationic leaves of each sub-canopy at different moments in a target period and a second instantaneous photosynthesis rate of the anionic leaves of each sub-canopy at different moments in the target period based on a maximum carbon dioxide assimilation rate of the leaves of each sub-canopy at saturated light intensity;
based on the first instantaneous photosynthesis rate and the second instantaneous photosynthesis rate, an instantaneous photosynthesis rate of each of the sub-canopy layers in the target corn canopy at different times within the target period of time is determined.
In actual practice, each sub-canopy is divided into a positive lobe that receives both direct and scattered light and a negative lobe that receives only scattered light. The first instantaneous photosynthetic rate of the male leaf part and the second instantaneous photosynthetic rate of the female leaf part of each sub-canopy are calculated respectively, and the probability distribution of the inclination angle of the leaf, the proportion of the female leaf and the proportion of the male leaf are considered at the same time, and are integrated into the whole target corn canopy, so that the instantaneous photosynthetic rate of each sub-canopy in the target corn canopy at different moments in the target period can be determined.
In some embodiments, the maximum carbon dioxide assimilation rate of the leaf at saturated light intensity for each sub-canopy is determined by:
Wherein n is the number of layers of the sub-crown layer, A max,n For the maximum carbon dioxide assimilation rate of the nth sub-canopy under saturated light intensity, SLN n A ratio She Dan for the nth sub-cap;
the specific leaf nitrogen of the nth sub-canopy is determined by:
wherein LNCT n Leaf nitrogen content of nth sub-canopy, LA n Representing the blade area of the nth sub-canopy;
the blade area of the nth sub-canopy is determined by:
wherein ,Ln Leaf area index of the nth sub-canopy, ρ is planting density;
the leaf area index of the nth sub-canopy is determined by:
the LAI is an accumulated leaf area index reached by the bottom of the target corn canopy; z e Is the distance between the bottom of the canopy and the top of the canopy; z m For the position where the canopy from the top to the bottom reaches the maximum value of the layering leaf area index, z is the distance from any position in the target corn canopy to the top of the canopy;
the first instantaneous rate of action is determined by:
wherein t is any time within the target period; a is a preset leaf inclination angle grade, and each preset leaf inclination angle grade corresponds to a leaf inclination angle value range; asun n,a,t A first instantaneous light action rate of a cationic leaf with a preset leaf inclination angle grade for an a-th sub-canopy in a target period at a time t; a is apparent quantum efficiency, isun n,a,t The PAR interception strength of unit leaf area of the a-th blade with preset blade inclination angle grade of the nth sub-canopy under the sunlight irradiation of the t moment in the target period; θ is a preset empirical coefficient;
the second transient photosynthesis rate is determined by the following formula:
wherein ,Ashn,t A second instantaneous photosynthesis rate of the pudendum leaf at a time t in the target period of time for the nth sub-canopy; ish (Ish) n,t PAR interception intensity is the unit leaf area of the pudendum leaf of the nth sub-canopy at the t moment in the target period.
In practical implementation, the proportion fsun of the cationic leaf of the nth sub-canopy at time t in one day is determined n,t And the pudendum leaf ratio fsh of the nth sub-canopy at time t in one day n,t The calculation is as follows:
fsh n,t ＝1-fsun n,t (12)
wherein ,the extinction coefficient of the direct PAR corresponding to the sun-generated leaf of each sub-canopy at the time t in the day; l (L) n Leaf area index for nth sub-canopy; l (L) n+1 Is the leaf area index of the n+1th sub-canopy.
First instantaneous rate of action of light Asun of cationic leaf of nth sub-canopy at time t n,a,t And a second instantaneous photosynthesis rate Ash of yin-green leaves of an nth sub-canopy at time t in one day n,t The calculation is as follows:
wherein a is a preset leaf inclination angle grade, and each preset leaf inclination angle grade corresponds to a leaf inclination angle value range; asun n,a,t The first instantaneous light action rate of the sun-generated leaf with the a-th preset leaf inclination angle grade of the nth sub-canopy in the day at the t moment is set; a is apparent quantum efficiency, and θ is a preset experience coefficient; ash (Ash) n,t A second instantaneous photosynthesis rate of the pudendum leaf at time t of the nth sub-canopy in the day;
Isun n,a,t PAR interception strength, ish, of unit leaf area of a leaf with a preset leaf inclination angle grade of an nth sub-canopy in a day under the irradiation of sunlight at time t n,t The PAR interception intensity of the unit leaf area of the pudendum leaf of the nth sub-canopy in the day at the time t is calculated as follows:
wherein ,ξa,t A blade inclination angle of the blade representing the a-th preset blade inclination angle level relative to the direct radiation ray at the time t; beta t Is the solar altitude at time t in the day; l (L) n Leaf area for nth sub-canopyAn index; l (L) n+1 Leaf area index for the n+1th sub-canopy; ΔIdiff n,t The difference of the scattering PAR of two adjacent sub-crowns at the time t; idir 0,t Is the direct PAR per unit area of ground at the top of the canopy.
A max,n Representing the maximum CO of the blade of the nth sub-canopy under saturated light intensity 2 Assimilation Rate (μmol CO) 2 m 2 s -1 ) According to the ratio She Dan (SLN) to the photosynthetic rate (A max ) The curve relationship of (2) is calculated as follows:
wherein ,SLNn A ratio She Dan for the nth sub-cap; LNCT n Leaf nitrogen content of nth sub-canopy, LA n Representing the blade area of the nth sub-canopy; l (L) n Leaf area index of the nth sub-canopy, ρ is planting density; 44 is denoted as CO 2 Molar mass (g mol) -1 )。
In some embodiments, the instantaneous photosynthetic rate of the target maize canopy at different times within the target period is represented by the following formula:
wherein ,Acan,t The instantaneous canopy photosynthesis rate of the target corn canopy at time t in the target period; asun n,a,t For the first instantaneous rate of action of light, ash n,t For the second transient photosynthesis rate, fsun n,t The sun-generated of the nth sub-canopy at time tShe Zhanbi; fsh (fsh) n,t The ratio of the pudendum leaves of the nth sub-canopy at the t moment; f (f) a For characterising the mean leaf inclination probability distribution, f a The ratio of the leaf inclination angle grade is preset in the a.
In actual practice, the instantaneous canopy photosynthesis rate A at time t in the day can,t The calculation is as follows:
wherein ,Ln Leaf area index for nth sub-canopy; fsun (fsun) n,t The ratio of the cationic leaves of the nth sub-canopy at the t moment; fsh (fsh) n,t The ratio of the pudendum leaves of the nth sub-canopy at the t moment; asun n,a,t First instantaneous rate of action of light and Ash at time t for a cationic leaf of a preset leaf inclination level of an nth sub-canopy in a day n,t Is the second instantaneous photosynthesis rate of the pudendum leaf at the time t of the nth sub-canopy in the day.
In some embodiments, determining the carbon dioxide assimilation amount of the target maize canopy within the target period based on the instantaneous photosynthesis rate of each sub-canopy in the target maize canopy at different times within the target period comprises:
and integrating the instantaneous photosynthesis rate of each sub-canopy at different moments in the target period in a time scale to determine the carbon dioxide assimilation quantity of the target corn canopy in the target period.
In actual implementation, will A can,t Integrating in the daily scale to obtain daily CO of the target corn canopy 2 Assimilation quantity (A) canDAY,d ) The calculation is as follows:
based on the carbon dioxide assimilation amount of the target corn canopy and the PAR interception amount of the target corn canopy in the target period, determining the light energy utilization efficiency of the target corn canopy in the target period comprises:
determining a daily accumulation of dry matter from the aerial parts of the corn based on the carbon dioxide assimilation amount of the target corn canopy within the target period;
and determining the light energy utilization efficiency of the target corn canopy in the target period based on the daily accumulation amount of the dry matter on the corn and the PAR interception amount of the target corn canopy in the target period.
In actual implementation, the method for calculating the solar energy utilization efficiency of the target corn canopy layer by layer is as follows:
wherein, the daily accumulation amount DM of dry matter on the overground parts of the corns DAY ，IPAR DAY,d PAR daily interception (MJ m) for target maize canopy -2 d -1 )。
Daily accumulation of dry matter DM of the aerial parts of corn DAY The calculation is as follows:
DM DAY ＝44A canDAY B×10 -6 (23)
wherein 44 is denoted as CO 2 Molar mass (g mol) -1 )；A canDAY,d Daily CO for target corn canopy in a day 2 Assimilation amount; b is CO of unit mass 2 The dry matter produced was a corn equivalent dry matter value of 0.41.
In some embodiments, 28 leaf inclination angle distribution types, 28 nitrogen content distribution types and 15 leaf area index distribution types are combined, and the radiation utilization efficiency, namely the light energy utilization efficiency, of the canopy of the corn plant 61 days after flowers is calculated in a simulation mode. The simulation results are shown in fig. 5.
FIG. 5 is a schematic RUE of maize canopy structure with different combinations of average leaf inclination, she Dan vertical distribution and leaf area index distribution. As shown in fig. 5, the maximum RUE position and size can be seen for each leaf area index distribution. Wherein, figures A-C: RUE response with leaf area index 3; graphs D-F: RUE response with leaf area index 4; graph G-I: RUE response with leaf area index 5; graph J-L: RUE response with leaf area index 6; graph M-O: RUE response with leaf area index 7.
RUEs respond differently to different leaf area index distributions, with higher RUEs increasing in combination with increasing leaf area index, but this trend changes slowly in higher leaf area index scenarios. This trend is evident when the corn canopy leaf area index is at its maximum in the middle lower portion. The maximum RUE value occurs at a downward shift (where the greater nitrogen content of the corn canopy leaf is concentrated toward the lower portion) as the canopy leaf area index reaches the highest position, and the maximum RUE value increases.
In addition, as the leaf area index increases, the position of the maximum RUE value is changed from the leaf inclination angle 68.62 degrees (A24) to 71.08 degrees (A25), the larger position of the corn canopy leaf nitrogen content ratio is concentrated to the upper part, and the maximum RUE value tends to increase and decrease with the increase of the leaf area index.
According to the canopy photosynthetic model-based high-light-efficiency plant type characteristic determination method provided by the invention, the statistical method proves that the vertical distribution of leaf nitrogen in the canopy of corn accords with Beta distribution, and quantitative characterization on average leaf inclination distribution, she Dan vertical distribution and leaf area index vertical distribution is realized by using the Beta distribution model. Based on the improved canopy photosynthetic model, the common characteristics of the canopy with high light energy utilization efficiency are searched from the mass virtual corn canopy structure by using an exhaustion algorithm, namely, the average leaf inclination angle is 71.08 degrees, and the light nitrogen resources reach cooperative distribution.
The high-light-efficiency plant type characteristic determining system based on the canopy photosynthetic model provided by the invention is described below, and the high-light-efficiency plant type characteristic determining system based on the canopy photosynthetic model described below and the high-light-efficiency plant type characteristic determining method based on the canopy photosynthetic model described above can be correspondingly referred to each other.
Fig. 6 is a schematic structural diagram of a canopy photosynthetic model-based high-light-efficiency plant type characteristic determining system provided by the invention. Referring to fig. 6, the high light efficiency plant type characteristic determining system based on a canopy photosynthetic model provided by the invention comprises:
the first determining module 610 is configured to determine, based on a Beta distribution model, average leaf inclination probability distributions, multiple groups of vertical distribution data of nitrogen content of corn canopy leaf and a corn canopy leaf area index distribution function corresponding to multiple groups of corn of different plant types respectively;
the processing module 620 is configured to simulate a plurality of corn canopy structures based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function, and construct a canopy photosynthetic model;
a second determining module 630, configured to determine light energy utilization efficiencies corresponding to the various maize canopy structures respectively based on the canopy photosynthetic model;
And a third determining module 640, configured to determine canopy structure characteristics corresponding to the high light efficiency plant type based on the light energy utilization efficiency corresponding to the multiple corn canopy structures.
According to the canopy photosynthetic model-based high-light-efficiency plant type characteristic determination system, quantitative characterization of average leaf inclination distribution, she Dan vertical distribution and leaf area index vertical distribution is realized through the Beta distribution model; and the canopy structure characteristics of the high-light-efficiency plant type with high light energy utilization are determined in the virtual corn canopy structure, so that the commonality characteristics of the high-light-efficiency plant type can be acquired more accurately and comprehensively, and a reference basis is provided for improving the light energy utilization efficiency of the corn canopy.
In some embodiments, the second determining module 630 is specifically configured to:
determining the carbon dioxide assimilation amount of each sub-canopy in a target corn canopy in a target period based on the instantaneous photosynthesis rate of the sub-canopy in the target corn canopy at different times in the target period; the target corn canopy is any one of the multiple corn canopy structures, each sub-canopy is determined after layering the target corn canopy, each sub-canopy comprises a male leaf and a female leaf, each sub-canopy has the same average leaf inclination probability distribution, and different sub-canopy has different leaf area indexes;
And determining the light energy utilization efficiency of the target corn canopy in the target period based on the carbon dioxide assimilation quantity of the target corn canopy and the PAR interception quantity of the target corn canopy in the target period.
In some embodiments, the instantaneous photosynthesis rate of each sub-canopy in the target corn canopy at different times within the target period is determined by:
determining a first instantaneous photosynthesis rate of the cationic leaf of each sub-canopy at different moments in the target period and a second instantaneous photosynthesis rate of the anionic leaf of each sub-canopy at different moments in the target period based on the maximum carbon dioxide assimilation rate of the leaf of each sub-canopy at saturated light intensity;
based on the first instantaneous photosynthesis rate and the second instantaneous photosynthesis rate, an instantaneous photosynthesis rate of each sub-canopy in the target corn canopy at a different time within a target period is determined.
In some embodiments, the maximum carbon dioxide assimilation rate of the leaf at saturated light intensity for each of the canopy layers is determined by:
wherein n is the number of layers of the sub-crown layer, A max,n For the maximum carbon dioxide assimilation rate of the nth sub-canopy under saturated light intensity, SLN n A ratio She Dan for the nth sub-canopy;
the specific leaf nitrogen of the nth sub-canopy is determined by:
wherein LNCT n For the leaf nitrogen content of the nth sub-canopy, LA n Representing the blade area of the nth sub-canopy;
the blade area of the nth sub-canopy is determined by:
wherein ,Ln Leaf area index of the nth sub-canopy, ρ is planting density;
the leaf area index of the nth sub-canopy is determined by:
wherein LAI is the cumulative leaf area index reached at the bottom of the target corn canopy; z e Is the distance between the bottom of the canopy and the top of the canopy; z m Z is the distance from any position in the target corn canopy to the top of the canopy, wherein z is the position where the canopy starts from the top to the bottom and the leaf area index reaches the maximum value;
the first instantaneous rate of action is determined by:
wherein t is any time within the target period; a is a preset leaf inclination angle grade, and each preset leaf inclination angle grade corresponds to a leaf inclination angle value range; asun n,a,t A first instantaneous light action rate at time t in the target period for a Yang Shengshe of a preset leaf inclination angle grade of an nth sub-canopy; a is apparent quantum efficiency, isun n,a,t The PAR interception intensity of unit leaf area of the a-th blade with the preset blade inclination angle grade of the nth sub-canopy under the sunlight irradiation at the time t in the target period; θ is a preset empirical coefficient;
the second transient photosynthesis rate is determined by:
wherein ,Ashn,t Is the firstA second instantaneous photosynthesis rate of the pudendum leaves of the n sub-canopy at time t within the target period; ish (Ish) n,t And the PAR interception intensity of the unit leaf area of the pudendum leaf of the nth sub-canopy at the t moment in the target period is obtained.
In some embodiments, the instantaneous photosynthetic rate of the target maize canopy at different times within the target period is represented by the following formula:
wherein ,Acan,t A transient canopy photosynthesis rate for the target corn canopy at time t within the target period; asum (Asum) n,a,t For the first instantaneous rate of action of light, ash n,t For the second transient photosynthesis rate, fsun n,t The ratio of the cationic leaves of the nth sub-canopy at the time t is the ratio of the cationic leaves of the nth sub-canopy at the time t; fsh (fsh) n,t The ratio of the pudendum leaves of the nth sub-canopy at the t moment is; f (f) a For characterising said mean leaf inclination probability distribution, f a The ratio of the leaf inclination angle grade is preset in the a.
In some embodiments, the determining the carbon dioxide assimilation amount of the target maize canopy within the target period based on the instantaneous photosynthesis rate of each sub-canopy within the target maize canopy at different times within the target period comprises:
integrating the instantaneous photosynthesis rate of each sub-canopy at different moments in the target period of time on a time scale to determine the carbon dioxide assimilation quantity of the target corn canopy in the target period of time;
the determining the light energy utilization efficiency of the target corn canopy in the target period based on the carbon dioxide assimilation amount of the target corn canopy and the PAR interception amount of the target corn canopy in the target period includes:
determining a daily accumulation of dry matter from the aerial parts of corn based on the carbon dioxide assimilation amount of the target corn canopy within the target period of time;
and determining the light energy utilization efficiency of the target corn canopy within the target period based on the daily accumulation of dry matter on the corn overground and the PAR interception of the target corn canopy.
In some embodiments, before the determining the light energy utilization efficiency of the target corn canopy based on the daily accumulation of dry matter on the corn aerial parts and the PAR interception of the target corn canopy within the target period of time, the method further comprises:
Determining extinction coefficients of direct PAR corresponding to the sun-generated leaves of each sub-canopy at different moments based on average projection parameters of all leaves of the target corn canopy in the sunlight beam direction and sun altitude angles at the different moments in the target period;
the extinction coefficients of the direct PAR corresponding to the cationic leaf of each sub-canopy at different moments are determined by the following formula:
wherein ,for the extinction coefficient of the direct PAR corresponding to the sun leaf of each sub-canopy at time t in the target period, O av Is the average projection parameter; beta t -a solar altitude at time t within the target period;
determining PAR interception amounts of the target corn canopy at different moments in the target period based on direct PAR and scattered PAR of each sub canopy at different moments in the target period;
PAR interception amounts of the target corn canopy at different moments in the target period are determined by the following formula:
wherein Δidir n,t As the difference between the direct PAR of two adjacent sub-crowns at time t, deltaIdiff n,t The difference of the scattering PAR of two adjacent sub-crowns at the time t;
determining PAR interception amounts of the target corn canopy in the target period based on the PAR interception amounts of the target corn canopy at different moments in the target period;
The PAR interception amount of the target corn canopy in the target period is determined by the following formula:
fig. 7 illustrates a physical schematic diagram of an electronic device, as shown in fig. 7, which may include: processor 710, communication interface (Communications Interface) 720, memory 730, and communication bus 740, wherein processor 710, communication interface 720, memory 730 communicate with each other via communication bus 740. Processor 710 may invoke logic instructions in memory 730 to perform a high light efficiency plant type characterization method based on a canopy photosynthetic model, the method comprising:
based on a Beta distribution model, determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of corn canopy leaves and an index distribution function of leaf areas of the corn canopy of a plurality of groups of corn with different plant types respectively;
simulating various corn canopy structures based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function, and constructing a canopy photosynthetic model;
based on the canopy photosynthetic model, determining the light energy utilization efficiency corresponding to the various corn canopy structures respectively;
And determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
Further, the logic instructions in the memory 730 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, where the computer program, when executed by a processor, can perform a method for determining a high light efficiency plant type feature based on a canopy photosynthetic model provided by the above methods, where the method includes:
Based on a Beta distribution model, determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of corn canopy leaves and an index distribution function of leaf areas of the corn canopy of a plurality of groups of corn with different plant types respectively;
simulating various corn canopy structures based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function, and constructing a canopy photosynthetic model;
based on the canopy photosynthetic model, determining the light energy utilization efficiency corresponding to the various corn canopy structures respectively;
and determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
In yet another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for determining high light efficiency plant type characteristics based on a canopy photosynthetic model provided by the above methods, the method comprising:
based on a Beta distribution model, determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of corn canopy leaves and an index distribution function of leaf areas of the corn canopy of a plurality of groups of corn with different plant types respectively;
Simulating various corn canopy structures based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function, and constructing a canopy photosynthetic model;
based on the canopy photosynthetic model, determining the light energy utilization efficiency corresponding to the various corn canopy structures respectively;
and determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The high-light-efficiency plant type characteristic determining method based on the canopy photosynthetic model is characterized by comprising the following steps of:
based on a Beta distribution model, determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of a plurality of groups of corn canopy leaves and a plurality of groups of corn canopy leaf area index distribution functions, wherein the average leaf inclination probability distribution, the vertical distribution data of nitrogen content of a plurality of groups of corn canopy leaves correspond to the corn of different plant types respectively;
simulating various corn canopy structures based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function, and constructing a canopy photosynthetic model;
based on the canopy photosynthetic model, determining the light energy utilization efficiency corresponding to the various corn canopy structures respectively;
And determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
2. The method for determining the high-light-efficiency plant type characteristics based on the canopy photosynthetic model according to claim 1, wherein the canopy photosynthetic model determines the light energy utilization efficiency respectively corresponding to the plurality of corn canopy structures by:
determining the carbon dioxide assimilation amount of each sub-canopy in a target corn canopy in a target period based on the instantaneous photosynthesis rate of the sub-canopy in the target corn canopy at different times in the target period; the target corn canopy is any one of the multiple corn canopy structures, each sub-canopy is determined after layering the target corn canopy, each sub-canopy comprises a male leaf and a female leaf, each sub-canopy has the same average leaf inclination probability distribution, and different sub-canopy has different leaf area indexes;
and determining the light energy utilization efficiency of the target corn canopy in the target period based on the carbon dioxide assimilation quantity of the target corn canopy and the PAR interception quantity of the target corn canopy in the target period.
3. The canopy photosynthetic model-based high light efficiency plant type characteristic determination method of claim 2, wherein the instantaneous photosynthesis rate of each sub-canopy in the target corn canopy at different times within a target period is determined by:
determining a first instantaneous photosynthesis rate of the cationic leaf of each sub-canopy at different moments in the target period and a second instantaneous photosynthesis rate of the anionic leaf of each sub-canopy at different moments in the target period based on the maximum carbon dioxide assimilation rate of the leaf of each sub-canopy at saturated light intensity;
based on the first instantaneous photosynthesis rate and the second instantaneous photosynthesis rate, an instantaneous photosynthesis rate of each sub-canopy in the target corn canopy at a different time within a target period is determined.
4. The method for determining the high-light-efficiency plant type characteristics based on the canopy photosynthetic model according to claim 3, wherein the maximum carbon dioxide assimilation rate of the leaf of each canopy under saturated light intensity is determined by the following formula:
wherein n is the number of layers of the sub-crown layer, A max,n For the maximum carbon dioxide assimilation rate of the nth sub-canopy under saturated light intensity, SLN n A ratio She Dan for the nth sub-canopy;
the specific leaf nitrogen of the nth sub-canopy is determined by:
wherein LNCT n For the leaf nitrogen content of the nth sub-canopy, LA n Representing the blade area of the nth sub-canopy;
the blade area of the nth sub-canopy is determined by:
wherein ,Ln Leaf area index of the nth sub-canopy, ρ is planting density;
the leaf area index of the nth sub-canopy is determined by:
wherein LAI is the cumulative leaf area index reached at the bottom of the target corn canopy; z e Is the distance between the bottom of the canopy and the top of the canopy; z m Z is the distance from any position in the target corn canopy to the top of the canopy, wherein z is the position where the canopy starts from the top to the bottom and the leaf area index reaches the maximum value;
the first instantaneous rate of action is determined by:
wherein t is any time within the target period; a is the preset leaf inclination angle grade, each preset leaf inclination angle and the likeThe stage corresponds to a leaf inclination angle value range; asun n,a,t A first instantaneous light action rate at time t in the target period for a Yang Shengshe of a preset leaf inclination angle grade of an nth sub-canopy; alpha is apparent quantum efficiency, isun n,a,t The PAR interception intensity of unit leaf area of the a-th blade with the preset blade inclination angle grade of the nth sub-canopy under the sunlight irradiation at the time t in the target period; θ is a preset empirical coefficient;
the second transient photosynthesis rate is determined by:
wherein ,Ashn,t A second instantaneous photosynthesis rate of the pudendum leaf for the nth sub-canopy at time t within the target period; ish (Ish) n,t And the PAR interception intensity of the unit leaf area of the pudendum leaf of the nth sub-canopy at the t moment in the target period is obtained.
5. The canopy photosynthetic model-based high light efficiency plant type characteristic determination method of claim 4, wherein the instantaneous photosynthesis rates of the target corn canopy at different times within a target period are represented by the following formula:
wherein ,Acan,t A transient canopy photosynthesis rate for the target corn canopy at time t within the target period; asun n,a,t For the first instantaneous rate of action of light, ash n,t For the second transient photosynthesis rate, fsun n,t The ratio of the cationic leaves of the nth sub-canopy at the time t is the ratio of the cationic leaves of the nth sub-canopy at the time t; fsh (fsh) n,t The ratio of the pudendum leaves of the nth sub-canopy at the t moment is; f (f) a For characterising said mean leaf inclination probability distribution, f a For the a-th preset leaf inclination angleThe rank is the proportion.
6. The canopy photosynthetic model-based high light efficiency plant type characteristic determination method of claim 5, wherein the determining the carbon dioxide assimilation amount of the target corn canopy within the target period based on the instantaneous photosynthesis rate of each sub-canopy within the target corn canopy at different times within the target period comprises:
integrating the instantaneous photosynthesis rate of each sub-canopy at different moments in the target period of time on a time scale to determine the carbon dioxide assimilation quantity of the target corn canopy in the target period of time;
the determining the light energy utilization efficiency of the target corn canopy in the target period based on the carbon dioxide assimilation amount of the target corn canopy and the PAR interception amount of the target corn canopy in the target period includes:
determining a daily accumulation of dry matter from the aerial parts of corn based on the carbon dioxide assimilation amount of the target corn canopy within the target period of time;
and determining the light energy utilization efficiency of the target corn canopy within the target period based on the daily accumulation of dry matter on the corn overground and the PAR interception of the target corn canopy.
7. The method for determining high light efficiency plant type characteristics based on a canopy photosynthetic model of claim 6, wherein prior to determining the light energy utilization efficiency of the target canopy of corn in the target period of time based on the daily accumulation of dry matter on the aerial parts of corn and the PAR interception of the target canopy of corn, the method further comprises:
determining extinction coefficients of direct PAR corresponding to the sun-generated leaves of each sub-canopy at different moments based on average projection parameters of all leaves of the target corn canopy in the sunlight beam direction and sun altitude angles at the different moments in the target period;
the extinction coefficients of the direct PAR corresponding to the cationic leaf of each sub-canopy at different moments are determined by the following formula:
wherein ,for the extinction coefficient of the direct PAR corresponding to the sun leaf of each sub-canopy at time t in the target period, O av Is the average projection parameter; beta t -a solar altitude at time t within the target period;
determining PAR interception amounts of the target corn canopy at different moments in the target period based on direct PAR and scattered PAR of each sub canopy at different moments in the target period;
PAR interception amounts of the target corn canopy at different moments in the target period are determined by the following formula:
wherein Δidir n,t As the difference between the direct PAR of two adjacent sub-crowns at time t, deltaIdiff n,t The difference of the scattering PAR of two adjacent sub-crowns at the time t;
determining PAR interception amounts of the target corn canopy in the target period based on the PAR interception amounts of the target corn canopy at different moments in the target period;
the PAR interception amount of the target corn canopy in the target period is determined by the following formula:
8. a high light efficiency plant type characteristic determining system based on a canopy photosynthetic model, comprising:
the first determining module is used for determining average leaf inclination probability distribution, vertical distribution data of nitrogen content of corn canopy leaves and area index distribution functions of corn canopy leaves of a plurality of groups of corn with different plant types respectively based on the Beta distribution model;
the processing module is used for simulating various corn canopy structures and constructing a canopy photosynthetic model based on the average leaf inclination probability distribution, the corn canopy leaf nitrogen content vertical distribution data and the corn canopy leaf area index distribution function;
the second determining module is used for determining the light energy utilization efficiency corresponding to the various corn canopy structures respectively based on the canopy photosynthetic model;
And the third determining module is used for determining the canopy structure characteristics corresponding to the high-light-efficiency plant type based on the light energy utilization efficiency respectively corresponding to the various corn canopy structures.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the high light efficiency plant type characteristic determination method based on a canopy photosynthetic model as claimed in any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the high light efficiency plant type characteristic determination method based on a canopy photosynthetic model of any one of claims 1 to 7.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070250962A1 (en) * | 2003-08-18 | 2007-10-25 | Cerse, Inc. | Nucleotide Sequences and Polypeptides Encoded Thereby Useful for Increasing Plant Size and Increasing the Number and Size of Leaves |
| CN109115725A (en) * | 2018-06-14 | 2019-01-01 | 中国农业大学 | A kind of maize canopy LAI and chlorophyll content joint inversion method and equipment |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070250962A1 (en) * | 2003-08-18 | 2007-10-25 | Cerse, Inc. | Nucleotide Sequences and Polypeptides Encoded Thereby Useful for Increasing Plant Size and Increasing the Number and Size of Leaves |
| CN109115725A (en) * | 2018-06-14 | 2019-01-01 | 中国农业大学 | A kind of maize canopy LAI and chlorophyll content joint inversion method and equipment |
Non-Patent Citations (5)
| Title |
|---|
| 刘源;黄文江;任华忠;杨贵军;谢东辉;王纪华;: "夏玉米冠层光合有效辐射垂直分布模型", 农业工程学报, no. 09, 30 September 2011 (2011-09-30) * |
| 杨成勋;张旺锋;徐守振;随龙龙;梁福斌;董恒义;: "喷施化学打顶剂对棉花冠层结构及群体光合生产的影响", 中国农业科学, no. 09, 1 May 2016 (2016-05-01) * |
| 焦雪敏;张赫林;徐富宝;王岩;彭代亮;李存军;徐希燕;范海生;黄运新;: "青藏高原1982～2015年FPAR时空变化分析", 遥感技术与应用, no. 04, 20 August 2020 (2020-08-20) * |
| 王俊浩: "消光系数估算方法对玉米冠层光能利用模拟的影响", 中国农业气象, vol. 43, no. 10, 20 October 2022 (2022-10-20) * |
| 王靖雯: "基于遥感模型的作物产量差估算体系研究", 中国博士学位论文全文数据库, 31 December 2022 (2022-12-31) * |
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