CN104376142A - Rice plant type quantitative control method integrating crop virtual growth model - Google Patents

Abstract

Provided is a rice plant type quantitative design method integrating a crop virtual growth model. The crop virtual growth model is constructed, effective correlation and combination of the main plant physiological process, morphological structures and a light environmental model are achieved, and visual virtual growth of crop plants can be achieved; in the crop virtual growth model, morphologies of crops at different growth periods are set up through a structure model, a physiological model is used for realizing dynamic prediction of production, distribution and final yield of crop assimilates, and the light environmental model is used for computing crop canopy light radiation quantity and crop individual fractional interception of photosynthetic active radiation; the crop plant type structure is continuously changed through an optimization algorithm, so that simulation results of different adaption degrees are obtained, and the optimal crop plant type based on different targets can be obtained; the different adaption degrees are used as optimization results of the targets and set as morphological parameters of the virtual growth model, analog operation is carried out, horizontal and longitudinal comparison is carried out, and the optimal plant type data of crops are verified and determined.

Description

A kind of plant type of rice quantitative control methodin in conjunction with crop virtual growth model

Technical field

Content of the present invention relates to plant Dummy modeling, plant physioecology and Optimization Algorithm field, is a kind of crop plant type control method that can realize crop plant type Automatic Optimal, and the quantification that can apply to crop plant type controls and design.

Background technology

Traditional breeding method, through development for a long time, has defined a whole set of comparatively perfect breeding theory and technical system, and has achieved huge achievement in practice.Especially, since the eighties, the fast development of modern biotechnology and the combination with traditional breeding technology thereof, molecule aspect facilitates the development of traditional breeding method.But, be that means assist traditional breeding method excellent in design plant type current and unrealistic with Protocols in Molecular Biology.Therefore current high-yield breeding of crops research needs researchist constantly to carry out field cultivation test to obtain experience and data, and confirms as Main Means and kind, the product of evaluating incubation tie up to value in production.Therefore, the selection of crop ideotype and acquisition need the field test of longer cycle, especially when uncertain cultivation target, cause the plant type cultivated to lack specific aim, thus add breeding cost.

As one of the core and gordian technique of Digital Agriculture, the investigation and application of modeling of Crop Growth Virtual Plant at home and abroad all receives to be paid attention to widely.Virtual plant can be formed and growth course with the visual form representing plant of the form of three-dimensional picture on computers, and in conjunction with crop physiology and ecology process, realize regulation and control and the research of the Growth trends feature of crop particular physiological process and organ, thus dynamic regulation is carried out, for agriculture and forestry produce service to the space structure of crop, the Managed Solution of main production link.

Crop plant type optimization method based on virtual Crop, combine crop functional architecture model and genetic algorithm optimization thought, can realize optimizing the fixing quantity cultivating target, determine optimum Characters of Plant Type parameter, thus utilize the present computer technology to assist traditional breeding method means, realize the object economizing on resources, reduce costs and improve crop yield.

Summary of the invention

In order to assist the breeding method that traditional crop breeding and modern biological project combine further, the deficiency making up in general crop plant type design cycle that experimental period is long, specific aim is poor, manual operation is loaded down with trivial details etc., the invention provides a kind of plant type of rice quantitative control methodin, the method can make one's options to the ideotype of crop automatically and rapidly, thus realize being optimized plant type pointedly, in breeding process, play booster action.

The technical scheme that the technical matters that the present invention solves plant type optimization and control adopts is:

In conjunction with a plant type quantitative control methodin for crop virtual growth model, it is characterized in that, by virtual plant model and computer optimization algorithm organic combination, and comprise following steps:

Step 1, crop modeling builds

1.1 first, carry out comprehensive data acquisition: the form in experimental record target crop whole growth period is dynamic, comprise from organ to geometric shape characteristic trait data that are individual and whole crop groups, and crop modeling build needed for physiological data and environmental data (as the quantity of illumination etc.);

1.2 secondly, build crop growth model: based on computer graphics techniques, with plant growth principle for rule-based approach, adopt Lindenmayer system modeling language XL, Java programming language of expansion and RGG (the Relational Growth Grammar) technology of figure replacement can be carried out, to crop organ's growth and morphology process simulation, set up crop topology controlment (for paddy rice, namely comprising the structural model that the organ morphologies such as stem, leaf, fringe, seed build up); On the basis of structural model, add the simulation of plant physiology process, LEAFC3 Photosynthesis Model simulation crop is used to produce assimilation quotient by leaf photosynthesis, according to the distribution of growth model in conjunction with source library model simulation assimilation quotient, calculate the growth rate of this organ based on the assimilation quotient amount of beta growth function and each organ storehouse intensity size, acquisition and residing growth phase, thus realize the Developmental stage of whole plant at whole growth cycle; And then set up light model, the position of simulated solar light source and radiancy change; Virtual sunshine on high in be divided into direct light and scattered light, comprise their distribution in three dimensions, and after arrival crop canopies blocking by canopy space, thus realize the size of luminous flux in leaf photosynthesis; Final language function-structural modeling technology, in conjunction with Crop Structure model, physiological models and environmental model, take time as axis, explain by doing regularization based on the syntax rule of expansion Lindenmayer system modeling language and RGG to the formation of crop organ and growth, and iteration between implementation rule, replacement, the assimilate formation of combined with virtual crop and distribution, thus it is visual to realize virtual Crop Growth trends on the basis in conjunction with physiological function, topological structure and luminous environment condition, obtain the crop plant of specifying breeding time;

1.3 is last, and set up optimized algorithm model, and in crop modeling, add optimized algorithm model, the general step of optimized algorithm is as follows:

A) the general Characters of Plant Type of crop is determined;

B) crop of encoding is individual;

C) optimized algorithm fitness is chosen;

D) judgement of genetic manipulation and realization;

Step 2, specifically optimizes for the genetic algorithm of paddy rice

The object of 2.1 genetic manipulations is the plant type of paddy rice individuality, and individual plant type is the combination of the selected plant type characteristics of objects factor; When encoding to individuality, first by all characteristics of objects of individual plant type, namely all genes are in line in order, then carry out binary coding to each gene;

In plant type of rice is optimized, choose the Leaf inclination of Different Leaf-position Leaf Blades, plant height, maximum tillering number and tillering angle as plant type of rice characteristics of objects to be optimized, form plant type of rice combinations of factors; When encoding to individual plant type R, first all Output factors in this individual plant type are in line in order, note g _ibe i-th Output factors;

The individual plant type number contained by R of 2.2 hypothesis paddy rice is n _g, gene g _igene position length be designated as then the long L of the string of paddy rice individual UVR exposure is:

$L=\underset{i=1}{\overset{n_g}{\mathrm{\Σ}}}{n}_b^i$

Note $R=\{g_1,g_2,...,g_{n_g}\},$ $g_i=\{b_i^1,b_i^2,...,b_i^{n_b^i}\};$ G _ibe i-th Output factors, each Output factors g _ibe made up of binary-coded information position, each information bit is 0 or 1; for the value of this Output factors jth information bit; for the string of the binary code of this Output factors is long, its value is by the span [u of this Output factors _i, v _i] and value precision s jointly determine; The span of each gene can experimentally demand be carried out presetting, and calculates gene g respectively according to its span _igene position length

Need to arrange g ₀, g ₁, g ₂and g ₃span, and calculate four gene position length respectively according to span, paddy rice genes of individuals bit string length L is g ₀, g ₁, g ₂and g ₃gene position length sum;

2.3 are carrying out initialization population P ₀time, in order to improve the diversity of population, adopt the individuality in the method generation initialization population of completely random, population at individual number is n _c; Largest optimization algebraically t is set _max=100; Be L for a length _rindividual plant type binary code string, each information bit on individual plant type binary code string is { the upper random uniform design of 0,1}, so population scale is n _cinitialization of population at least need L _r× n _csecondary random value; The concrete steps of this process are:

Step1: from population first individual until n-th _ctill individuality, Step2 is performed for each individuality;

Step2: to L from the 1st information bit of selected individuality _rtill individual information bit, each information bit performs Step3;

Step3: on selected information position, the random number in stochastic generation one [0,1] space, and judge whether this random number is less than 0.5, if so, then assignment 0 in this information bit; Otherwise, assignment 1 in this information bit;

Provide initialization population process INITIALIZE (n below _c, L _r) false code:

2.4 in paddy rice model, decodes, obtain the parameter combinations of plant type of rice feature, be applied to the setting parameter of each individuality in optimizing process Rice Population the individual binary code string of the simulation being in selected growth period; Paddy rice individual chromosome carries plant type of rice characteristic parameter value information, and different characteristic parameters can obtain different individual morphology structures in dummy model; Choose rice canopy light interception amount, ultimate capacity respectively as the fitness value (in paddy rice model, individual morphosis has appreciable impact to the light interception amount of rice canopy and ultimate capacity) of optimizing process, be optimized computing;

And be module with fitness value, ascending order arrangement is carried out to the individuality in current optimization population;

If 2.5 current optimization algebraically t=0, the individuality selecting fitness value maximum is individual as elite;

2.6 elite's individualities and the competition of current population at individual, if current population optimum individual fitness value is not more than elite's ideal adaptation angle value, then substitutes the poorest individuality in current population, produce t+1 for rice population with this elite's individuality; Otherwise substitute elite's individuality with the optimum individual in current population, current population is that t+1 is for rice population simultaneously;

If 2.7 current optimization algebraically are greater than largest optimization algebraically, forward 2.12 to;

2.8 adopt roulette method to carry out genetic manipulation from t for selecting suitable individuality paddy rice individuality:

Step1: for the n-th generation individual plant type population individual R _j∈ P, calculating its fitness value is f (R _j);

Step2: for individual R _j, the ratio shared in colony's fitness value summation of the fitness value calculating this individuality is as its select probability p _s(R _j) be:

$p_s\left(R_j\right)=\frac{f\left(R_j\right)}{\underset{i=1}{\overset{n_c}{\mathrm{\Σ}}}f\left(R_i\right)},j=\mathrm{1,2},...,n_c$

Step3: for individual R _j, calculate this individuality and select probability sum individual before thereof the cumulative probability p as this individuality _a(R _j) be:

$p_a\left(R_j\right)=\frac{\underset{i=1}{\overset{j}{\mathrm{\Σ}}}f\left(R_j\right)}{\underset{i=1}{\overset{n_c}{\mathrm{\Σ}}}f\left(R_i\right)},j=\mathrm{1,2},...,n_c$

Step4: a random generation probable value in 0 to 1 scope.From colony first individuality, random chance is carried out order with the cumulative probability of individual in population and compares, select last individuality that individual cumulative probability is less than or equal to this random chance;

2.9 adopt phase dystopy Crossover Strategy, and the figure place different according to two father's genes of individuals place values determines whether interlace operation occurs, and is generally divided into following three steps:

Step1: calculate the figure place m that in the individual plant type of two fathers, information bit value is different;

Step2: a Stochastic choice m/2 position in dissimilarity information position the individual plant type of father;

Step3: from the position of first in selected position, produces a random chance, judges whether this random chance is greater than crossover probability p in 0 to 1 scope _c, if so, carry out the exchange of this positional information position in the individual plant type of father; Otherwise this process that circulates is until m/2 position completes.Finally obtain a pair new individual plant type;

Provide the false code of interlace operation CROSS (Chrosome c1, Chrosome c2) below:

2.10 adopt layering Mutation Strategy, mutation operation are divided into two levels (individual layer and information bit layer): first, carry out individual layer variation probability of happening and judge, select the individuality needing to morph from population; Secondly, selected individuality carries out information bit layer variation probability of happening and judges, from individuality, select the gene position needing to morph, in the enterprising row variation operation of gene position; Arrange individuality to morph Probability p _m, calculate paddy rice individual layer mutation probability respectively with Rice information position layer mutation probability

Step1: calculate the probability that in individual layer, individual plant type morphs for:

$p_m^1=1-\underset{i=1}{\overset{n_g}{\mathrm{\Π}}}{(1-p_m)}^{n_b^i}$

Step2: calculate the probability that on gene position layer, information bit morphs for:

$p_m^2=\frac{\underset{i=1}{\overset{n_g}{\mathrm{\Σ}}}{n}_b^i\×p_m}{1-\underset{i=1}{\overset{n_g}{\mathrm{\Π}}}{(1-p_m)}^{n_b^i}}$

Step3: from the individuality of first in population, produce a random chance in 0 to 1 scope, judge whether this random chance is less than or equal to individual plant type mutation probability if so, Step4 is proceeded to; Otherwise, this this process of process ring that circulates to the last body one by one;

Step4: from the information bit of first in this Output factors, produces a random chance, judges whether this random chance is less than or equal to information bit mutation probability in 0 to 1 scope if so, in the operation of this information bit enterprising row variation, 1 or replace with 0 by 1 is replaced with by 0; Otherwise this this process of process ring that circulates is until last information bit of this Output factors;

Provide mutation operation Mutation (Chrosome [] c, int nc, int ng, int below ) false code:

2.11 obtain interim population by genetic manipulation, forward 2.4 to;

In the current population of 2.12 output, the chromosome of optimum individual, obtains the plant type of rice characteristic parameter of this individuality;

Step 3, the comparison of optimum plant type and checking

Using light interception amount, seed number and ultimate capacity as in the optimizing process of fitness, have chosen the virtual individual in three different growth periods (jointing stage, pustulation period and maturity stage) as initial population; First, its optimum results is carried out longitudinal contrast respectively, draw the plant type of rice feature plant type parameter combinations based on growth period; Secondly, in the paddy rice virtual growth model built, use this parameter combinations, realize, on the basis of visual Simulation, predicting ultimate capacity in its growth course; , produce and allocation algorithm in conjunction with the assimilation quotient in paddy rice model meanwhile, aforementioned parameters combined, the ultimate capacity of prediction and be that the optimum results of fitness value carries out across comparison with output; Finally draw the plant type of rice optimum results comprising growth time and Spatial Dimension, thus realize the fixing quantity of optimum plant type.

In described step 1, based on form-physiology-environment interrelated of crop plant type structure, crop plant type structure can be changed by numerical value, and then realize the simulation to target fitness value.

In described step 2, crop plant type design problem is converted into Numerical Optimization, in computer optimization method, change crop plant type Structural Eigenvalue, and it is realized visual Simulation in dummy model, export as target carries out numeric ratio comparatively with the difference simulating plant.

In described step 2, corresponding method is used to carry out the relevant genetic manipulation of feature to optimizing colony: to carry out selection operation by roulette method; Interlace operation is realized by phase dystopy Crossover Strategy; Realize mutation operation with layering Mutation Strategy, thus increase the polymorphism optimizing colony.

Beneficial effect of the present invention

Language function of the present invention and structural modeling technique construction crop virtual growth model, and the genetic algorithm after improving is incorporated in crop plant type optimization problem as optimized algorithm, when virtual Crop simulate growth process, crop plant type eigenwert is changed by optimized algorithm, the crop plant type (the highest canopy light interception amount, assimilation quotient pond flux and ultimate capacity) that search is wherein optimum, and carry out simplation verification.

Apply the plant type quantitative control methodin in conjunction with crop growth model described in the invention, Quantitative design and the control of the common crop plant types such as paddy rice can be applied to, thus the Automatic Optimal of the crop plant type realized.The crop characteristic morphology of acquisition after optimizing, can cultivate target for the breeding of Plants type provide quantification targetedly, for crop breeding provides auxiliary and reference, and final raising crop breeding and production management efficiency.

Accompanying drawing explanation

Algorithm flow chart is grown by Fig. 1 rice plant growth of the present invention

The modules of Fig. 2 crop virtual growth of the present invention model and plant type optimization method module relation diagram

Fig. 3 the present invention is applied to the process flow diagram that plant type of rice is optimized

The individual UVR exposure string schematic diagram of optimization object in Fig. 4 plant type of rice optimizing process of the present invention

Embodiment

For paddy rice, the present invention builds paddy rice virtual growth model, uses Revised genetic algorithum to realize the plant type optimization of paddy rice.By reference to the accompanying drawings, this method realize plant type optimize embodiment as follows:

In conjunction with a plant type quantitative control methodin for crop virtual growth model, it is characterized in that, by virtual plant model and computer optimization algorithm organic combination, and comprise following steps:

Step 1, crop modeling builds

1.1 first, carry out comprehensive data acquisition: the form in experimental record target crop whole growth period is dynamic, comprise from organ to geometric shape characteristic trait data that are individual and whole crop groups, and crop modeling build needed for physiological data and environmental data (as the quantity of illumination etc.);

1.2 secondly, build crop growth model: based on computer graphics techniques, with plant growth principle for rule-based approach, adopt Lindenmayer system modeling language XL, Java programming language of expansion and RGG (the Relational Growth Grammar) technology of figure replacement can be carried out, to crop organ's growth and morphology process simulation, set up crop topology controlment (for paddy rice, namely comprising the structural model that the organ morphologies such as stem, leaf, fringe, seed build up); On the basis of structural model, add the simulation of plant physiology process, LEAFC3 Photosynthesis Model simulation crop is used to produce assimilation quotient by leaf photosynthesis, according to the distribution of growth model in conjunction with source library model simulation assimilation quotient, calculate the growth rate of this organ based on the assimilation quotient amount of beta growth function and each organ storehouse intensity size, acquisition and residing growth phase, thus realize the Developmental stage of whole plant at whole growth cycle; And then set up light model, the position of simulated solar light source and radiancy change; Virtual sunshine on high in be divided into direct light and scattered light, comprise their distribution in three dimensions, and after arrival crop canopies blocking by canopy space, thus realize the size of luminous flux in leaf photosynthesis; Final language function-structural modeling technology, in conjunction with Crop Structure model, physiological models and environmental model, take time as axis, explain by doing regularization based on the syntax rule of expansion Lindenmayer system modeling language and RGG to the formation of crop organ and growth, and iteration between implementation rule, replacement, the assimilate formation of combined with virtual crop and distribution, thus it is visual to realize virtual Crop Growth trends on the basis in conjunction with physiological function, topological structure and luminous environment condition, obtain the crop plant of specifying breeding time;

1.3 is last, and set up optimized algorithm model, and in crop modeling, add optimized algorithm model, the general step of optimized algorithm is as follows:

A) the general Characters of Plant Type of crop is determined;

B) crop of encoding is individual;

C) optimized algorithm fitness is chosen;

D) judgement of genetic manipulation and realization;

Step 2, specifically optimizes for the genetic algorithm of paddy rice

The object of 2.1 genetic manipulations is the plant type of paddy rice individuality, and individual plant type is the combination of the selected plant type characteristics of objects factor; When encoding to individuality, first by all characteristics of objects of individual plant type, namely all genes are in line in order, then carry out binary coding to each gene;

In plant type of rice is optimized, choose the Leaf inclination of Different Leaf-position Leaf Blades, plant height, maximum tillering number and tillering angle as plant type of rice characteristics of objects to be optimized, form plant type of rice combinations of factors; When encoding to individual plant type R, first all Output factors in this individual plant type are in line in order, note g _ibe i-th Output factors;

The individual plant type number contained by R of 2.2 hypothesis paddy rice is n _g, gene g _igene position length be designated as then the long L of the string of paddy rice individual UVR exposure is:

$L=\underset{i=1}{\overset{n_g}{\mathrm{\Σ}}}{n}_b^i$

Note $R=\{g_1,g_2,...,g_{n_g}\},$ $g_i=\{b_i^1,b_i^2,...,b_i^{n_b^i}\};$ G _ibe i-th Output factors, each Output factors g _ibe made up of binary-coded information position, each information bit is 0 or 1; for the value of this Output factors jth information bit; for the string of the binary code of this Output factors is long, its value is by the span [u of this Output factors _i, v _i] and value precision s jointly determine; The span of each gene can experimentally demand be carried out presetting, and calculates gene g respectively according to its span _igene position length

Need to arrange g ₀, g ₁, g ₂and g ₃span, and calculate four gene position length respectively according to span, paddy rice genes of individuals bit string length L is g ₀, g ₁, g ₂and g ₃gene position length sum;

2.3 are carrying out initialization population P ₀time, in order to improve the diversity of population, adopt the individuality in the method generation initialization population of completely random, population at individual number is n _c; Largest optimization algebraically t is set _max=100; Be L for a length _rindividual plant type binary code string, each information bit on individual plant type binary code string is { the upper random uniform design of 0,1}, so population scale is n _cinitialization of population at least need L _r× n _csecondary random value; The concrete steps of this process are:

Step1: from population first individual until n-th _ctill individuality, Step2 is performed for each individuality;

Step2: to L from the 1st information bit of selected individuality _rtill individual information bit, each information bit performs Step3;

Step3: on selected information position, the random number in stochastic generation one [0,1] space, and judge whether this random number is less than 0.5, if so, then assignment 0 in this information bit; Otherwise, assignment 1 in this information bit;

Provide initialization population process INITIALIZE (n below _c, L _r) false code:

2.4 in paddy rice model, decodes, obtain the parameter combinations of plant type of rice feature, be applied to the setting parameter of each individuality in optimizing process Rice Population the individual binary code string of the simulation being in selected growth period; Paddy rice individual chromosome carries plant type of rice characteristic parameter value information, and different characteristic parameters can obtain different individual morphology structures in dummy model; Choose rice canopy light interception amount, ultimate capacity respectively as the fitness value (in paddy rice model, individual morphosis has appreciable impact to the light interception amount of rice canopy and ultimate capacity) of optimizing process, be optimized computing;

And be module with fitness value, ascending order arrangement is carried out to the individuality in current optimization population;

If 2.5 current optimization algebraically t=0, the individuality selecting fitness value maximum is individual as elite;

2.6 elite's individualities and the competition of current population at individual, if current population optimum individual fitness value is not more than elite's ideal adaptation angle value, then substitutes the poorest individuality in current population, produce t+1 for rice population with this elite's individuality; Otherwise substitute elite's individuality with the optimum individual in current population, current population is that t+1 is for rice population simultaneously;

If 2.7 current optimization algebraically are greater than largest optimization algebraically, forward 2.12 to;

2.8 adopt roulette method to carry out genetic manipulation from t for selecting suitable individuality paddy rice individuality:

Step1: for the n-th generation individual plant type population individual R _j∈ P, calculating its fitness value is f (R _j);

Step2: for individual R _j, the ratio shared in colony's fitness value summation of the fitness value calculating this individuality is as its select probability p _s(R _j) be:

$p_s\left(R_j\right)=\frac{f\left(R_j\right)}{\underset{i=1}{\overset{n_c}{\mathrm{\Σ}}}f\left(R_i\right)},j=\mathrm{1,2},...,n_c$

Step3: for individual R _j, calculate this individuality and select probability sum individual before thereof the cumulative probability p as this individuality _a(R _j) be:

$p_a\left(R_j\right)=\frac{\underset{i=1}{\overset{j}{\mathrm{\Σ}}}f\left(R_j\right)}{\underset{i=1}{\overset{n_c}{\mathrm{\Σ}}}f\left(R_i\right)},j=\mathrm{1,2},...,n_c$

Step4: a random generation probable value in 0 to 1 scope.From colony first individuality, random chance is carried out order with the cumulative probability of individual in population and compares, select last individuality that individual cumulative probability is less than or equal to this random chance;

2.9 adopt phase dystopy Crossover Strategy, and the figure place different according to two father's genes of individuals place values determines whether interlace operation occurs, and is generally divided into following three steps:

Step1: calculate the figure place m that in the individual plant type of two fathers, information bit value is different;

Step2: a Stochastic choice m/2 position in dissimilarity information position the individual plant type of father;

Step3: from the position of first in selected position, produces a random chance, judges whether this random chance is greater than crossover probability p in 0 to 1 scope _c, if so, carry out the exchange of this positional information position in the individual plant type of father; Otherwise this process that circulates is until m/2 position completes.Finally obtain a pair new individual plant type;

Provide the false code of interlace operation CROSS (Chrosome c1, Chrosome c2) below:

2.10 adopt layering Mutation Strategy, mutation operation are divided into two levels (individual layer and information bit layer): first, carry out individual layer variation probability of happening and judge, select the individuality needing to morph from population; Secondly, selected individuality carries out information bit layer variation probability of happening and judges, from individuality, select the gene position needing to morph, in the enterprising row variation operation of gene position; Arrange individuality to morph Probability p _m, calculate paddy rice individual layer mutation probability respectively with Rice information position layer mutation probability

Step1: calculate the probability that in individual layer, individual plant type morphs for:

$p_m^1=1-\underset{i=1}{\overset{n_g}{\mathrm{\Π}}}{(1-p_m)}^{n_b^i}$

Step2: calculate the probability that on gene position layer, information bit morphs for:

$p_m^2=\frac{\underset{i=1}{\overset{n_g}{\mathrm{\Σ}}}{n}_b^i\×p_m}{1-\underset{i=1}{\overset{n_g}{\mathrm{\Π}}}{(1-p_m)}^{n_b^i}}$

Step3: from the individuality of first in population, produce a random chance in 0 to 1 scope, judge whether this random chance is less than or equal to individual plant type mutation probability if so, Step4 is proceeded to; Otherwise, this this process of process ring that circulates to the last body one by one;

Step4: from the information bit of first in this Output factors, produces a random chance, judges whether this random chance is less than or equal to information bit mutation probability in 0 to 1 scope if so, in the operation of this information bit enterprising row variation, 1 or replace with 0 by 1 is replaced with by 0; Otherwise this this process of process ring that circulates is until last information bit of this Output factors;

Provide mutation operation Mutation (Chrosome [] c, int nc, int ng, int below ) false code:

2.11 obtain interim population by genetic manipulation, forward 2.4 to;

In the current population of 2.12 output, the chromosome of optimum individual, obtains the plant type of rice characteristic parameter of this individuality;

Step 3, the comparison of optimum plant type and checking

Using light interception amount, seed number and ultimate capacity as in the optimizing process of fitness, have chosen the virtual individual in three different growth periods (jointing stage, pustulation period and maturity stage) as initial population; First, its optimum results is carried out longitudinal contrast respectively, draw the plant type of rice feature plant type parameter combinations based on growth period; Secondly, in the paddy rice virtual growth model built, use this parameter combinations, realize, on the basis of visual Simulation, predicting ultimate capacity in its growth course; , produce and allocation algorithm in conjunction with the assimilation quotient in paddy rice model meanwhile, aforementioned parameters combined, the ultimate capacity of prediction and be that the optimum results of fitness value carries out across comparison with output; Finally draw the plant type of rice optimum results comprising growth time and Spatial Dimension, thus realize the fixing quantity of optimum plant type.

In described step 1, based on form-physiology-environment interrelated of crop plant type structure, crop plant type structure can be changed by numerical value, and then realize the simulation to target fitness value.

In described step 2, crop plant type design problem is converted into Numerical Optimization, in computer optimization method, change crop plant type Structural Eigenvalue, and it is realized visual Simulation in dummy model, export as target carries out numeric ratio comparatively with the difference simulating plant.

In described step 2, corresponding method is used to carry out the relevant genetic manipulation of feature to optimizing colony: to carry out selection operation by roulette method; Interlace operation is realized by phase dystopy Crossover Strategy; Realize mutation operation with layering Mutation Strategy, thus increase the polymorphism optimizing colony.

Claims (5)

1. in conjunction with a plant type of rice quantitative control methodin for crop virtual growth model, it is characterized in that, by virtual plant model and computer optimization algorithm organic combination, and comprise following steps:

Step 1, crop modeling builds

1.1 first, carry out comprehensive data acquisition: the form in experimental record target crop whole growth period is dynamic, comprise from organ to geometric shape characteristic trait data that are individual and whole crop groups, and crop modeling build needed for physiological data and environmental data (as the quantity of illumination etc.);

1.2 secondly, build crop growth model: based on computer graphics techniques, with plant growth principle for rule-based approach, adopt Lindenmayer system modeling language XL, Java programming language of expansion and RGG (the Relational Growth Grammar) technology of figure replacement can be carried out, to crop organ's growth and morphology process simulation, set up crop topology controlment (for paddy rice, namely comprising the structural model that the organ morphologies such as stem, leaf, fringe, seed build up); On the basis of structural model, add the simulation of plant physiology process, LEAFC3 Photosynthesis Model simulation crop is used to produce assimilation quotient by leaf photosynthesis, according to the distribution of growth model in conjunction with source library model simulation assimilation quotient, calculate the growth rate of this organ based on the assimilation quotient amount of beta growth function and each organ storehouse intensity size, acquisition and residing growth phase, thus realize the Developmental stage of whole plant at whole growth cycle; And then set up light model, the position of simulated solar light source and radiancy change; Virtual sunshine on high in be divided into direct light and scattered light, comprise their distribution in three dimensions, and after arrival crop canopies blocking by canopy space, thus realize the size of luminous flux in leaf photosynthesis; Final language function-structural modeling technology, in conjunction with Crop Structure model, physiological models and environmental model, take time as axis, explain by doing regularization based on the syntax rule of expansion Lindenmayer system modeling language and RGG to the formation of crop organ and growth, and iteration between implementation rule, replacement, the assimilate formation of combined with virtual crop and distribution, thus it is visual to realize virtual Crop Growth trends on the basis in conjunction with physiological function, topological structure and luminous environment condition, obtain the crop plant of specifying breeding time;

1.3 is last, and set up optimized algorithm model, and in crop modeling, add optimized algorithm model, the general step of optimized algorithm is as follows:

A) the general Characters of Plant Type of crop is determined;

B) crop of encoding is individual;

C) optimized algorithm fitness is chosen;

D) judgement of genetic manipulation and realization;

Step 2, specifically optimizes for the genetic algorithm of paddy rice

The object of 2.1 genetic manipulations is the plant type of paddy rice individuality, and individual plant type is the combination of the selected plant type characteristics of objects factor; When encoding to individuality, first by all characteristics of objects of individual plant type, namely all genes are in line in order, then carry out binary coding to each gene;

In plant type of rice is optimized, choose the Leaf inclination of Different Leaf-position Leaf Blades, plant height, maximum tillering number and tillering angle as plant type of rice characteristics of objects to be optimized, form plant type of rice combinations of factors; When encoding to individual plant type R, first all Output factors in this individual plant type are in line in order, note g _ibe i-th Output factors;

The individual plant type number contained by R of 2.2 hypothesis paddy rice is n _g, gene g _igene position length be designated as then the long L of the string of paddy rice individual UVR exposure is:

$L=\underset{i=1}{\overset{n_g}{\mathrm{\Σ}}}{n}_b^i$

Note $R=\{g_1,g_2,...,g_{n_g}\},$ $g_i=\{b_i^1,b_i^2,...,b_i^{n_b^i}\};$ G _ibe i-th Output factors, each Output factors g _ibe made up of binary-coded information position, each information bit is 0 or 1; for the value of this Output factors jth information bit; for the string of the binary code of this Output factors is long, its value is by the span [u of this Output factors _i, v _i] and value precision s jointly determine; The span of each gene can experimentally demand be carried out presetting, and calculates gene g respectively according to its span _igene position length

Need to arrange g ₀, g ₁, g ₂and g ₃span, and calculate four gene position length respectively according to span, paddy rice genes of individuals bit string length L is g ₀, g ₁, g ₂and g ₃gene position length sum;

2.3 are carrying out initialization population P ₀time, in order to improve the diversity of population, adopt the individuality in the method generation initialization population of completely random, population at individual number is n _c; Largest optimization algebraically t is set _max=100; Be L for a length _rindividual plant type binary code string, each information bit on individual plant type binary code string is { the upper random uniform design of 0,1}, so population scale is n _cinitialization of population at least need L _r× n _csecondary random value; The concrete steps of this process are:

Step1: from population first individual until n-th _ctill individuality, Step2 is performed for each individuality;

Step2: to L from the 1st information bit of selected individuality _rtill individual information bit, each information bit performs Step3;

Step3: on selected information position, the random number in stochastic generation one [0,1] space, and judge whether this random number is less than 0.5, if so, then assignment 0 in this information bit; Otherwise, assignment 1 in this information bit;

Provide initialization population process INITIALIZE (n below _c, L _r) false code:

2.4 in paddy rice model, decodes, obtain the parameter combinations of plant type of rice feature, be applied to the setting parameter of each individuality in optimizing process Rice Population the individual binary code string of the simulation being in selected growth period; Paddy rice individual chromosome carries plant type of rice characteristic parameter value information, and different characteristic parameters can obtain different individual morphology structures in dummy model; Choose rice canopy light interception amount, ultimate capacity respectively as the fitness value (in paddy rice model, individual morphosis has appreciable impact to the light interception amount of rice canopy and ultimate capacity) of optimizing process, be optimized computing;

And be module with fitness value, ascending order arrangement is carried out to the individuality in current optimization population;

If 2.5 current optimization algebraically t=0, the individuality selecting fitness value maximum is individual as elite;

2.6 elite's individualities and the competition of current population at individual, if current population optimum individual fitness value is not more than elite's ideal adaptation angle value, then substitutes the poorest individuality in current population, produce t+1 for rice population with this elite's individuality; Otherwise substitute elite's individuality with the optimum individual in current population, current population is that t+1 is for rice population simultaneously;

If 2.7 current optimization algebraically are greater than largest optimization algebraically, forward 2.12 to;

2.8 adopt roulette method to carry out genetic manipulation from t for selecting suitable individuality paddy rice individuality:

Step1: for the n-th generation individual plant type population individual R _j∈ P, calculating its fitness value is f (R _j);

Step2: for individual R _j, the ratio shared in colony's fitness value summation of the fitness value calculating this individuality is as its select probability p _s(R _j) be:

$p_s\left(R_j\right)=\frac{f\left(R_j\right)}{\underset{i=1}{\overset{n_c}{\mathrm{\Σ}}}f\left(R_i\right)},j=\mathrm{1,2},...,n_c$

Step3: for individual R _j, calculate this individuality and select probability sum individual before thereof the cumulative probability p as this individuality _a(R _j) be:

$p_a\left(R_j\right)=\frac{\underset{i=1}{\overset{j}{\mathrm{\Σ}}}f\left(R_j\right)}{\underset{i=1}{\overset{n_c}{\mathrm{\Σ}}}f\left(R_i\right)},j=\mathrm{1,2},...,n_c$

Step4: a random generation probable value in 0 to 1 scope.From colony first individuality, random chance is carried out order with the cumulative probability of individual in population and compares, select last individuality that individual cumulative probability is less than or equal to this random chance;

2.9 adopt phase dystopy Crossover Strategy, and the figure place different according to two father's genes of individuals place values determines whether interlace operation occurs, and is generally divided into following three steps:

Step1: calculate the figure place m that in the individual plant type of two fathers, information bit value is different;

Step2: a Stochastic choice m/2 position in dissimilarity information position the individual plant type of father;

Step3: from the position of first in selected position, produces a random chance, judges whether this random chance is greater than crossover probability p in 0 to 1 scope _c, if so, carry out the exchange of this positional information position in the individual plant type of father; Otherwise this process that circulates is until m/2 position completes.Finally obtain a pair new individual plant type;

Provide the false code of interlace operation CROSS (Chrosome c1, Chrosome c2) below:

2.10 adopt layering Mutation Strategy, mutation operation are divided into two levels (individual layer and information bit layer): first, carry out individual layer variation probability of happening and judge, select the individuality needing to morph from population; Secondly, selected individuality carries out information bit layer variation probability of happening and judges, from individuality, select the gene position needing to morph, in the enterprising row variation operation of gene position; Arrange individuality to morph Probability p _m, calculate paddy rice individual layer mutation probability respectively with Rice information position layer mutation probability

Step1: calculate the probability that in individual layer, individual plant type morphs for:

$p_m^1=1-\underset{i=1}{\overset{n_g}{\mathrm{\Π}}}{(1-p_m)}^{n_b^i}$

Step2: calculate the probability that on gene position layer, information bit morphs for:

$p_m^2=\frac{\underset{i=1}{\overset{n_g}{\mathrm{\Σ}}}{n}_b^i\×p_m}{1-\underset{i=1}{\overset{n_g}{\mathrm{\Π}}}{(1-p_m)}^{n_b^i}}$

Step3: from the individuality of first in population, produce a random chance in 0 to 1 scope, judge whether this random chance is less than or equal to individual plant type mutation probability if so, Step4 is proceeded to; Otherwise, this this process of process ring that circulates to the last body one by one;

Step4: from the information bit of first in this Output factors, produces a random chance, judges whether this random chance is less than or equal to information bit mutation probability in 0 to 1 scope if so, in the operation of this information bit enterprising row variation, 1 or replace with 0 by 1 is replaced with by 0; Otherwise this this process of process ring that circulates is until last information bit of this Output factors;

Provide mutation operation Mutation (Chrosome [] c, int nc, int ng, int below ) false code:

2.11 obtain interim population by genetic manipulation, forward 2.4 to;

In the current population of 2.12 output, the chromosome of optimum individual, obtains the plant type of rice characteristic parameter of this individuality;

Step 3, the comparison of optimum plant type and checking

Using light interception amount, seed number and ultimate capacity as in the optimizing process of fitness, have chosen the virtual individual in three different growth periods (jointing stage, pustulation period and maturity stage) as initial population; First, its optimum results is carried out longitudinal contrast respectively, draw the plant type of rice feature plant type parameter combinations based on growth period; Secondly, in the paddy rice virtual growth model built, use this parameter combinations, realize, on the basis of visual Simulation, predicting ultimate capacity in its growth course; , produce and allocation algorithm in conjunction with the assimilation quotient in paddy rice model meanwhile, aforementioned parameters combined, the ultimate capacity of prediction and be that the optimum results of fitness value carries out across comparison with output; Finally draw the plant type of rice optimum results comprising growth time and Spatial Dimension, thus realize the fixing quantity of optimum plant type.

2. as claimed in claim 1 in conjunction with the plant type of rice Quantitative design method of crop dummy model, it is characterized in that: in described step 1, based on form-physiology-environment interrelated of crop plant type structure, crop plant type structure can be changed by numerical value, and then realize the simulation to target fitness value.

3. as claimed in claim 1 in conjunction with the plant type of rice Quantitative design method of crop dummy model, it is characterized in that: in described step 2, crop plant type design problem is converted into Numerical Optimization, crop plant type Structural Eigenvalue is changed in computer optimization method, and it is realized visual Simulation in dummy model, export as target carries out numeric ratio comparatively with the difference simulating plant.

4. as claimed in claim 1 in conjunction with the plant type of rice Quantitative design method of crop dummy model, it is characterized in that: in described step 2, using corresponding method to carry out the relevant genetic manipulation of feature to optimizing colony: to carry out selection operation by roulette method; Interlace operation is realized by phase dystopy Crossover Strategy; Realize mutation operation with layering Mutation Strategy, thus increase the polymorphism optimizing colony.

5. as claimed in claim 1 in conjunction with the plant type Quantitative design method of crop dummy model, it is characterized in that: in described step 3, first use optimized algorithm with the optimum Characters of Plant Type parameter of different target fitness value calculation different times, space and different simulated target; Then these parameter combinations returned the parameter value being set as dummy model and re-start visual Simulation; Finally analog result is contrasted, obtain the optimum results comprising time, Spatial Dimension.