CN110321627B - Photosynthetic rate prediction method for photosynthetic potential of fused leaves - Google Patents
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Abstract
A photosynthetic rate prediction method fusing leaf photosynthetic potential comprises the steps of designing plant photosynthesis tests with the same parameters except for illumination grades, generating difference among plants to be subjected to different illumination treatments, and randomly selecting plants with different physiological states as experiment samples; measuring the temperature and CO of the different blades 2 Net photosynthetic rate under nested conditions of concentration and illumination intensity, and recording dark fluorescence parameters of leaves as sample data; normalization processing is carried out on different dimensions of the sample data, so that the data of all dimensions are in one order of magnitude, and a test set and a training set are divided; establishing a photosynthetic rate prediction model fusing the photosynthetic potential of the leaves by utilizing a regression type support vector machine algorithm; the method utilizes the prediction model to predict the photosynthetic rate of the photosynthetic potential of the fused leaves, and provides an important theoretical basis and technical realization for the accurate regulation and control of facility environment.
Description
Technical Field
The invention belongs to the technical field of intelligent agriculture, relates to photosynthetic rate prediction, and particularly relates to a photosynthetic rate prediction method fusing photosynthetic potential of leaves.
Background
Photosynthesis is the key to plant growth, provides an energy source for plant carbon accumulation, and the yield of crops is closely related to the photosynthetic accumulation of the crops. The photosynthesis rate of plants is influenced by the environment and the state of the plants, and the external environment including temperature, atmospheric carbon dioxide concentration, photosynthetic light quantum flux density and the like has important influence on the photosynthesis of crops. Meanwhile, the photosynthetic potential of the plant leaves at different periods and different leaf positions has great difference, and the reason for causing different photosynthetic rates is that the inherent photosynthetic potential of the plant leaves is different. The photosynthetic potential of the crops is fused, the external environmental factors of the crops are coupled, a photosynthetic rate model based on the coupling of the photosynthetic potential of the crops and the facility environment multifactor is established, and the photosynthetic rate of the crops can be reflected more accurately.
Numerous scholars have conducted extensive studies on the plant photosynthetic rate model, and k.w.brown proposed atmospheric CO 2 The illumination intensity-photosynthetic rate model under the concentration predicts the CO fixed by the leaves 2 Without ever considering CO 2 Concentration, etc. on the rate of photosynthesis. Jingz et al establish photosynthetic rate response functions of the effects of various environmental factors on photosynthetic rate, reflect the effects of various factors on photosynthetic rate to a certain extent, but do not consider the coupling relationship of multi-environmental factors on plant photosynthetic rate. Zhang Haihui and the like establish a photosynthetic rate model fusing leaf positions in consideration of the photosynthetic capacity difference of plant leaves at different leaf positions, further improve the accuracy of the photosynthetic rate model, but do not consider the photosynthetic capacity difference of leaves at the same leaf positions in different states.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a photosynthetic rate prediction method, a photosynthetic experiment under nesting of facility environmental factors of different fluorescent leaves is designed, the photosynthetic potential of the leaves of a plant is fused, a photosynthetic rate prediction model is established based on a regression type support vector machine optimized by an improved genetic algorithm after data are preprocessed, accurate photosynthetic rate prediction can be realized by utilizing the model, a uniform model can be provided for the photosynthetic rates of the leaves in different growth states, and a foundation is established for regulation and control of the facility agricultural environmental factors.
In order to achieve the purpose, the invention adopts the technical scheme that:
a photosynthetic rate prediction method for photosynthetic potential of fused leaves comprises the following steps:
s1, designing plant photosynthesis tests with the same parameters except for illumination grades, wherein the physiological states of leaves of plants to be treated by different illuminations are different, and randomly selecting plants with obvious differences in the physiological states of the leaves as experiment samples;
s2, measuring the temperature and CO of the different blades 2 The net photosynthetic rate under the nesting condition of concentration and illumination intensity, and the dark fluorescence parameters of the leaves are recorded as sample data;
s3, normalization processing is carried out on different dimensions of the sample data, so that the data of all the dimensions are in the same order of magnitude, and a test set and a training set are divided;
s4, establishing a photosynthetic rate prediction model fusing the photosynthetic potential of the leaves by utilizing a regression type support vector machine algorithm;
and S5, predicting the photosynthetic rate of the photosynthetic potential of the fused leaves by using the prediction model.
In the step S1, the illumination level is 1-6, and other parameters are day/night light cycle, day/night temperature, day/night relative humidity and CO 2 And (4) concentration.
In step S3, the normalized interval is [0,1], and the formula is:
wherein x is the original data, x min Is the minimum value, x, of all the raw data max Is the maximum of all the raw data.
In the step S3, 80% of sample data is randomly divided into a training set, and the rest 20% of sample data is divided into a test set, wherein the input characteristic of the ith sample is x i ＝(x i (1) ,x i (2) ,x i (3) ,x i (4) ,x i (5) ) The output label is y i ，x i (1) ,x i (2) ,x i (3) ,x i (4) ,x i (5) The light quantum flux density, the carbon dioxide concentration, the temperature, the minimum fluorescence parameter Fo and the maximum fluorescence parameter Fm, y of the ith sample i The photosynthetic rate of the ith sample.
In step S4, an improved genetic algorithm is used to solve the regularization parameter c and the kernel function parameter g that are optimal for the model, the index range of c is set to [0.01,50], the number of genes is 6, the index range of g is set to [0.01,5], the number of genes is 5, the coding mode is set to binary coding, and the coding formula is:
the decoding formula is:
wherein b is the coded binary string, m is the number of characters of the binary string taken by the chromosome, a is the decimal number to be coded, a min For coding the space minimum decimal number, a max Is the maximum decimal number of the coding space;
using decoded c and g as parameters, training a regression type support vector machine by using training set data, calculating a decision coefficient of test set data, using the decision coefficient as chromosome fitness, carrying out population elimination and selection by using a roulette wheel, simulating manual intervention in a species selection process in formed offspring, replacing an offspring lowest fitness individual by a parent maximum fitness individual, forming new offspring by crossing, variation and selection operations of the offspring, and carrying out iterative evolution to finally converge to optimal solution parameters c and g. The coefficient of variation is selected to be 0.2, the cross coefficient is selected to be 0.8, and the optimal solution parameters c and g can be obtained by iterative evolution for 50 generations.
In the step S4, the modeling process is as follows:
s4.1, reading in modeling data and carrying out normalization pretreatment;
s4.2, selecting 80% of sample data as a training set, and selecting modeling parameters including a regularization parameter c, a kernel function and a kernel function parameter g;
s4.3, solving regression hyperplane parameters according to the KKT condition to obtain a photosynthetic rate model;
step S4.4, substituting the test set data into the modelObtaining fitting data, calculating fitting errors, if the fitting errors do not meet the requirements, repeating the step S4.2 to the step S4.4 until the fitting errors meet the requirements, and obtaining a final model f (x) = w T And x + v, wherein w is an input coefficient, and v is the intercept of the model in each axis.
In model training, the model introduces a relaxation variable epsilon i Forming the functional soft interval, the regression support vector machine problem can be transformed into:
the target is as follows:
constraint of f (x) i )-y i ≤ε+ε i ,ε i ≥0,i＝1,2,…,M
Wherein, y i The photosynthetic rate of the ith sample is epsilon, an insensitive loss function is determined by the support vector; epsilon i And
is a relaxation variable; m is the total number of model training samples, for a nonlinear problem, an input vector is mapped to a higher-dimensional space through a kernel function, the nonlinear problem is converted into a linear problem to be solved, a regression hyperplane is constructed, and finally the regression hyperplane is mapped back to an original space and converted into a nonlinear hypersurface.
And (3) selecting a Gauss kernel function for dimension transformation, wherein the Gauss kernel function is in the form of:
wherein X is a sample to be transformed, X p The center of the p-th kernel function is defined, g is a kernel function parameter, and finally, the model is learned by combining kernel skills as follows:
wherein,
α i is the lagrange multiplier in the solution process.
Compared with the prior art, the invention has the beneficial effects that:
1) A photosynthetic rate prediction model based on plant photosynthetic potential is established, plant dark fluorescence parameters Fo and Fm are used as input factors when the model is established, and the phenomenon that the traditional photosynthetic rate prediction model cannot accurately predict the photosynthetic rates of leaves in different states is effectively avoided.
2) The regression support vector machine parameters with the highest sample data fitting accuracy can be found in a short time by using the genetic algorithm, and the genetic algorithm after the genetic mode is improved can effectively avoid the situation that the optimal individual is lost in the evolution process, so that the optimization convergence speed is higher.
3) A regression type support vector machine is used for establishing a photosynthetic rate prediction model, the model establishment is determined by a small amount of support vectors, the modeling time is accelerated, and the generalization capability and the fitting precision are effectively improved by the soft interval and kernel function design. After the model is established, 5-fold cross validation is used, and the generalization capability of the validation model to unknown data is strong.
Drawings
FIG. 1 is a flow chart of the photosynthetic rate prediction model building with the leaf photosynthetic potential of the present invention fused.
FIG. 2 is a flow chart of the improved genetic algorithm of the present invention.
FIG. 3 is a flowchart of the regression-type SVM algorithm of the present invention.
FIG. 4 is a photosynthetic curve of leaves with different fluorescence values.
FIG. 5 is a photosynthetic rate error surface based on different model parameters according to the present invention.
FIG. 6 is a genetic algorithm evolution diagram of the present invention.
FIG. 7 is a diagram illustrating the correlation between measured values and fitting values of the light combination rate in the model verification according to the present invention.
FIG. 8 is a distribution of the calculated data and the raw data for the fluorescence-free parametric model.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the drawings and examples.
The invention relates to a photosynthetic rate prediction method for fused leaf photosynthetic potential, which evaluates the photosynthetic potential of plants by using chlorophyll dark fluorescence parameters as an index for measuring the leaf photosynthetic capacity. The different dark fluorescence parameters have different meanings, the maximum fluorescence Fm parameter can reflect the electron transfer condition of the PSII reaction center, the initial fluorescence Fo parameter can represent the light energy radiation of the PSII reaction center which does not participate in the photochemical reaction, and the initial fluorescence Fo parameter and the PSII reaction center can reflect the activity of the PSII reaction center and represent the photosynthetic potential of the plant leaf.
The invention adopts a regression type support vector machine algorithm to establish a photosynthetic rate model fusing the photosynthetic potential of the plant, and obtains the optimal support vector machine parameters by an improved genetic algorithm. And (4) taking 80% of the experimental samples as training sets to train the models, taking the remaining 20% of samples as test sets to test the generalization ability of the models, and performing model evaluation.
As shown in fig. 1, taking eggplant as an example, the process of the prediction method of the present invention is as follows:
1 materials and methods
1.1 test materials and methods
The test is carried out in agricultural Internet of things emphasis laboratories (34 '07', 39 'north, 107' 59 'east, 50' east, 648 meters above sea level) at agricultural rural areas of agricultural forestry science and technology university at northwest of Xiyan city, shaanxi in 2018 and 3 months 2019 (internal test, confidentiality state). The material for experiment is "Long eggplant 305", the substrate used for planting is Pindstrup substrate (Pindstrup substrate), and the experimental eggplant plants are cultivated in RGL-P500D-CO produced by Hefedbatchet company 2 Carbon dioxide artificial box. The incubator is set with photoperiod of day/night: 14/10 hours, set temperature day/night: 25 ℃/16 ℃, relative humidity set day/night: 60%/50% with CO set 2 The concentration was 400. Mu. Mol/mol. Dividing 54 eggplant seedlings into 6 groups, respectively culturing in 1-6 grade incubator with illumination grade, and culturing with other environmental factorsThe breeding management is consistent, the watering is unified, and no hormone or pesticide is sprayed. And (3) when the eggplant plants subjected to different light treatments have differences, randomly selecting the plants with obvious differences in each incubator as experimental samples.
The experiment designs the temperature and CO of the different blades 2 And measuring and recording the dark fluorescence parameters of the leaves at the net photosynthetic rate under the nested condition of concentration and illumination intensity. In the experiment, the fluorescence data of the leaves of eggplant was measured using a MINI-PAM-II fluorometer manufactured by WALZ, germany, and the net photosynthetic rate of the leaves of eggplant was measured using an LI-6800 photosynthesizer manufactured by LI-COR, USA. The net photosynthetic rate measurement experiment sets the photosynometry quantum flux density gradient as 1500, 1200, 800, 600, 150, 60, 30, 15 mu mol/m 2 S, setting leaf chamber CO 2 The concentration gradient was 1300 1000 to 700. Mu. Mol/mol, and the leaf chamber temperature gradient was set at 35 to 27 to 23 ℃ and 19 ℃. The data measurement experiment starts from 27 days 2 and 3 days 21 in 2019, and four leaves with larger differences are randomly selected to carry out the experiment on the same day. Dark adaptation was first performed for 20 minutes for each leaf using dark-adapted leaf clamps, followed by measurement of dark fluorescence parameters and net photosynthetic rate to obtain 1295 sets of data for modeling.
1.2 model building method
The process for establishing the regression type support vector machine accurate photosynthetic rate model based on the improved genetic algorithm optimization parameters mainly comprises sample data processing, model parameter selection and model construction.
1.2.1 data preprocessing
Normalization processing is needed to be carried out on different dimensions of sample data before a support vector machine is trained, so that the data of all dimensions are in the same order of magnitude, sample imbalance caused by overlarge data difference is avoided, and finally a model deviates from an accurate hyperplane. The normalized interval is [0,1], and the formula is:
wherein x is the original data, x min Is the minimum value, x, of all the raw data max Is the maximum of all the original data。
After the data set is normalized, the data set is randomly divided into 80% of training set and 20% of testing set for model training. Wherein the input characteristic of the ith sample is x i ＝(x i (1) ,x i (2) ,x i (3) ,x i (4) ,x i (5) ) The output label is y i ，x i (1) ,x i (2) ,x i (3) ,x i (4) ,x i (5) The light quantum flux density, the carbon dioxide concentration, the temperature, the minimum fluorescence parameter Fo and the maximum fluorescence parameter Fm, y of the ith sample i The photosynthetic rate of the ith sample.
1.2.2 model parameter selection
The genetic algorithm is an algorithm which quickly converges to an optimal value point by simulating the natural selection and evolution of species in nature, and is widely applied to the actual problem solving by effectively preventing the local optimization and the efficient nonlinear solving. The photosynthetic rate model parameters c and g of the photosynthetic capacity of the fused eggplant leaves based on the regression type support vector machine are solved by an improved genetic algorithm.
The algorithm sets the index range of c as [0.01,50] and the number of genes as 6, sets the index range of g as [0.01,5] and the number of genes as 5.
Setting the encoding mode as binary encoding, wherein the encoding formula is as follows:
the decoding formula is:
wherein b is the coded binary string, m is the number of characters of the binary string taken by the chromosome, a is the decimal number to be coded, a min For coding the space minimum decimal number, a max Is the largest decimal number in the coding space.
And (3) training a regression type support vector machine by using the decoded c and g as parameters and training set data, calculating a decision coefficient of a prediction set data set, using the decision coefficient as chromosome fitness, and carrying out population elimination and selection by using a roulette wheel. In the formed offspring, the artificial intervention of the species selection process is simulated, and the highest fitness individual of the parent is substituted for the lowest fitness individual of the offspring. And forming new filial generations through crossing, mutation and selection operations, and carrying out iterative evolution to finally converge to the optimal solution parameters c and g. Because the gene diversity in the population is reduced by manual intervention operation, a suitably large population variation coefficient should be selected, the selection variation coefficient is 0.2, the cross coefficient is 0.8, and the optimal solution parameters c and g are obtained by iterative evolution for 50 generations. The flow chart of the improved genetic algorithm is shown in fig. 2.
1.2.3 photosynthetic rate prediction model establishment for leaf photosynthetic potential fusion
For a given training sample { (x) 1 ,y 1 ),(x 2 ,y 2 ),…,(x m ,y m ) The regression model of the regression support vector machine can be trained to have a regression model f (x) = w T x + v, where w is the input coefficient and v is the model intercept at each axis, let f (x) i ) And y i As close as possible, the modeling flow is shown in fig. 3.
In model training, the model introduces a relaxation variable epsilon i Forming a function soft interval, and converting the regression support vector machine problem into:
the target is as follows:
constraint of f (x) i )-y i ≤ε+ε i ,ε i ≥0,i＝1,2,…,m
Wherein c is a regularization parameter, which influences the model accuracy; epsilon is an insensitive loss function and is determined by a support vector;ε i and
is the relaxation variable.
For nonlinear problems, a support vector machine converts the nonlinear problems into linear problems through kernel techniques to solve, and the specific method is to map input vectors into a higher-dimensional space through a kernel function, convert the nonlinear problems into linear problems to solve, construct a regression hyperplane, finally map the regression hyperplane back to an original space and convert the regression hyperplane into a nonlinear hypersurface. The Gauss kernel function has the characteristics of parameter change and unchanged model complexity, and the Gauss kernel function is selected for the model to carry out dimension transformation. The Gauss kernel function is of the form:
wherein X is a sample to be transformed, X p Is the center of the p-th kernel function, and g is a kernel function parameter, which influences the kernel function form.
Combining the nuclear skills, the model is finally learned to be:
wherein,
α i is the lagrange multiplier in the solution process.
2 results and discussion
2.1 comparison of the photoresponse curves of the leaves with different fluorescence parameters
The photosynthetic rate of the leaves with different fluorescence parameters is different under the same external environment. FIG. 4 shows the light response curves of leaves with different fluorescence parameters under the condition of consistent temperature and carbon dioxide concentration, and the difference is obvious and can be attributed to the difference of the photosynthetic potential of the leaves. As shown in the figure, the smaller Fo value and the larger Fm value of the photosynthetic potential of the leaf are larger, and the photosynthetic capacity is larger under the same external conditions. And the external environments required by the leaves with different fluorescence values at the same photosynthetic rate are different, which has important significance for guiding the precise regulation and control of the facility environment.
2.2 model Performance and validation results analysis
The accuracy of the photosynthetic rate model trained by the regression support vector machine algorithm on data prediction is closely related to the parameters c and g, and different prediction errors are obtained by different parameters. The mean square error curve of the model for the unknown data prediction is shown in fig. 5, which shows that there are relatively optimal parameters, so that the prediction error of the model for the unknown data can be minimized.
The optimal parameter value is obtained by optimizing with an improved genetic algorithm, and the obtained genetic evolution curve is shown in fig. 6. The coefficients for determining the model prediction dataset and the true dataset increased with the increase in the genetic algebra, and finally converged to 0.993, and the root mean square error at which the optimum parameters were obtained (c =41.07, g = 3.30) was 0.246 μmol · m -2 s -1 The data can be fitted with higher accuracy.
In order to verify the generalization capability of the model, a 5-fold cross verification method is adopted, sample data is randomly divided into five parts, the model is set by using parameters c and g obtained by an optimization algorithm, one part is sequentially selected as a verification set, and the remaining four parts are used for training the model, so that five models under the parameters c and g obtained by optimization are finally obtained. The performance of each model is verified through verification set data to obtain five groups of verification data. The maximum coefficient of the decision of the model prediction data and the minimum coefficient of the decision of the verification data are 0.992 and 0.988, and the approximation degrees of the two are shown in FIG. 7, which shows that the model has excellent generalization capability
The leaves in different physiological states have larger photosynthetic potential difference, when the photosynthetic potential in the leaves is not introduced as a photosynthetic rate influencing factor and only external environmental factors are used as model input, different leaf photosynthetic rate models have larger difference, and a nested experimental data set is difficult to fit an accurate photosynthetic rate model. Therefore, in the process of building and researching the photosynthetic rate model by the scholars in the past, plants with strictly consistent growth vigor must be adopted for the experiment, the same leaf position is required to be used for the position measured by the experimental data, and the built model is poor in universality.
Dark fluorescence parameters representing plant photosynthetic potential are removed from the experimental sample, and only external PPFD and CO are used 2 The concentration and the atmospheric temperature are input parameters, the photosynthetic rate is used as an output parameter, and a photosynthetic rate model is established under the same condition. The coefficient of the model for determining unknown prediction set data is 0.5909, and the root mean square error is 14.4937 mu mol.m -2 s -1 The distribution of the calculated value and the real value of the photosynthetic rate model is shown in fig. 8, which is difficult to be applied in practice.
Therefore, the photosynthetic rate model established by fusing the light and the potential inside the plant by adopting the support vector machine optimized by the improved genetic algorithm has excellent fitting effect and generalization capability, and can realize accurate prediction of the photosynthetic rates of the plant leaves in different states.
In summary, the invention is based on the principle that the photosynthetic rate of the crop is not only related to the external environment, but also related to the state of the plant, and the photosynthetic rate model fused with the photosynthetic potential of the plant can more accurately predict the photosynthetic rate of the plant, the concentration of carbon dioxide, the illumination intensity and the temperature in the greenhouse environment are main external factors influencing the photosynthetic rate, the photosynthetic potential of the plant leaves is an internal factor influencing the photosynthesis of the plant, and the photosynthetic potential of the plant is reflected by the dark fluorescence parameters of the plant. Experiments are designed to obtain the photosynthetic rate of the leaves with different dark fluorescence parameters under the nesting of temperature, carbon dioxide concentration and illumination light quantum flux density, and 1295 groups of data are obtained. According to the obtained data, a regression type support vector machine algorithm is utilized to establish a crop photosynthetic rate model, an improved genetic algorithm is used for matching optimal parameters, the decision coefficient of the obtained model to unknown prediction set data is 0.993, and the root mean square error is 0.246 mu mol.m -2 s -1 . Based on the obtained model, 5-fold cross validation is carried out on all data, the maximum coefficient of determination is 0.992, and the minimum coefficient of determination is 0.988, and the model is proved to have excellent generalization capability. A photosynthetic rate model fusing internal and external influence factors of crops is established for the crops with different photosynthetic potentials, and an important theoretical basis and technical realization are provided for the accurate regulation and control of facility environment.
The photosynthetic rate prediction model based on the photosynthetic potential of the leaves can provide a theoretical basis for the accurate facility environment regulation and control of plants in different growth states, obviously, the model can be expanded to different crops, and the facility crop efficiency is improved.
Claims (6)
1. The photosynthetic rate prediction method for the photosynthetic potential of the fused leaves is characterized by comprising the following steps of:
s1, designing plant photosynthesis tests with the same parameters except for illumination grades, wherein the physiological states of leaves of plants to be treated by different illuminations are different, and randomly selecting plants with obvious differences in the physiological states of the leaves as experiment samples;
s2, measuring the temperature and CO of the different blades 2 Net photosynthetic rate under nested conditions of concentration and illumination intensity, and recording dark fluorescence parameters of leaves as sample data;
s3, normalization processing is carried out on different dimensions of the sample data, so that the data of all the dimensions are in the same order of magnitude, and a test set and a training set are divided;
s4, establishing a photosynthetic rate prediction model fusing the photosynthetic potential of the leaves by utilizing a regression type support vector machine algorithm;
s5, predicting the photosynthetic rate of the photosynthetic potential of the fused leaves by using the prediction model;
wherein, in the step S4,
solving the optimal regularization parameter c and kernel function parameter g of the model by using an improved genetic algorithm, setting the index range of c as [0.01,50], the number of genes as 6, the index range of g as [0.01,5], the number of genes as 5, setting the coding mode as binary coding, and setting the coding formula as follows:
the decoding formula is:
wherein b is the coded binary string, m is the number of characters of the binary string taken by the chromosome, a is the decimal number to be coded, a min For coding the space minimum decimal number, a max Is the maximum decimal number of the coding space;
using decoded c and g as parameters, training a regression type support vector machine by using training set data, calculating a decision coefficient of test set data, using the decision coefficient as chromosome fitness, carrying out population elimination and selection by using a roulette wheel, simulating manual intervention in a species selection process in formed offspring, replacing an offspring lowest fitness individual by a parent maximum fitness individual, forming new offspring by crossing, variation and selection of the offspring, taking a variation coefficient of 0.2, taking a cross coefficient of 0.8, and carrying out iterative evolution for 50 generations to obtain optimal solution parameters c and g;
in the step S4, the modeling process is as follows:
s4.1, reading in modeling data and carrying out normalization pretreatment;
s4.2, selecting 80% of sample data as a training set, and selecting modeling parameters including a regularization parameter c, a kernel function and a kernel function parameter g;
s4.3, solving regression hyperplane parameters according to the KKT condition to obtain a photosynthetic rate model;
step S4.4, substituting the test set data into the model to obtain fitting data, calculating fitting errors, if the fitting errors do not meet the requirements, repeating the steps S4.2-S4.4 until the fitting errors meet the requirements, and obtaining a final model f (x) = w T And x + v, wherein w is an input coefficient, and v is the intercept of the model in each axis.
2. The method for predicting photosynthetic rate of photosynthetic potential of the fused leaf as claimed in claim 1 wherein in step S1, the illumination level is 1-6 levels and the other parameters are day/night light cycle, day/night temperature, day/night relative humidity and CO 2 And (4) concentration.
3. The method for predicting photosynthetic rate of photosynthetic potential of fused blade according to claim 1, wherein in step S3, the normalized interval is [0,1], and the formula is:
wherein x is the original data, x min Is the minimum value, x, of all the raw data max Is the maximum of all the raw data.
4. The method for predicting photosynthetic rate of photosynthetic potential of fused leaf according to claim 1 or 3, wherein in step S3, 80% of sample data are randomly divided into a training set, and the remaining 20% of sample data are divided into a testing set, wherein the input characteristic of the ith sample is x i ＝(x i (1) ,x i (2) ,x i (3) ,x i (4) ,x i (5) ) The output label is y i ，x i (1) ,x i (2) ,x i (3) ,x i (4) ,x i (5) The light quantum flux density, the carbon dioxide concentration, the temperature, the minimum fluorescence parameter Fo and the maximum fluorescence parameter Fm, y of the ith sample are respectively i The photosynthetic rate of the ith sample.
5. The method for predicting photosynthetic rate of photosynthetic potential of fused leaf according to claim 1, wherein the model introduces a relaxation variable epsilon during model training i Forming the functional soft interval, the regression support vector machine problem can be transformed into:
the target is as follows:
constraint of f (x) i )-y i ≤ε+ε i ,ε i ≥0,i＝1,2,…,M
Wherein, y i Determining the photosynthetic rate of the ith sample, wherein epsilon is an insensitive loss function and is determined by a support vector; epsilon i And
is a relaxation variable; and M is the total number of model training samples, for a nonlinear problem, an input vector is mapped to a higher-dimensional space through a kernel function, the nonlinear problem is converted into a linear problem to be solved, a regression hyperplane is constructed, and finally the regression hyperplane is mapped back to an original space and converted into a nonlinear hypersurface.
6. The photosynthetic rate prediction method for photosynthetic potential of fused leaves according to claim 5, characterized in that Gauss kernel function is used for dimension transformation, and the form of the Gauss kernel function is as follows:
wherein X is a sample to be transformed, X p The center of the p-th kernel function is defined, g is a kernel function parameter, and finally, the model is learned by combining kernel skills as follows:
wherein,
α i is the lagrange multiplier in the solution process.
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