System and method for analyzing influence of illumination on wheat growth based on SVM algorithm

Abstract

The invention discloses a system and method for analyzing the influence of illumination on wheat growth based on an SVM algorithm. The method comprises the steps that illumination intensity data in a wheat growth environment and growth and development index data of wheat are collected; carrying out cleaning and normalization preprocessing on the collected data; an SVM algorithm is adopted to analyze the relation between the illumination intensity and wheat growth and development; a result obtained through analysis is displayed to a user in a visual mode; according to the system and the method, the SVM algorithm is adopted, and the system and the method have the advantages of small sample data, nonlinear relation processing, generalization ability and parameter adjustment, so that a scientific basis is provided for wheat planting management, and the beneficial technical effect of better adapting to the requirements of wheat growth and development analysis is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural data analysis, and in particular to a system and method for analyzing the influence of illumination on wheat growth based on a SVM algorithm. Background Art

[0002] Wheat growth and development are affected by many factors, among which light intensity is crucial. Different algorithms have their own characteristics when analyzing the effect of light intensity on wheat growth and development.

[0003] However, in analyzing the impact of light intensity on wheat growth and development, the current existing algorithms have defects and shortcomings such as the inability to perform accurate analysis and modeling with small sample data, weak ability to handle the complex nonlinear relationship between light intensity and wheat growth and development, poor generalization ability, and complex parameter adjustment; they are unable to achieve the goal of optimizing the wheat growth environment, providing a scientific basis for wheat planting management, and improving wheat yield and quality. Summary of the invention

[0004] In view of the above technical problems in the related art, the present invention proposes a system and method for analyzing the impact of light on wheat growth based on the SVM algorithm, which can overcome the above shortcomings of the prior art.

[0005] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows:

[0006] In the first aspect, the present invention provides a method for analyzing the effect of light on wheat growth based on a SVM algorithm;

[0007] The method for analyzing the effect of light on wheat growth based on the SVM algorithm specifically comprises the following steps:

[0008] S1: Collect data, including the light intensity data in the wheat growth environment and the growth and development index data of wheat;

[0009] S2: Data preprocessing, cleaning and normalizing the data collected in S1;

[0010] S3: Correlation analysis, using SVM algorithm to analyze the relationship between light intensity and wheat growth and development;

[0011] S4: Result display: the results analyzed in S3 are displayed to the user in a visual manner.

[0012] Furthermore, S3 comprises the following steps:

[0013] S301: Find an optimal classification hyperplane in the feature space;

[0014] For the linearly separable case, this hyperplane can be expressed as: T x+b=0, where w is the normal vector, which determines the direction of the hyperplane, and b is the intercept;

[0015] For the case of linear inseparability, by introducing the kernel function K(x i , x j ), mapping the data to a high-dimensional space so that the data becomes linearly separable in the high-dimensional space;

[0016] S302: Perform binary classification. For binary classification problems, the goal of SVM is to find a hyperplane that maximizes the distances between the two types of sample points and the hyperplane.

[0017] S303: Perform SVM regression; by constructing a function: f(x) = w T φ(x)+b minimizes the error between the predicted value and the true value, where φ(x) is a function that maps the input data to a high-dimensional space, and w and b are model parameters.

[0018] Furthermore, in S301, the decision function in the process of solving the optimal classification hyperplane is:

[0019]

[0020] where α i is the Lagrange multiplier, y i is the category label of the sample, and N is the number of samples.

[0021] According to a second aspect of the present invention, the present invention provides a system for analyzing the effect of illumination on wheat growth based on a SVM algorithm;

[0022] The light effect analysis system on wheat growth based on the SVM algorithm includes a data acquisition module, a data preprocessing module, an algorithm analysis module and a result display module connected by communication;

[0023] The data acquisition module is used to collect light intensity data in the wheat growth environment and wheat growth and development index data;

[0024] The data preprocessing module is used to clean and normalize the data collected by the data acquisition module;

[0025] The algorithm analysis module uses the SVM algorithm to analyze the relationship between light intensity and wheat growth and development;

[0026] The result display module is used to display the analysis results to the user in a visual manner.

[0027] Furthermore, the data acquisition module includes a light sensor and a device for measuring wheat growth indicators.

[0028] The beneficial effects of the present invention are as follows: through the system and method of the present invention, the SVM algorithm is adopted, which has the advantages of small sample data, nonlinear relationship processing, generalization ability and parameter adjustment, thereby providing a scientific basis for wheat planting management, and further achieving the beneficial technical effect of better adapting to the needs of wheat growth and development analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 It is a logic block diagram of a system and method for analyzing the effect of illumination on wheat growth based on a SVM algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0032] It should be understood that, in the description of the embodiments of the present invention, the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "several" is two or more, unless otherwise clearly and specifically defined.

[0033] A system for analyzing the effect of illumination on wheat growth based on a SVM algorithm according to an embodiment of the present invention includes a data acquisition module, a data preprocessing module, an algorithm analysis module, and a result display module connected in communication.

[0034] The data acquisition module is used to collect the light intensity data in the wheat growth environment and the wheat growth and development index data. The module may include a light sensor, a device for measuring wheat growth indexes, etc., to ensure the accuracy and real-time nature of the data.

[0035] The data preprocessing module performs preprocessing operations such as cleaning and normalization on the collected data, removes outliers and noise, makes the data have a unified dimension, and provides high-quality data for subsequent analysis.

[0036] The algorithm analysis module: uses the SVM algorithm to analyze the relationship between light intensity and wheat growth and development, determines the appropriate kernel function, penalty parameters, etc., and constructs a model to accurately predict wheat growth and development indicators.

[0037] The result display module: displays the analysis results to the user in a visual manner. For example, the effect of light intensity on different growth stages of wheat is displayed in the form of charts, graphs, etc., so that the user can intuitively understand the analysis results.

[0038] A method for analyzing the effect of illumination on wheat growth based on a SVM algorithm according to an embodiment of the present invention specifically comprises the following steps:

[0039] S1: Collect the light intensity data in the wheat growth environment and the wheat growth and development index data;

[0040] S2: Clean and normalize the data collected in S1;

[0041] S3: SVM algorithm was used to analyze the relationship between light intensity and wheat growth and development;

[0042] S4: Display the results analyzed in S3 to the user in a visual manner.

[0043] Among them, the SVM algorithm formula and description are as follows:

[0044] The core goal of the SVM algorithm is to find an optimal classification hyperplane in the feature space. For the linearly separable case, this hyperplane can be expressed as: T x+b=0, where w is the normal vector, which determines the direction of the hyperplane, and b is the intercept, so that sample points of different categories are divided as correctly as possible and the intervals are maximized.

[0045] For the binary classification problem, the goal of SVM is to find a hyperplane that maximizes the distance between the two types of sample points and the hyperplane. This distance is called the margin, and the larger the margin, the better the classification robustness.

[0046] For the case of linear inseparability, by introducing the kernel function: K(x i , x j ), mapping the data to a high-dimensional space so that the data becomes linearly separable in the high-dimensional space. Common kernel functions include linear kernel, polynomial kernel, Gaussian kernel, etc.

[0047] In the process of solving the optimal hyperplane, it is usually transformed into a convex quadratic programming problem, which can be solved using the Lagrange multiplier method. The final decision function is:

[0048]

[0049] where α i is the Lagrange multiplier, y i is the category label of the sample, and N is the number of samples.

[0050] For regression problems, the goal of SVM regression (SVR) is to construct a function:

[0051] f(x)=w T φ(x)+b

[0052] Minimize the error between the predicted value and the true value, where φ(x) is the function that maps the input data to a high-dimensional space, w and b are model parameters, and the flatness of the function is controlled. The model parameters are also solved by introducing kernel functions and optimization algorithms.

[0053] The present invention relates to code implementation, which is as follows:

[0054] #Import required libraries

[0055] from sklearn.svm import SVR

[0056] #Assume that X is the feature matrix and y is the target variable (wheat growth and development index)

[0057] X = np.array([...])

[0058] y = np.array([...])

[0059] #Create SVM regression model

[0060] svm_model = SVR()

[0061] #Train model

[0062] svm_model.fit(X,y)

[0063] # Make predictions on new data

[0064] new_data = np.array([...])

[0065] prediction=svm_model.predict(new_data)

[0066] In analyzing the effect of light intensity on wheat growth and development, the SVM algorithm of the present invention has the following significant advantages over the random forest algorithm:

[0067] 1. Strong adaptability to small samples: SVM can accurately analyze and model small samples, while random forests require large-scale data and are prone to overfitting in small samples;

[0068] 2. Strong nonlinear processing capability: SVM can handle complex nonlinear relationships through kernel functions, and is more flexible and accurate than random forest in some problems;

[0069] 3. Good generalization ability: SVM can make reasonable predictions when the new data is similar to the training data, while random forests are affected by parameters and need to be adjusted carefully;

[0070] 4. Simple parameter adjustment: SVM parameters such as the kernel width of the Gaussian kernel function are easy to adjust, while random forest parameters are numerous and complex.

[0071] To sum up, with the help of the above-mentioned technical scheme of the present invention, through the system and method of the present invention, the SVM algorithm is adopted, which has the advantages of small sample data, nonlinear relationship processing, generalization ability and parameter adjustment, thereby providing a scientific basis for wheat planting management, and thus achieving the beneficial technical effect of better adapting to the needs of wheat growth and development analysis.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the effect of light on wheat growth based on SVM algorithm, characterized in that: The specific steps include: S1: Collect data, including the light intensity data in the wheat growth environment and the growth and development index data of wheat; S2: Data preprocessing, cleaning and normalizing the data collected in S1; S3: Correlation analysis, using SVM algorithm to analyze the relationship between light intensity and wheat growth and development; S4: Result display: the results analyzed in S3 are displayed to the user in a visual manner.

2. The method for analyzing the effect of illumination on wheat growth based on the SVM algorithm according to claim 1, characterized in that: The S3 comprises the following steps: S301: Find an optimal classification hyperplane in the feature space; For the linearly separable case, this hyperplane can be expressed as: T x+b=0, where w is the normal vector, which determines the direction of the hyperplane, and b is the intercept; For the case of linear inseparability, by introducing the kernel function K(x i , x j ), mapping the data to a high-dimensional space so that the data becomes linearly separable in the high-dimensional space; S302: Perform binary classification. For binary classification problems, the goal of SVM is to find a hyperplane that maximizes the distances between the two types of sample points and the hyperplane. S303: Perform SVM regression; by constructing a function: f(x) = w T φ(x)+b minimizes the error between the predicted value and the true value, where φ(x) is a function that maps the input data to a high-dimensional space, and w and b are model parameters.

3. The method for analyzing the effect of illumination on wheat growth based on the SVM algorithm according to claim 2, characterized in that: In S301, the decision function in the process of solving the optimal classification hyperplane is: where α i is the Lagrange multiplier, y i is the category label of the sample, and N is the number of samples.

4. A system for analyzing the effect of light on wheat growth based on SVM algorithm, characterized in that: It includes a data acquisition module, a data preprocessing module, an algorithm analysis module and a result display module connected by communication; The data acquisition module is used to collect light intensity data in the wheat growth environment and wheat growth and development index data; The data preprocessing module is used to clean and normalize the data collected by the data acquisition module; The algorithm analysis module uses the SVM algorithm to analyze the relationship between light intensity and wheat growth and development; The result display module is used to display the analysis results to the user in a visual manner.

5. The system for analyzing the effect of illumination on wheat growth based on the SVM algorithm according to claim 4 is characterized in that: The data acquisition module includes a light sensor and a device for measuring wheat growth indicators.

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