Trait evaluation method for lagerstroemia indica germplasm resources

Abstract

The invention discloses a character evaluation method for lagerstroemia indica germplasm resources, and relates to the technical field of lagerstroemia indica planting. A character evaluation method of a crape myrtle germplasm resource comprises the steps of target crape myrtle variety morphological character extraction, target crape myrtle variety morphological character evaluation, target crape myrtle variety gene character evaluation, target crape myrtle variety germplasm resource environment evaluation and target crape myrtle variety character comprehensive evaluation. By combining morphological character extraction, gene character evaluation and environment evaluation, comprehensive evaluation of germplasm resources of crape myrtle is provided, visible morphological characteristics are considered, and genetic and environmental factors are also included; a large number of images are automatically analyzed by using an image classification model and a multi-scale feature extraction model, so that the character extraction efficiency and speed are remarkably improved; through the accurate feature extraction and evaluation strategy, a finer and more accurate evaluation result can be provided, which is helpful for identifying and distinguishing the nuisance of different crape myrtle varieties.

Description

Technical Field

[0001] The invention relates to the technical field of crape myrtle planting, and in particular to a method for evaluating the properties of crape myrtle germplasm resources. Background Art

[0002] As an important ornamental plant, crape myrtle occupies an important position in landscaping and ecological protection. The trait evaluation of its germplasm resources is of great significance for variety improvement, genetic diversity protection and adaptive management. Traditional crape myrtle germplasm resource evaluation methods often focus on the evaluation of a single trait, such as only focusing on the color or morphological characteristics of crape myrtle, while ignoring the comprehensive consideration of multiple traits; or rely on manual observation and evaluation, which is easily affected by subjective judgment and lacks quantification and standardization. A trait evaluation method that comprehensively considers multiple aspects of crape myrtle, such as morphology, ecological adaptability, genetic diversity, etc., is needed to provide a more comprehensive evaluation of crape myrtle germplasm resources. Summary of the invention

[0003] The present invention aims to provide a method for evaluating the traits of crape myrtle germplasm resources, collect the trait characteristics of crape myrtle germplasm resources in a diversified manner, and conduct accurate evaluation.

[0004] A method for evaluating the traits of crape myrtle germplasm resources, comprising:

[0005] S1. Extraction of morphological traits of target crape myrtle varieties

[0006] Obtain an image of a target crape myrtle variety to be analyzed; input the image of the target crape myrtle variety to be analyzed into a crape myrtle variety image classification model for analysis to obtain a classification result of the target crape myrtle variety; match the classification result of the target crape myrtle variety in a crape myrtle germplasm resource trait evaluation library to obtain a trait evaluation strategy for the target crape myrtle variety; the trait evaluation strategy for the target crape myrtle variety includes morphological trait evaluation rules and genetic trait evaluation rules; the crape myrtle germplasm resource trait evaluation library includes several crape myrtle varieties and corresponding crape myrtle variety trait evaluation strategies;

[0007] Obtain a set of morphological images of target crape myrtle varieties to be analyzed; the set of morphological images of target crape myrtle varieties to be analyzed contains M morphological images P of target crape myrtle varieties to be analyzed. m , m=1,2,…,M; input the target crape myrtle variety morphological image set to be analyzed into the crape myrtle variety multi-scale feature extraction model for feature extraction, and obtain the target crape myrtle variety morphological trait feature set X, X=[X1,X2,…,X n , …, X N ];X n represents the target crape myrtle variety morphological trait X in the target crape myrtle variety morphological trait set X n , n = 1, 2, ..., N; N is the number of morphological categories of the target crape myrtle varieties;

[0008] S2. Evaluation of morphological traits of target crape myrtle varieties

[0009] Based on the target crape myrtle variety trait evaluation strategy, the target crape myrtle variety morphological trait feature set X is evaluated to obtain the target crape myrtle variety morphological trait evaluation score set Y, where Y = [Y1, Y2, …, Y n , …, Y N ]; Y n Indicates the morphological characteristics of the target crape myrtle variety X n Corresponding morphological trait evaluation score Y of target crape myrtle variety n ;

[0010] S3. Evaluation of genetic traits of target crape myrtle varieties

[0011] Obtaining the genetic information of the target crape myrtle variety to be analyzed; evaluating the genetic information of the target crape myrtle variety to be analyzed based on the target crape myrtle variety trait evaluation strategy, and obtaining the genetic evaluation score of the target crape myrtle variety;

[0012] S4. Environmental assessment of target crape myrtle germplasm resources

[0013] Obtaining germplasm environmental information of the target crape myrtle variety to be analyzed; inputting the germplasm environmental information of the target crape myrtle variety to be analyzed into the crape myrtle germplasm resource environmental assessment model for auxiliary analysis to obtain an environmental assessment score of the target crape myrtle variety;

[0014] S5. Comprehensive evaluation of the traits of target crape myrtle varieties

[0015] The gene evaluation score of the target crape myrtle variety, the morphological trait evaluation score set Y of the target crape myrtle variety and the environmental evaluation score of the target crape myrtle variety are comprehensively evaluated using the crape myrtle variety trait evaluation function to obtain the comprehensive evaluation score of the target crape myrtle variety trait; the crape myrtle variety trait evaluation function is determined using a population optimization algorithm.

[0016] As a preferred technical solution of the present invention, the crape myrtle variety image classification model in step S1 includes an image preprocessing layer, an image feature extraction layer, an image classification layer and a result output layer;

[0017] The image preprocessing layer is used to preprocess the target crape myrtle variety image to be analyzed to obtain a preprocessed target crape myrtle variety image;

[0018] The image feature extraction layer is used to extract features from the preprocessed target crape myrtle variety image to obtain the target crape myrtle variety image features;

[0019] The image classification layer is used to classify the target crape myrtle varieties according to the image features of the target crape myrtle varieties, and obtain the classification results of the target crape myrtle varieties;

[0020] The result output layer is used to output the classification results of the target crape myrtle varieties;

[0021] The specific steps for training the image classification layer in the image classification model for crape myrtle varieties include:

[0022] Collect several groups of crape myrtle variety image classification samples; each group of crape myrtle variety image classification samples contains crape myrtle variety images and corresponding crape myrtle variety categories; use the corresponding crape myrtle variety categories in each group of crape myrtle variety image classification samples as target values; combine several groups of crape myrtle variety image classification samples to obtain a crape myrtle variety image classification training set;

[0023] The crape myrtle variety image classification training set is input into the crape myrtle variety image classification model to train the image classification layer to obtain the initial image classification layer; the initial image classification layer is evaluated to obtain the initial image classification layer model evaluation result; if the initial image classification layer model evaluation result is passed, the initial image classification layer is used as the image classification layer in the crape myrtle variety image classification model; otherwise, the crape myrtle variety image classification training set is used to continue model training.

[0024] As a preferred technical solution of the present invention, the multi-scale feature extraction model of crape myrtle varieties in step S1 includes an image set classification layer, N morphological feature extraction layers T n and the resulting output layer;

[0025] The image set classification layer is used to classify the target crape myrtle variety morphological image P in the target crape myrtle variety morphological image set. m Classify and obtain the target crape myrtle variety morphological image classification F m ; Morphological feature extraction layer T n Corresponding to the target crape myrtle variety morphological image classification L n ;

[0026] For the morphological image P of the target crape myrtle variety to be analyzed m , analysis and classification of target crape myrtle variety morphological images F m Classification of morphological images of the same target crape myrtle varieties n The corresponding morphological feature extraction layer T n , and obtain the target morphological feature extraction layer; the target crape myrtle variety morphological image P m Input into the target morphological feature extraction layer;

[0027] N morphological feature extraction layers T n It is used to extract features from the input morphological image of the target crape myrtle variety to be analyzed, and obtain the morphological trait feature X of the target crape myrtle variety. n ;

[0028] In the morphological feature extraction layer T nIn the process, the input morphological images of the target crape myrtle varieties to be analyzed are spliced ​​by using the image stitching method to obtain the comprehensive morphological images of the target crape myrtle varieties; the feature analysis of the comprehensive morphological images of the target crape myrtle varieties is performed to obtain the morphological trait feature X of the target crape myrtle varieties. n ;

[0029] The result output layer is used to extract the N morphological features from the N morphological feature extraction layers T n Output of the target crape myrtle variety morphological characteristics X n Combination, to obtain the target crape myrtle variety morphological trait feature set X, X = [X1, X2, ..., X n , …, X N ], output the morphological trait feature set X of the target crape myrtle variety.

[0030] As a preferred technical solution of the present invention, the training morphological feature extraction layer T n The specific steps include:

[0031] For the morphological feature extraction layer T n :

[0032] Collect several sets of target crape myrtle variety morphological image training samples, each set of target crape myrtle variety morphological image training samples contains the target crape myrtle variety morphological image classification L n corresponding target crape myrtle variety morphological images and corresponding image features; combining a plurality of target crape myrtle variety morphological image training samples to obtain a target crape myrtle variety morphological image training set;

[0033] The target crape myrtle variety morphological image training set is input into the morphological feature extraction layer T n In the training, the image features are used as the target to obtain the initial morphological feature extraction layer C n ; Extract the initial morphological features from layer C n Perform model evaluation to obtain the initial morphological feature extraction layer C n Model evaluation results; if the initial morphological feature extraction layer C n If the model evaluation result is passed, the initial morphological feature extraction layer C n As the morphological feature extraction layer T n ; Otherwise, continue model training using the target crape myrtle variety morphological image training set;

[0034] Traverse all morphological feature extraction layers T n , until N morphological feature extraction layers T are constructed n .

[0035] As a preferred technical solution of the present invention, the specific steps of step S2 include:

[0036] Evaluate according to the morphological trait evaluation rules in the target crape myrtle variety trait evaluation strategy, where the morphological trait evaluation rules include a morphological trait evaluation rule set;

[0037] Targeted crape myrtle variety morphological characteristics X n :

[0038] Match the morphological characteristics of the target crape myrtle variety X n The morphological trait evaluation rule set with the largest membership function is obtained, and the morphological trait evaluation rule set J is obtained. n ; Based on the morphological trait evaluation rule set J n Assign the morphological trait evaluation score Y of the target crape myrtle variety n ;

[0039] X is the morphological characteristics of all target crape myrtle varieties n Evaluate and score all target crape myrtle varieties with morphological traits Y n Combination, to obtain the target crape myrtle variety morphological trait evaluation score set Y, Y = [Y1, Y2, ..., Y n , …, Y N ].

[0040] As a preferred technical solution of the present invention, the specific steps of step S3 include:

[0041] The target crape myrtle variety is evaluated according to the genetic trait evaluation rules in the trait evaluation strategy, which includes G genetic trait score ranges and corresponding gene evaluation scores;

[0042] Determine the genetic trait score range of the target crape myrtle variety's genetic information to be analyzed, and obtain the genetic trait score range of the target crape myrtle variety; determine the target crape myrtle variety's genetic evaluation score based on the genetic trait score range of the target crape myrtle variety.

[0043] As a preferred technical solution of the present invention, the crape myrtle germplasm resource environmental assessment model in step S4 includes an environmental information preprocessing layer, an environmental prediction layer and a result output layer;

[0044] The environmental information preprocessing layer is used to preprocess the germplasm environmental information of the target crape myrtle variety to be analyzed, and obtain the preprocessed germplasm environmental information of the target crape myrtle variety;

[0045] The environmental prediction layer is used to make predictions based on the pre-processed target crape myrtle variety germplasm environmental information to obtain the target crape myrtle variety germplasm environmental prediction information;

[0046] The result output layer is used to match the environmental assessment score of the target crape myrtle variety according to the target crape myrtle variety germplasm environmental prediction information;

[0047] The specific steps of training the environment prediction layer include:

[0048] Collecting several groups of continuous environmental prediction training samples, each group of environmental prediction training samples contains the germplasm environmental information of crape myrtle varieties; combining several groups of continuous environmental prediction training samples to obtain an environmental prediction training set; taking the latter group of environmental prediction training samples of two adjacent groups as the target value;

[0049] The environment prediction layer is trained with the target value as the target using the environment prediction training set to obtain an initial environment prediction layer; the initial environment prediction layer is evaluated to obtain an initial environment prediction layer model evaluation result; if the initial environment prediction layer model evaluation result is passed, the initial environment prediction layer is used as the environment prediction layer; otherwise, the model training is continued using the environment prediction training set.

[0050] As a preferred technical solution of the present invention, the specific steps of step S5 include:

[0051] The target crape myrtle variety gene evaluation score, the target crape myrtle variety morphological trait evaluation score set Y and the target crape myrtle variety environmental evaluation score are normalized to obtain the standard target crape myrtle variety gene evaluation score, the standard target crape myrtle variety morphological trait evaluation score set Y' and the standard target crape myrtle variety environmental evaluation score;

[0052] The gene evaluation score of the standard target crape myrtle variety, the morphological trait evaluation score set Y' of the standard target crape myrtle variety and the environmental evaluation score of the standard target crape myrtle variety are input into the crape myrtle variety trait evaluation function for calculation to obtain the comprehensive evaluation score of the target crape myrtle variety trait;

[0053] Specific steps to construct the evaluation function of crape myrtle variety traits:

[0054] Construct K initialization function individuals F k , each initialization function individual F k The method includes a simulated crape myrtle variety trait evaluation function for comprehensive evaluation; a plurality of simulated crape myrtle variety comprehensive evaluation training samples are collected; each group of simulated crape myrtle variety comprehensive evaluation training samples includes function input parameters and corresponding simulation target values;

[0055] Based on the initialization function individual F k The initial fitness A is obtained by performing simulation calculations with several groups of simulated comprehensive evaluation training samples of crape myrtle varieties. k ; Initial fitness A k The calculation method is as follows: according to the function input parameters and initialization function individual F in the simulated crape myrtle variety comprehensive evaluation training sample kPerform function calculation to obtain the simulated score value; calculate the accuracy of the simulated score value and the simulated target value to obtain the simulated accuracy; take the average of several groups of simulated accuracy as the initial fitness A k ;

[0056] Based on the initialization function individual F k The initial fitness A k , initialize K function individuals F k Divide into rooster iteration population, hen iteration population and chick iteration population; set the maximum number of iterations B max ; The current iteration number is b;

[0057] During iteration, the initialization function individuals in the rooster iteration population, hen iteration population, and chicken iteration population are mutated and updated, and the current iteration number b is less than B max / 2, the chicken iteration population mutates to the initialization function individuals in the hen iteration population, and the hen iteration population mutates to the initialization function individuals in the rooster iteration population; the current iteration number b is greater than B max / 2, the chicken iteration population and the hen iteration population mutate to the initialization function individuals in the rooster iteration population;

[0058] When the maximum number of iterations is reached, the output is the initialization function individual F corresponding to the maximum fitness k , which is the optimal initialization function individual; the simulated crape myrtle variety trait evaluation function in the optimal initialization function individual is used as the crape myrtle variety trait evaluation function.

[0059] The present invention has the following advantages:

[0060] 1. The present invention provides a comprehensive evaluation of crape myrtle germplasm resources by combining morphological trait extraction, genetic trait evaluation and environmental evaluation, which not only takes into account visible morphological characteristics, but also includes genetic and environmental factors; a large number of images are automatically analyzed using image classification models and multi-scale feature extraction models, which significantly improves the efficiency and speed of trait extraction; through precise feature extraction and evaluation strategies, more sophisticated and accurate evaluation results can be provided, which is helpful to identify and distinguish the subtle differences between different crape myrtle varieties; by establishing a trait evaluation library and evaluation strategy, the standardization of the evaluation process is achieved, the influence of human subjective judgment is reduced, and the consistency and repeatability of the evaluation are improved.

[0061] 2. The present invention helps to identify and protect genetic diversity by evaluating the genetic traits of crape myrtle varieties, and provides important information for breeding and germplasm resource protection; by evaluating the environmental resources of crape myrtle varieties, it provides information on the adaptability of crape myrtle varieties to specific environmental conditions, which helps to select varieties suitable for specific environments; by using a population optimization algorithm to determine the crape myrtle variety trait evaluation function, the accuracy and reliability of the comprehensive evaluation score are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a schematic diagram of a method for evaluating the traits of crape myrtle germplasm resources used in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0064] A method for evaluating the traits of Lagerstroemia indica germplasm resources, see Figure 1 As shown, including:

[0065] S1. Extraction of morphological traits of target crape myrtle varieties

[0066] Obtain an image of a target crape myrtle variety to be analyzed; input the image of the target crape myrtle variety to be analyzed into a crape myrtle variety image classification model for analysis to obtain a classification result of the target crape myrtle variety; match the classification result of the target crape myrtle variety in a crape myrtle germplasm resource trait evaluation library to obtain a trait evaluation strategy for the target crape myrtle variety; the trait evaluation strategy for the target crape myrtle variety includes morphological trait evaluation rules and genetic trait evaluation rules; the crape myrtle germplasm resource trait evaluation library includes several crape myrtle varieties and corresponding crape myrtle variety trait evaluation strategies;

[0067] Obtain a set of morphological images of target crape myrtle varieties to be analyzed; the set of morphological images of target crape myrtle varieties to be analyzed contains M morphological images P of target crape myrtle varieties to be analyzed. m , m=1,2,…,M; input the target crape myrtle variety morphological image set to be analyzed into the crape myrtle variety multi-scale feature extraction model for feature extraction, and obtain the target crape myrtle variety morphological trait feature set X, X=[X1,X2,…,X n , …, X N ];X n represents the target crape myrtle variety morphological trait X in the target crape myrtle variety morphological trait set X n , n = 1, 2, ..., N; N is the number of morphological categories of the target crape myrtle varieties;

[0068] The target crape myrtle variety image to be analyzed and the target crape myrtle variety morphological image set to be analyzed are obtained by photographing the target crape myrtle variety using a camera; the crape myrtle germplasm resource trait evaluation library is set by professional and technical personnel according to actual conditions, and the crape myrtle germplasm resource trait evaluation library contains evaluation strategies for several crape myrtle varieties; the target crape myrtle variety morphological classification is set by professional and technical personnel, such as leaf size, petal color, stamen shape and other characteristics;

[0069] The crape myrtle variety image classification model in step S1 includes an image preprocessing layer, an image feature extraction layer, an image classification layer and a result output layer;

[0070] The image preprocessing layer is used to preprocess the target crape myrtle variety image to be analyzed to obtain a preprocessed target crape myrtle variety image;

[0071] The image feature extraction layer is used to extract features from the preprocessed target crape myrtle variety image to obtain the target crape myrtle variety image features;

[0072] The image classification layer is used to classify the target crape myrtle varieties according to the image features of the target crape myrtle varieties, and obtain the classification results of the target crape myrtle varieties;

[0073] The result output layer is used to output the classification results of the target crape myrtle varieties;

[0074] The specific steps for training the image classification layer in the image classification model for crape myrtle varieties include:

[0075] Collect several groups of crape myrtle variety image classification samples; each group of crape myrtle variety image classification samples contains crape myrtle variety images and corresponding crape myrtle variety categories; use the corresponding crape myrtle variety categories in each group of crape myrtle variety image classification samples as target values; combine several groups of crape myrtle variety image classification samples to obtain a crape myrtle variety image classification training set;

[0076] Input the image classification training set of crape myrtle varieties into the image classification model of crape myrtle varieties to train the image classification layer and obtain the initial image classification layer; perform model evaluation on the initial image classification layer and obtain the model evaluation result of the initial image classification layer; if the model evaluation result of the initial image classification layer is passed, the initial image classification layer is used as the image classification layer in the image classification model of crape myrtle varieties; otherwise, continue to perform model training using the image classification training set of crape myrtle varieties;

[0077] Through the image preprocessing layer, automatic image processing is achieved, including denoising, contrast enhancement, resizing, etc., laying a good foundation for subsequent feature extraction and classification; the image feature extraction layer is specifically responsible for extracting key features from the preprocessed images, which can effectively express the morphological differences of crape myrtle varieties and identify different crape myrtle varieties; by collecting a large number of labeled crape myrtle variety image samples and using a data-driven method to train the model, the generalization ability of the model is enhanced, and the CNN model can be used as the basic model to identify the types of crape myrtle;

[0078] The multi-scale feature extraction model of crape myrtle varieties in step S1 includes an image set classification layer, N morphological feature extraction layers T n and the resulting output layer;

[0079] The image set classification layer is used to classify the target crape myrtle variety morphological image P in the target crape myrtle variety morphological image set. m Classify and obtain the target crape myrtle variety morphological image classification F m ; Morphological feature extraction layer T n Corresponding to the target crape myrtle variety morphological image classification L n ;

[0080] For the morphological image P of the target crape myrtle variety to be analyzed m , analysis and classification of target crape myrtle variety morphological images F m Classification of morphological images of the same target crape myrtle varieties n The corresponding morphological feature extraction layer T n , and obtain the target morphological feature extraction layer; the target crape myrtle variety morphological image P m Input into the target morphological feature extraction layer;

[0081] N morphological feature extraction layers T n It is used to extract features from the input morphological image of the target crape myrtle variety to be analyzed, and obtain the morphological trait feature X of the target crape myrtle variety. n ;

[0082] In the morphological feature extraction layer T n In the process, the input morphological images of the target crape myrtle varieties to be analyzed are spliced ​​by using the image stitching method to obtain the comprehensive morphological images of the target crape myrtle varieties; the feature analysis of the comprehensive morphological images of the target crape myrtle varieties is performed to obtain the morphological trait feature X of the target crape myrtle varieties. n ;

[0083] The result output layer is used to extract the N morphological features from the N morphological feature extraction layers T n Output of the target crape myrtle variety morphological characteristics X n Combination, to obtain the target crape myrtle variety morphological trait feature set X, X = [X1, X2, ..., X n, …, X N ], output the morphological trait feature set X of the target crape myrtle variety;

[0084] Train the morphological feature extraction layer T n The specific steps include:

[0085] For the morphological feature extraction layer T n :

[0086] Collect several sets of target crape myrtle variety morphological image training samples, each set of target crape myrtle variety morphological image training samples contains the target crape myrtle variety morphological image classification L n corresponding target crape myrtle variety morphological images and corresponding image features; combining a plurality of target crape myrtle variety morphological image training samples to obtain a target crape myrtle variety morphological image training set;

[0087] The target crape myrtle variety morphological image training set is input into the morphological feature extraction layer T n In the training, the image features are used as the target to obtain the initial morphological feature extraction layer C n ; Extract the initial morphological features from layer C n Perform model evaluation to obtain the initial morphological feature extraction layer C n Model evaluation results; if the initial morphological feature extraction layer C n If the model evaluation result is passed, the initial morphological feature extraction layer C n As the morphological feature extraction layer T n ; Otherwise, continue model training using the target crape myrtle variety morphological image training set;

[0088] Traverse all morphological feature extraction layers T n , until N morphological feature extraction layers T are constructed n ;

[0089] Image set classification layer and morphological feature extraction layer T n The combined use of ensures the accurate classification and feature extraction of the morphological images of crape myrtle varieties, which helps to more accurately identify and distinguish the morphological characteristics of different types of crape myrtle; through the setting of N morphological feature extraction layers Tn, the model can extract features from different scales, increasing the ability to capture the morphological diversity of crape myrtle varieties; the use of image stitching methods improves the comprehensiveness of feature extraction, which helps to grasp the morphological characteristics of crape myrtle varieties as a whole; accurate morphological trait evaluation helps to identify crape myrtle varieties with excellent traits, providing a scientific basis for breeding and variety improvement;

[0090] S2. Evaluation of morphological traits of target crape myrtle varieties

[0091] Based on the target crape myrtle variety trait evaluation strategy, the target crape myrtle variety morphological trait feature set X is evaluated to obtain the target crape myrtle variety morphological trait evaluation score set Y, where Y = [Y1, Y2, …, Y n , …, Y N ]; Y n Indicates the morphological characteristics of the target crape myrtle variety X n Corresponding morphological trait evaluation score Y of target crape myrtle variety n ;

[0092] The specific steps of step S2 include:

[0093] The evaluation is performed according to the morphological trait evaluation rules in the target crape myrtle variety trait evaluation strategy, wherein the morphological trait evaluation rules include a morphological trait evaluation rule set; the morphological trait evaluation rule set is set by professional and technical personnel according to actual conditions;

[0094] Targeted crape myrtle variety morphological characteristics X n :

[0095] Match the morphological characteristics of the target crape myrtle variety X n The morphological trait evaluation rule set with the largest membership function is obtained, and the morphological trait evaluation rule set J is obtained. n ; Based on the morphological trait evaluation rule set J n Assign the morphological trait evaluation score Y of the target crape myrtle variety n ;

[0096] X is the morphological characteristics of all target crape myrtle varieties n Evaluate and score all target crape myrtle varieties with morphological traits Y n Combination, to obtain the target crape myrtle variety morphological trait evaluation score set Y, Y = [Y1, Y2, ..., Y n , …, Y N ];

[0097] For example, for leaf morphological characteristics, if a certain morphological characteristic is "large leaves", then the calculation is performed in the morphological trait evaluation rule set according to the membership function. The morphological trait evaluation rule set is: "excellent", "good", and "medium". If the leaf trait is "large leaves", the score is "excellent", and the corresponding score is 95;

[0098] By using the trait evaluation strategy of the target crape myrtle variety, a personalized evaluation of each feature is achieved to ensure that the evaluation results are more in line with the specific features; by matching the evaluation rule set with the largest membership degree of the morphological trait of the target crape myrtle variety, the relevance and accuracy of the evaluation are improved; for each morphological trait X n Assign a specific evaluation score Y n, achieving refined scoring of different features;

[0099] S3. Evaluation of genetic traits of target crape myrtle varieties

[0100] Obtaining the genetic information of the target crape myrtle variety to be analyzed; evaluating the genetic information of the target crape myrtle variety to be analyzed based on the target crape myrtle variety trait evaluation strategy, and obtaining the genetic evaluation score of the target crape myrtle variety;

[0101] The specific steps of step S3 include:

[0102] The evaluation is conducted according to the genetic trait evaluation rules in the target crape myrtle variety trait evaluation strategy, which includes G genetic trait score ranges and corresponding gene evaluation scores; the G genetic trait score ranges and corresponding gene evaluation scores are set by professional technicians based on actual conditions;

[0103] Determine the genetic trait score range of the target crape myrtle variety gene information to be analyzed, and obtain the genetic trait score range of the target crape myrtle variety; determine the target crape myrtle variety gene evaluation score according to the genetic trait score range of the target crape myrtle variety;

[0104] For example, if the effective gene allele of genetic diversity reaches 1.52, within a certain genetic trait score range, the corresponding gene evaluation score is 90;

[0105] The genetic trait score range and gene evaluation score set by professional technicians based on actual conditions ensure the scientificity and professionalism of the evaluation; the genetic information is quantitatively scored through specific evaluation rules, which improves the accuracy and measurability of genetic trait evaluation; the score range is set for different genetic traits, allowing personalized evaluation of different target crape myrtle varieties; through genetic trait evaluation, the adaptability of different crape myrtle varieties to specific environmental conditions can be analyzed, providing a basis for the selection of planting environment;

[0106] S4. Environmental assessment of target crape myrtle varieties germplasm resources

[0107] Obtaining germplasm environmental information of the target crape myrtle variety to be analyzed; inputting the germplasm environmental information of the target crape myrtle variety to be analyzed into the crape myrtle germplasm resource environmental assessment model for auxiliary analysis to obtain an environmental assessment score of the target crape myrtle variety;

[0108] The crape myrtle germplasm resource environmental assessment model in step S4 includes an environmental information preprocessing layer, an environmental prediction layer and a result output layer;

[0109] The environmental information preprocessing layer is used to preprocess the germplasm environmental information of the target crape myrtle variety to be analyzed, and obtain the preprocessed germplasm environmental information of the target crape myrtle variety;

[0110] The environmental prediction layer is used to make predictions based on the pre-processed target crape myrtle variety germplasm environmental information to obtain the target crape myrtle variety germplasm environmental prediction information;

[0111] The result output layer is used to match the environmental assessment score of the target crape myrtle variety according to the target crape myrtle variety germplasm environmental prediction information;

[0112] The specific steps of training the environment prediction layer include:

[0113] Collecting several groups of continuous environmental prediction training samples, each group of environmental prediction training samples contains the germplasm environmental information of crape myrtle varieties; combining several groups of continuous environmental prediction training samples to obtain an environmental prediction training set; taking the latter group of environmental prediction training samples of two adjacent groups as the target value;

[0114] The environment prediction layer is trained with the target value as the target using the environment prediction training set to obtain an initial environment prediction layer; the initial environment prediction layer is evaluated to obtain an initial environment prediction layer model evaluation result; if the initial environment prediction layer model evaluation result is passed, the initial environment prediction layer is used as the environment prediction layer; otherwise, the model training is continued using the environment prediction training set;

[0115] The environmental information preprocessing layer ensures the quality and consistency of input data, providing accurate environmental information for subsequent analysis. The environmental prediction layer makes predictions based on preprocessed data, improving the accuracy of predictions on the environmental adaptability of crape myrtle germplasm. The environmental assessment score provides data-driven decision support for growers, helping them better understand the needs of crape myrtle varieties for specific environmental conditions. The environmental assessment score is trained based on the environmental adaptability of crape myrtle varieties, and predicts the future growth conditions of crape myrtle varieties based on the predicted environmental conditions to determine whether the environment needs to be improved.

[0116] S5. Comprehensive evaluation of the traits of target crape myrtle varieties

[0117] The gene evaluation score of the target crape myrtle variety, the morphological trait evaluation score set Y of the target crape myrtle variety and the environmental evaluation score of the target crape myrtle variety are comprehensively evaluated using the crape myrtle variety trait evaluation function to obtain the target crape myrtle variety trait comprehensive evaluation score; the crape myrtle variety trait evaluation function is determined using a population optimization algorithm;

[0118] The specific steps of step S5 include:

[0119] The target crape myrtle variety gene evaluation score, the target crape myrtle variety morphological trait evaluation score set Y and the target crape myrtle variety environmental evaluation score are normalized to obtain the standard target crape myrtle variety gene evaluation score, the standard target crape myrtle variety morphological trait evaluation score set Y' and the standard target crape myrtle variety environmental evaluation score;

[0120] The gene evaluation score of the standard target crape myrtle variety, the morphological trait evaluation score set Y' of the standard target crape myrtle variety and the environmental evaluation score of the standard target crape myrtle variety are input into the crape myrtle variety trait evaluation function for calculation to obtain the comprehensive evaluation score of the target crape myrtle variety trait;

[0121] Specific steps to construct the evaluation function of crape myrtle variety traits:

[0122] Construct K initialization function individuals F k , each initialization function individual F k The method includes a simulated crape myrtle variety trait evaluation function for comprehensive evaluation; a plurality of simulated crape myrtle variety comprehensive evaluation training samples are collected; each group of simulated crape myrtle variety comprehensive evaluation training samples includes function input parameters and corresponding simulation target values;

[0123] Based on the initialization function individual F k The initial fitness A is obtained by performing simulation calculations with several groups of simulated comprehensive evaluation training samples of crape myrtle varieties. k ; Initial fitness A k The calculation method is as follows: according to the function input parameters and initialization function individual F in the simulated crape myrtle variety comprehensive evaluation training sample k Perform function calculation to obtain the simulated score value; calculate the accuracy of the simulated score value and the simulated target value to obtain the simulated accuracy; take the average of several groups of simulated accuracy as the initial fitness A k ;

[0124] Based on the initialization function individual F k The initial fitness A k , initialize K function individuals F k Divide into rooster iteration population, hen iteration population and chick iteration population; set the maximum number of iterations B max ; The current number of iterations is b; The maximum number of iterations is set by professional technicians according to actual conditions; Some individuals with higher initial fitness are input into the rooster iteration population, Some individuals with medium initial fitness are input into the hen iteration population, and Some individuals with lower initial fitness are input into the chick iteration population; The crape myrtle variety trait evaluation function is determined by the group optimization algorithm. The group optimization algorithm is a chicken group optimization algorithm, which can simulate the chicken group optimization algorithm to update the population position. The rooster iteration population contains rooster individuals, the hen iteration population contains hen individuals, and the chick iteration population contains chick individuals;

[0125] During iteration, the initialization function individuals in the rooster iteration population, hen iteration population, and chicken iteration population are mutated and updated, and the current iteration number b is less than B max / 2, the chicken iteration population mutates to the initialization function individuals in the hen iteration population, and the hen iteration population mutates to the initialization function individuals in the rooster iteration population; the current iteration number b is greater than B max / 2, the chicken iteration population and the hen iteration population mutate to the initialization function individuals in the rooster iteration population;

[0126] In the iterative process, the chicks are updated to the hen position in the early stage, and the hen position is updated to the rooster position, which can maintain the breadth and diversity of the population and avoid searching for the local optimal point too early. In the later stage of the iteration, the convergence speed needs to be accelerated, and both the chicks and the hens are updated to the rooster position to maintain a high fitness level, which is conducive to the convergence of the optimal solution.

[0127] When the maximum number of iterations is reached, the output is the initialization function individual F corresponding to the maximum fitness k , which is the optimal initialization function individual; the simulated crape myrtle variety trait evaluation function in the optimal initialization function individual is used as the crape myrtle variety trait evaluation function;

[0128] Through normalization processing and comprehensive evaluation functions, a comprehensive consideration of the genes, morphological traits and environmental adaptability of Crape Myrtle varieties is achieved, providing a more comprehensive trait evaluation; the fitness is obtained by calculating the accuracy of the simulated score value and the simulated target value, which provides a basis for the selection and optimization of the evaluation function; the evaluation function is continuously updated and optimized by an iterative method, which improves the accuracy and reliability of the evaluation function; the position is updated using an improved flock optimization algorithm, which improves the efficiency and effect of searching for the optimal solution. In the early stage of iteration, the population diversity is maintained to avoid premature convergence to the local optimal solution; in the later stage of iteration, the convergence speed is accelerated by concentrating on searching high fitness areas.

[0129] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A method for evaluating the traits of Lagerstroemia indica germplasm resources, characterized in that: include: S1. Extraction of morphological traits of target crape myrtle varieties Acquire the image of the target crape myrtle variety to be analyzed; Input the target crape myrtle variety image to be analyzed into the crape myrtle variety image classification model for analysis to obtain the classification result of the target crape myrtle variety; According to the classification results of the target crape myrtle varieties, the crape myrtle germplasm resource trait evaluation database is matched to obtain the target crape myrtle variety trait evaluation strategy; the target crape myrtle variety trait evaluation strategy includes morphological trait evaluation rules and genetic trait evaluation rules; the crape myrtle germplasm resource trait evaluation database includes several crape myrtle varieties and corresponding crape myrtle variety trait evaluation strategies; Obtain a set of morphological images of target crape myrtle varieties to be analyzed; the set of morphological images of target crape myrtle varieties to be analyzed contains M morphological images P of target crape myrtle varieties to be analyzed. m , m=1,2,…,M; The morphological image set of the target crape myrtle variety to be analyzed is input into the multi-scale feature extraction model of crape myrtle varieties for feature extraction, and the morphological trait feature set X of the target crape myrtle variety is obtained, where X = [X1, X2, …, X n , …, X N ];X n represents the target crape myrtle variety morphological trait X in the target crape myrtle variety morphological trait set X n , n = 1, 2, ..., N; N is the number of morphological categories of the target crape myrtle varieties; S2. Evaluation of morphological traits of target crape myrtle varieties Based on the target crape myrtle variety trait evaluation strategy, the target crape myrtle variety morphological trait feature set X is evaluated to obtain the target crape myrtle variety morphological trait evaluation score set Y, where Y = [Y1, Y2, …, Y n , …, Y N ]; Y n Indicates the morphological characteristics of the target crape myrtle variety X n Corresponding morphological trait evaluation score Y of target crape myrtle variety n ; S3. Evaluation of genetic traits of target crape myrtle varieties Obtaining the genetic information of the target crape myrtle variety to be analyzed; evaluating the genetic information of the target crape myrtle variety to be analyzed based on the target crape myrtle variety trait evaluation strategy, and obtaining the genetic evaluation score of the target crape myrtle variety; S4. Environmental assessment of target crape myrtle varieties germplasm resources Obtaining germplasm environmental information of the target crape myrtle variety to be analyzed; inputting the germplasm environmental information of the target crape myrtle variety to be analyzed into the crape myrtle germplasm resource environmental assessment model for auxiliary analysis to obtain an environmental assessment score of the target crape myrtle variety; S5. Comprehensive evaluation of the traits of target crape myrtle varieties The gene evaluation score of the target crape myrtle variety, the morphological trait evaluation score set Y of the target crape myrtle variety and the environmental evaluation score of the target crape myrtle variety are comprehensively evaluated using the crape myrtle variety trait evaluation function to obtain the comprehensive evaluation score of the target crape myrtle variety trait; the crape myrtle variety trait evaluation function is determined using a population optimization algorithm.

2. The method for evaluating the properties of crape myrtle germplasm resources according to claim 1, characterized in that: The crape myrtle variety image classification model in step S1 includes an image preprocessing layer, an image feature extraction layer, an image classification layer and a result output layer; the image preprocessing layer is used to preprocess the target crape myrtle variety image to be analyzed to obtain a preprocessed target crape myrtle variety image; The image feature extraction layer is used to extract features from the preprocessed target crape myrtle variety image to obtain the target crape myrtle variety image features; The image classification layer is used to classify the target crape myrtle varieties according to the image features of the target crape myrtle varieties, and obtain the classification results of the target crape myrtle varieties; The result output layer is used to output the classification results of the target crape myrtle varieties; The specific steps for training the image classification layer in the image classification model for crape myrtle varieties include: Collect several groups of crape myrtle variety image classification samples; each group of crape myrtle variety image classification samples contains crape myrtle variety images and corresponding crape myrtle variety categories; use the corresponding crape myrtle variety categories in each group of crape myrtle variety image classification samples as target values; combine several groups of crape myrtle variety image classification samples to obtain a crape myrtle variety image classification training set; The crape myrtle variety image classification training set is input into the crape myrtle variety image classification model to train the image classification layer to obtain the initial image classification layer; the initial image classification layer is evaluated to obtain the initial image classification layer model evaluation result; if the initial image classification layer model evaluation result is passed, the initial image classification layer is used as the image classification layer in the crape myrtle variety image classification model; otherwise, the crape myrtle variety image classification training set is used to continue model training.

3. The method for evaluating the properties of crape myrtle germplasm resources according to claim 2, characterized in that: The multi-scale feature extraction model of crape myrtle varieties in step S1 includes an image set classification layer, N morphological feature extraction layers T n and result output layer; the image set classification layer is used to analyze the target crape myrtle variety morphological image P in the target crape myrtle variety morphological image set to be analyzed. m Classify and obtain the target crape myrtle variety morphological image classification F m ; Morphological feature extraction layer T n Corresponding to the target crape myrtle variety morphological image classification L n ; For the morphological image P of the target crape myrtle variety to be analyzed m , analysis and classification of target crape myrtle variety morphological images F m Classification of morphological images of the same target crape myrtle varieties n The corresponding morphological feature extraction layer T n , get the target morphological feature extraction layer; The morphological image P of the target crape myrtle variety to be analyzed m Input into the target morphological feature extraction layer; N morphological feature extraction layers T n It is used to extract features from the input morphological image of the target crape myrtle variety to be analyzed, and obtain the morphological trait feature X of the target crape myrtle variety. n ; In the morphological feature extraction layer T n In the process, the input morphological images of the target crape myrtle varieties to be analyzed are spliced ​​by using the image stitching method to obtain the comprehensive morphological images of the target crape myrtle varieties; the feature analysis of the comprehensive morphological images of the target crape myrtle varieties is performed to obtain the morphological trait feature X of the target crape myrtle varieties. n ; The result output layer is used to extract the N morphological features from the N morphological features. n Output of the target crape myrtle variety morphological characteristics X n Combination, to obtain the target crape myrtle variety morphological trait feature set X, X = [X1, X2, ..., X n , …, X N ], output the morphological trait feature set X of the target crape myrtle variety.

4. The method for evaluating the properties of crape myrtle germplasm resources according to claim 3, characterized in that: Train the morphological feature extraction layer T n The specific steps include: For the morphological feature extraction layer T n : Collect several sets of target crape myrtle variety morphological image training samples, each set of target crape myrtle variety morphological image training samples contains the target crape myrtle variety morphological image classification L n corresponding target crape myrtle variety morphological images and corresponding image features; combining a plurality of target crape myrtle variety morphological image training samples to obtain a target crape myrtle variety morphological image training set; The target crape myrtle variety morphological image training set is input into the morphological feature extraction layer T n In the training, the image features are used as the target to obtain the initial morphological feature extraction layer C n ; Extract the initial morphological features from layer C n Perform model evaluation to obtain the initial morphological feature extraction layer C n Model evaluation results; if the initial morphological feature extraction layer C n If the model evaluation result is passed, the initial morphological feature extraction layer C n As the morphological feature extraction layer T n ; Otherwise, continue model training using the target crape myrtle variety morphological image training set; Traverse all morphological feature extraction layers T n , until N morphological feature extraction layers T are constructed n .

5. The method for evaluating the properties of crape myrtle germplasm resources according to claim 4, characterized in that: The specific steps of step S2 include: Evaluate according to the morphological trait evaluation rules in the target crape myrtle variety trait evaluation strategy, where the morphological trait evaluation rules include a morphological trait evaluation rule set; Targeted crape myrtle variety morphological characteristics X n : Match the morphological characteristics of the target crape myrtle variety X n The morphological trait evaluation rule set with the largest membership function is obtained, and the morphological trait evaluation rule set J is obtained. n ; Based on the morphological trait evaluation rule set J n Assign the morphological trait evaluation score Y of the target crape myrtle variety n ; X is the morphological characteristics of all target crape myrtle varieties n Evaluate and score all target crape myrtle varieties with morphological traits Y n Combination, to obtain the target crape myrtle variety morphological trait evaluation score set Y, Y = [Y1, Y2, ..., Y n , …, Y N ].

6. The method for evaluating the properties of crape myrtle germplasm resources according to claim 5, characterized in that: The specific steps of step S3 include: The target crape myrtle variety is evaluated according to the genetic trait evaluation rules in the trait evaluation strategy, which includes G genetic trait score ranges and corresponding gene evaluation scores; Determine the genetic trait score range of the target crape myrtle variety's genetic information to be analyzed, and obtain the genetic trait score range of the target crape myrtle variety; determine the target crape myrtle variety's genetic evaluation score based on the genetic trait score range of the target crape myrtle variety.

7. The method for evaluating the properties of crape myrtle germplasm resources according to claim 6, characterized in that: The crape myrtle germplasm resource environmental assessment model in step S4 includes an environmental information preprocessing layer, an environmental prediction layer and a result output layer; The environmental information preprocessing layer is used to preprocess the germplasm environmental information of the target crape myrtle variety to be analyzed, and obtain the preprocessed germplasm environmental information of the target crape myrtle variety; The environmental prediction layer is used to make predictions based on the pre-processed target crape myrtle variety germplasm environmental information to obtain the target crape myrtle variety germplasm environmental prediction information; The result output layer is used to match the environmental assessment score of the target crape myrtle variety according to the target crape myrtle variety germplasm environmental prediction information; The specific steps of training the environment prediction layer include: Collecting several groups of continuous environmental prediction training samples, each group of environmental prediction training samples contains the germplasm environmental information of crape myrtle varieties; combining several groups of continuous environmental prediction training samples to obtain an environmental prediction training set; taking the latter group of environmental prediction training samples of two adjacent groups as the target value; The environment prediction layer is trained with the target value as the target using the environment prediction training set to obtain an initial environment prediction layer; the initial environment prediction layer is evaluated to obtain an initial environment prediction layer model evaluation result; if the initial environment prediction layer model evaluation result is passed, the initial environment prediction layer is used as the environment prediction layer; otherwise, the model training is continued using the environment prediction training set.

8. The method for evaluating the properties of crape myrtle germplasm resources according to claim 7, characterized in that: The specific steps of step S5 include: The target crape myrtle variety gene evaluation score, the target crape myrtle variety morphological trait evaluation score set Y and the target crape myrtle variety environmental evaluation score are normalized to obtain the standard target crape myrtle variety gene evaluation score, the standard target crape myrtle variety morphological trait evaluation score set Y' and the standard target crape myrtle variety environmental evaluation score; The gene evaluation score of the standard target crape myrtle variety, the morphological trait evaluation score set Y' of the standard target crape myrtle variety and the environmental evaluation score of the standard target crape myrtle variety are input into the crape myrtle variety trait evaluation function for calculation to obtain the comprehensive evaluation score of the target crape myrtle variety trait; Specific steps to construct the evaluation function of crape myrtle variety traits: Construct K initialization function individuals F k , each initialization function individual F k The method includes a simulated crape myrtle variety trait evaluation function for comprehensive evaluation; a plurality of simulated crape myrtle variety comprehensive evaluation training samples are collected; each group of simulated crape myrtle variety comprehensive evaluation training samples includes function input parameters and corresponding simulation target values; Based on the initialization function individual F k The initial fitness A is obtained by performing simulation calculations with several groups of simulated comprehensive evaluation training samples of crape myrtle varieties. k ; Initial fitness A k The calculation method is as follows: according to the function input parameters and initialization function individual F in the simulated crape myrtle variety comprehensive evaluation training sample k Perform function calculation to obtain the simulated score value; calculate the accuracy of the simulated score value and the simulated target value to obtain the simulated accuracy; take the average of several groups of simulated accuracy as the initial fitness A k ; Based on the initialization function individual F k The initial fitness A k , initialize K function individuals F k Divide into rooster iteration population, hen iteration population and chick iteration population; set the maximum number of iterations B max ; The current iteration number is b; During iteration, the initialization function individuals in the rooster iteration population, hen iteration population, and chicken iteration population are mutated and updated, and the current iteration number b is less than B max / 2, the chicken iteration population mutates to the initialization function individuals in the hen iteration population, and the hen iteration population mutates to the initialization function individuals in the rooster iteration population; the current iteration number b is greater than B max / 2, the chicken iteration population and the hen iteration population mutate to the initialization function individuals in the rooster iteration population; When the maximum number of iterations is reached, the output is the initialization function individual F corresponding to the maximum fitness k , which is the optimal initialization function individual; the simulated crape myrtle variety trait evaluation function in the optimal initialization function individual is used as the crape myrtle variety trait evaluation function.

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