Bellamya piriforme meat yield evaluation method, equipment, medium and product

Abstract

The invention discloses a method and equipment for evaluating the meat yield of Bellamya piriforme, a medium and a product. The method comprises the following steps: acquiring a morphological data set of the Bellamya piriformis; the morphological data set of the Bellamya piriformis comprises character parameters of a plurality of sample Bellamya piriformis individuals and corresponding soft body weights; constructing an optimal KNN prediction model based on the morphological data set of the Bellamya piriformis; the optimal KNN prediction model is used for predicting the software weight according to the character parameters of the individual Bellamya piriformis; inputting the character parameters of the target Bellamya piriformis individual into the optimal KNN prediction model to obtain a soft weight prediction value of the target Bellamya piriformis individual; and dividing the soft body weight prediction value of the target Bellamya piriforme individual by the body weight to obtain the meat percentage prediction value of the target Bellamya piriforme individual. According to the method, nondestructive evaluation of the meat percentage of the Bellamya piriforme can be realized, and the meat percentage of the Bellamya piriforme can be predicted under the condition that the Bellamya piriforme survives.

Description

Technical Field

[0001] The present application relates to the field of aquatic breeding, and in particular to an evaluation method, equipment, medium and product for the meat yield of Pear-shaped Ring-ribbed Snails. Background Art

[0002] The pear-shaped ring-edge snail is one of the main raw materials for the best-selling food "snail noodles". It has high economic value and is now widely cultivated in many places. In order to further develop the scale and factory-based farming of pear-shaped ring-edge snails, it is necessary to carry out breeding work on pear-shaped ring-edge snails. At present, the chromosome-level genome assembly of pear-shaped ring-edge snails has been completed, and the molecular genetics, genetic parameters of geometric morphological growth traits, and the correlation between morphological traits and body weight of pear-shaped ring-edge snails have been studied. The meat yield of pear-shaped ring-edge snails is one of the important indicators in breeding, but the existing method of measuring the meat yield will cause the death of pear-shaped ring-edge snails, which is not conducive to the subsequent breeding work. Summary of the invention

[0003] The purpose of the present application is to provide a method, device, medium and product for evaluating the meat yield of pear-shaped ring-ribbed snails, which can realize non-destructive evaluation of the meat yield of pear-shaped ring-ribbed snails and predict the meat yield of pear-shaped ring-ribbed snails when the snails are alive.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for evaluating the meat yield of Pear-shaped Ring Edged Snails, comprising:

[0006] Acquire a morphological data set of Pyriformis ring-edge snails; the morphological data set of Pyriformis ring-edge snails includes: trait parameters and corresponding soft weights of several sample Pyriformis ring-edge snails;

[0007] An optimal KNN prediction model is constructed based on the morphological data set of Pyriformis ring-edge snails; the optimal KNN prediction model is used to predict the soft body weight according to the trait parameters of Pyriformis ring-edge snail individuals;

[0008] The trait parameters of the target Pyriformis ring-edge snail individual are input into the optimal KNN prediction model to obtain the soft body weight prediction value of the target Pyriformis ring-edge snail individual;

[0009] Divide the predicted value of the soft body weight of the target Pyriform Ring-ribbed Snail individual by its body weight to obtain the predicted value of the meat yield of the target Pyriform Ring-ribbed Snail individual.

[0010] Optionally, the trait parameters include: body weight, shell width, shell height, shell opening width, shell opening height, body whorl height, spiral part height and gender.

[0011] Optionally, an optimal KNN prediction model is constructed based on the morphological data set of Pyriformis sphenanthera, including:

[0012] The data with missing values ​​in the morphological data set of the pear-shaped ring-edge snail were deleted to obtain the training set;

[0013] Build the optimal KNN prediction model based on the training set.

[0014] Optionally, data containing missing values ​​in the morphological data set of Pyriformis annularis are deleted to obtain a training set, including:

[0015] The data containing missing values ​​in the pear-shaped ring-edge snail morphological data set are deleted, and the deleted pear-shaped ring-edge snail morphological data set is divided into a training set and a test set in a ratio of 8:2; the training set is used to construct the optimal KNN prediction model; and the test set is used to test the optimal KNN prediction model.

[0016] Optionally, an optimal KNN prediction model is constructed based on the training set, including:

[0017] Select multiple K values ​​to build corresponding KNN prediction models respectively;

[0018] The training set was divided into ten parts on average, and the ten-fold cross validation method was used to train and validate each KNN prediction model;

[0019] The prediction accuracy of each KNN prediction model is evaluated, and the KNN prediction model with the highest prediction accuracy is taken as the optimal KNN prediction model.

[0020] Optionally, the multiple K values ​​are 5, 7 and 9 respectively.

[0021] Optionally, the root mean square error is used to evaluate the prediction accuracy of each KNN prediction model, and the KNN prediction model with the smallest root mean square error is taken as the optimal KNN prediction model.

[0022] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for evaluating the meat yield of the pear-shaped ring-ribbed snail.

[0023] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for evaluating the meat yield of the pear-shaped ring-ribbed snail.

[0024] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for evaluating the meat yield of the pear-shaped ring-ribbed snail.

[0025] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0026] The present application provides a method, equipment, medium and product for evaluating the meat yield of pear-shaped ring-ribbed snails. By constructing an optimal KNN prediction model, the soft body weight of the pear-shaped ring-ribbed snails can be predicted according to the trait parameters of the individual pear-shaped ring-ribbed snails, and then the meat yield can be calculated. There is no need to take the ventral foot for weighing, thereby achieving non-destructive evaluation of the meat yield of the pear-shaped ring-ribbed snails and accurately predicting the meat yield of the pear-shaped ring-ribbed snails when they are alive, which is helpful for the breeding of pear-shaped ring-ribbed snails. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A flow chart of the method for evaluating the meat yield of the pear-shaped ring-edge snail provided in this application;

[0029] Figure 2 A detailed flow chart of the method for evaluating the meat yield of the pear-shaped ring-edge snail provided in this application;

[0030] Figure 3 Flowchart of KNN prediction model construction and prediction provided for this application;

[0031] Figure 4 This is a graph showing the difference between the predicted values ​​and the true values ​​obtained from the test set provided in this application. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] In an exemplary embodiment, the present application provides a method for evaluating the meat yield of pear-shaped ring-edge snails, which is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, Figure 1 and Figure 2 As shown, the method includes the following steps 1 to 4.

[0035] Step 1: Obtain a morphological dataset of Pyriformis scutellariae. The morphological dataset of Pyriformis scutellariae includes: trait parameters and corresponding soft weights of several sample Pyriformis scutellariae individuals.

[0036] Preferably, the trait parameters include: body weight, shell width, shell height, shell opening width, shell opening height, body whorl height, spiral part height and gender.

[0037] Step 2: construct an optimal KNN prediction model based on the morphological data set of the pear-shaped ring-shaped snail. The optimal KNN prediction model is used to predict the soft body weight according to the trait parameters of the pear-shaped ring-shaped snail individuals.

[0038] Specifically, the data with missing values ​​in the morphological dataset of Pyriformis annularis was first deleted, and the deleted morphological dataset was divided into a training set and a test set in a ratio of 8:2; then the optimal KNN prediction model was constructed based on the training set, and the test set was used to test the optimal KNN prediction model.

[0039] Step 3: Input the trait parameters of the target Pyriformis ring-edge snail individual into the optimal KNN prediction model to obtain the soft body weight prediction value of the target Pyriformis ring-edge snail individual.

[0040] Specifically, Figure 3 As shown in the figure, firstly, multiple K values ​​are selected to construct corresponding KNN prediction models (KNN models); secondly, the training set is divided into ten parts on average, and the ten-fold cross validation method (ten-fold cross validation method) is used to train and verify each KNN prediction model; finally, the prediction accuracy of each KNN prediction model is evaluated, and the KNN prediction model with the highest prediction accuracy is taken as the optimal KNN prediction model.

[0041] Preferably, the multiple K values ​​are 5, 7 and 9 respectively.

[0042] Preferably, the root mean square error (RMSE) is used to evaluate the prediction accuracy of each KNN prediction model, and the KNN prediction model with the smallest root mean square error is taken as the optimal KNN prediction model.

[0043] In addition, R-squared (R 2 ), Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Relative Error (MARE) are used to evaluate the prediction accuracy of each KNN prediction model, but are not limited to this.

[0044] Step 4: Divide the predicted value of the soft body weight of the target Pyriform Ring-rimmed Snail individual by its body weight to obtain the predicted value of the meat yield of the target Pyriform Ring-rimmed Snail individual.

[0045] Furthermore, the method further comprises:

[0046] Step 5: Calculate the R of the optimal KNN prediction model based on the test set 2 , MSE, MAE, MARE, the formulas are as follows:

[0047]

[0048] Among them, m is the number of samples in the test set, i is the sample index, and y i is the true value of the soft weight of sample i, is the average soft weight of sample i, is the predicted value of the soft weight of sample i.

[0049] A specific embodiment is provided below to explain the above technical solution of the present application in detail.

[0050] (1) Collect samples of Pyriform Ring Edge Snails from various locations, measure and record the weight, shell width, shell height, shell mouth width, shell mouth height, body whorl height, and spiral height of the collected Pyriform Ring Edge Snails. Remove the shell of the Pyriform Ring Edge Snails and record the sex of the Pyriform Ring Edge Snails (or determine its sex based on its external morphology). Take the Pyriform Ring Edge Snails' ventral foot, weigh it, and record the soft body weight of the Pyriform Ring Edge Snails.

[0051] (2) The data were imported into RStudio4.3.2, and a total of 2072 data were obtained after removing the data with missing values. All the data were divided into two parts, 80% (1452 records) were used for model construction, and 20% (620 records) were used for model testing.

[0052] (3) The caret package was used in RStudio4.3.2 to construct a KNN prediction model. The body weight, shell width, shell height, shell mouth width, shell mouth height, body whorl height, spiral height, and sex of the pear-shaped ring-shaped snail were used as independent variables, and the soft body weight was used as the dependent variable. Three KNN prediction models were constructed with K values ​​of 5, 7, and 9, respectively.

[0053] (4) The training set is divided into ten parts, each of which is used as a validation subset, and the others are used as training subsets to train and validate the above three KNN prediction models. The RMSE of the ten validations is calculated and the average is taken. The RMSE of the three KNN prediction models are 0.433, 0.432, and 0.431 respectively. The model with the lowest RMSE, that is, the model with K=9, is selected as the optimal KNN prediction model.

[0054] (5) Use the test set data to test each KNN prediction model, and plot the predicted value as the y-coordinate and the true value as the x-coordinate. Figure 4 The gap diagram shows that the predicted values ​​and the true values ​​are concentrated on the y=x line, and the prediction results are valid. 2 , RMSE, MAE, and MARE are 0.82, 0.24, 0.16, and -4.63%, respectively.

[0055] The implementation of the method for evaluating the meat yield of the pear-shaped ring-edge snail provided in the present application has the following beneficial effects:

[0056] 1. This application can evaluate the meat yield rate of Pyriform Ring Edge Snails while the Pyriform Ring Edge Snails are alive.

[0057] 2. The KNN prediction model constructed in this application has the characteristics of simple structure, easy application, and fast response, and has strong stability in practical applications.

[0058] In an exemplary embodiment, the present application further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0059] In an exemplary embodiment, the present application further provides a computer-readable storage medium storing a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0060] In an exemplary embodiment, the present application further provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0061] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the corresponding device owner. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant legal provisions.

[0062] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0063] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0064] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for evaluating the meat yield of Pear-shaped Ring-edge Snails, characterized in that: include: Obtain the morphological dataset of Pyriformis sphenanthera; The morphological data set of the pear-shaped ring-edge snail includes: the trait parameters and corresponding soft weights of several sample pear-shaped ring-edge snail individuals; An optimal KNN prediction model is constructed based on the morphological data set of Pyriformis ring-edge snails; the optimal KNN prediction model is used to predict the soft body weight according to the trait parameters of Pyriformis ring-edge snail individuals; The trait parameters of the target Pyriformis ring-edge snail individual are input into the optimal KNN prediction model to obtain the soft body weight prediction value of the target Pyriformis ring-edge snail individual; Divide the predicted value of the soft body weight of the target Pyriform Ring-ribbed Snail individual by its body weight to obtain the predicted value of the meat yield of the target Pyriform Ring-ribbed Snail individual.

2. The method for evaluating the meat yield of Pyriform Ringed Snail according to claim 1, characterized in that: The trait parameters include: body weight, shell width, shell height, shell mouth width, shell mouth height, body whorl height, spiral part height and sex.

3. The method for evaluating the meat yield of Pyriform Ringed Snail according to claim 1, characterized in that: The optimal KNN prediction model is constructed based on the morphological dataset of Pyriformis sphenanthera, including: The data with missing values ​​in the morphological data set of the pear-shaped ring-edge snail were deleted to obtain the training set; Build the optimal KNN prediction model based on the training set.

4. The method for evaluating the meat yield of Pyriform Ringed Snail according to claim 3, characterized in that: Delete the data with missing values ​​in the morphological data set of Pyriform Ring Edge Snail to obtain the training set, including: The data containing missing values ​​in the pear-shaped ring-edge snail morphological data set are deleted, and the deleted pear-shaped ring-edge snail morphological data set is divided into a training set and a test set in a ratio of 8:2; the training set is used to construct the optimal KNN prediction model; and the test set is used to test the optimal KNN prediction model.

5. The method for evaluating the meat yield of Pyriform Ringed Snail according to claim 3, characterized in that: Build the optimal KNN prediction model based on the training set, including: Select multiple K values ​​to build corresponding KNN prediction models respectively; The training set was divided into ten parts on average, and the ten-fold cross validation method was used to train and validate each KNN prediction model; The prediction accuracy of each KNN prediction model is evaluated, and the KNN prediction model with the highest prediction accuracy is taken as the optimal KNN prediction model.

6. The method for evaluating the meat yield of Pyriform Ringed Snail according to claim 5, characterized in that: The multiple K values ​​are 5, 7 and 9 respectively.

7. The method for evaluating the meat yield of Pyriformis ring-edge snails according to claim 5, characterized in that: The root mean square error is used to evaluate the prediction accuracy of each KNN prediction model, and the KNN prediction model with the smallest root mean square error is taken as the optimal KNN prediction model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for evaluating the meat yield of the pear-shaped ring-ribbed snail described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the meat yield of the pear-shaped ring-ribbed snail described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating the meat yield of the pear-shaped ring-ribbed snail described in any one of claims 1 to 7 is implemented.

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