CN118155758A - Pod physical characteristic-based easy shelling evaluation method, device and storage medium - Google Patents
Pod physical characteristic-based easy shelling evaluation method, device and storage medium Download PDFInfo
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Abstract
The invention discloses a pod physical characteristic-based easy shellability evaluation method, a pod physical characteristic-based easy shellability evaluation device and a storage medium, wherein the method comprises the steps of acquiring a database of experimental data of pods containing a plurality of varieties; performing a first round of data analysis on the measured data of the physical features based on the measured data of the quality index to obtain the physical features which are strongly related to the quality index and are called first physical features; performing a second round of data analysis on the measured data of the first physical features based on the measured data of the quality index in the database to divide all the first physical features into N groups, N being an integer greater than 1; selecting at least one representative physical feature from each set of said first physical features, referred to as a second physical feature; and constructing an evaluation model based on the second physical characteristics. The method can effectively select the most representative physical characteristics from a plurality of physical characteristics to construct an evaluation model for evaluating pod shellability.
Description
Technical Field
The invention relates to the technical field of attribute evaluation of agricultural materials, in particular to an easy-shelling property evaluation method and device based on pod physical characteristics and a storage medium.
Background
The pods such as peanuts and walnuts can be used as food, also important oil raw materials, when the pods are subjected to oil extraction processing, the shells of the pods need to be removed, and the obtained kernels are subjected to subsequent processing, so that the easy shellability is an important characteristic of the pods, and the easy shellability of the pods needs to be fully considered in the selection of the oil raw materials and the agricultural cultivation process.
In the prior art, when the pod with unknown easy shelling property is evaluated, the actual measurement of shelling is mainly performed in an experimental mode, and the evaluation is performed according to the result of the shelling experiment, in the method, enough samples are needed to be tested, in the testing process, repeated process parameter adjustment is needed to be performed on the shelling machine to achieve the optimal shelling effect (for example, the real-time process parameter adjustment according to the shelling effect is disclosed in the prior application CN115318633a of the applicant), so that the obtained result can be used as a reference, and the efficiency is very low.
In the aspect of theoretical research, research on the easy dehulling property is generally focused on biology angles such as genetics, and the evaluation of the difficulty of quantification on the pod with unknown easy dehulling property still belongs to the blank.
Disclosure of Invention
The invention aims to: in order to overcome the defects in the prior art, the invention provides a method, a device and a storage medium for evaluating the shellability of pods based on the physical characteristics of the pods, which can extract the physical characteristics of the cores and construct an evaluation model according to the physical characteristics of the cores so as to facilitate quantitative evaluation of the pods.
The technical scheme is as follows: to achieve the above object, the method for evaluating the easy-to-dehull based on the physical characteristics of pods of the present invention comprises:
Acquiring a database of experimental data comprising pods of a plurality of varieties, wherein the experimental data comprises variety information, measured data of a plurality of physical characteristics and measured data of quality indexes;
Performing a first round of data analysis on the measured data of the physical features based on the measured data of the quality index to obtain the physical features which are strongly related to the quality index and are called first physical features;
performing a second round of data analysis on the measured data of the first physical features based on the measured data of the quality index in the database to divide all the first physical features into N groups, N being an integer greater than 1;
Selecting at least one representative physical feature from each set of said first physical features, referred to as a second physical feature;
And constructing an evaluation model based on the second physical characteristics.
Preferably, the first round of data analysis is performed based on a correlation analysis, specifically including:
counting the frequency of all the physical characteristics in the database according to actual measurement data;
And determining the first physical characteristic according to the result of the frequency statistics and the measured data of the quality index and the correlation analysis.
Preferably, the performing a second round of data analysis on the measured data of the first physical features based on the measured data of the quality index in the database to divide all the first physical features into N groups includes:
performing a second round of data analysis on the measured data of the first physical features based on a factor analysis method to form N influence factors and the first physical features corresponding to the influence factors;
and taking the first physical characteristic corresponding to each influence factor as a group to obtain N groups.
Preferably, said selecting at least one representative physical feature from each set of said first physical features, referred to as a second physical feature, comprises:
The first physical feature with the greatest factor weight in each group is selected as the representative physical feature, i.e., the second physical feature.
Preferably, the constructing an evaluation model based on the second physical characteristic includes:
Distributing weights to the second physical characteristics, and calculating the comprehensive score of the pods of each variety according to the weights and the specific numerical values of the second physical characteristics corresponding to the pods of each variety;
Classifying peanuts of all varieties based on the comprehensive score to obtain three grades of varieties which are respectively three grades of easy shelling, medium difficulty and difficult shelling;
And constructing a grading function corresponding to each grade by adopting actual measurement data of the second physical characteristics of the variety corresponding to each grade, and waiting for the evaluation model by comprehensively obtaining all the grading functions.
An easy-to-dehull evaluation device based on pod physical characteristics, comprising:
An acquisition module for acquiring a database of experimental data comprising pods of a plurality of varieties, the experimental data comprising variety information, measured data of a plurality of physical characteristics, and measured data of quality indicators;
The first analysis module is used for carrying out first round data analysis on the measured data of the physical characteristics based on the measured data of the quality indexes to obtain the physical characteristics which are strongly related to the quality indexes and are called first physical characteristics;
The second analysis module is used for carrying out second-round data analysis on the measured data of the first physical features based on the measured data of the quality indexes in the database so as to divide all the first physical features into N groups, wherein N is an integer greater than 1;
A selection module for selecting at least one representative physical feature, called a second physical feature, from each set of the first physical features;
And a model construction module for constructing an evaluation model based on the second physical characteristics.
A storage medium having stored therein a computer executable program which when executed by a processor implements the pod physical characteristic-based easy-to-dehulling assessment method described above.
The beneficial effects are that: the pod physical characteristic-based easy shelling evaluation method, device and storage medium have the following beneficial effects:
(1) According to the invention, the first screening is performed based on the correlation between the physical characteristics and the quality indexes, the physical characteristics with information overlapping are divided into the same group, and the representative physical characteristics are selected for each group, so that a plurality of physical characteristics can be screened to obtain the most representative physical characteristics with a small number, namely the second physical characteristics, so that the pod shellability can be fully estimated based on the evaluation model constructed by the obtained second physical characteristics, the complexity of the model is low, and the pod shellability can be estimated by testing a limited number of physical characteristics of the pods to be estimated by a user, thereby providing convenience for research such as development of new varieties.
(2) The method fully utilizes the thought that factor analysis reduces the dimension of a plurality of influencing factors to form a few influencing factors, groups the first physical characteristics, effectively ensures that the first physical characteristics in the same group have information overlapping, and provides a basis for further simplification.
(3) The pods of each variety are scored based on the second physical characteristics, the varieties are graded based on the comprehensive scores obtained by scoring, and finally a grading function is obtained based on the second physical characteristic data of the varieties of each grade, and pod varieties with unknown easy shellability can be effectively predicted based on the obtained evaluation model.
Drawings
FIG. 1 is a flow chart of a method for evaluating pod physical characteristics based on easy-to-dehulling;
FIG. 2 is a schematic diagram showing the constitution of the pod physical characteristic-based easy-to-dehulling evaluation device.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
In this embodiment, peanut is taken as an example.
The method for evaluating the easy shellability based on the physical characteristics of the pods as shown in fig. 1 comprises the following steps S101 to S105:
step S101, acquiring a database of experimental data of pods containing a plurality of varieties, wherein the experimental data comprises variety information, measured data of a plurality of physical characteristics and measured data of quality indexes;
In this step, 29 varieties of peanuts are selected for data collection to obtain the database, and the statistical physical characteristics include pod sphericity, pod uniformity, pod mechanical properties, pod average geometric diameter, kernel sphericity, kernel uniformity, kernel mechanical properties, kernel average diameter, pod mouth and the like, and all the data possibly affecting quality indexes should be counted in the database for screening. The measured data of the physical characteristics can be numerical values or text descriptions; such as: the average geometric diameter of the pods, the mechanical properties of the kernels, the average diameter of the kernels and the measured data of the mechanical properties of the kernels are specific numerical values; the physical characteristics of the kernel shape, the pod mouth and the like are written description, the pod mouth shape comprises four kinds of shapes including a cylinder shape, a cone shape, an oval shape and a triangle shape, and the pod mouth comprises four kinds of shapes including a none, a short, a medium and a sharp. The quality indexes comprise breakage rate and threshing rate, and the measured data of the breakage rate and the threshing rate of each variety of peanuts are optimized results obtained by performing a shelling test on the variety of peanuts, so that the measured data of the quality indexes have sufficient credibility.
Step S102, carrying out first round data analysis on the measured data of the physical characteristics based on the measured data of the quality indexes to obtain the physical characteristics which are strongly related to the quality indexes and are called first physical characteristics;
Step S103, performing a second round of data analysis on the measured data of the first physical features based on the measured data of the quality indexes in the database so as to divide all the first physical features into N groups, wherein N is an integer greater than 1;
Step S104, selecting at least one representative physical feature from each group of the first physical features, namely a second physical feature;
and step S105, constructing an evaluation model based on the second physical characteristics.
In the step S103, the first physical features included in the same group and having information overlapping, and the step S104 selects the representative first physical features in each group, so as to further screen out the physical features that can most represent the easy shelling property of the peanuts.
The method in the steps S101-S105 performs the first round of screening based on the correlation between the physical characteristics and the quality index, divides the physical characteristics with information overlapping into the same group, selects the representative physical characteristics for each group, and can screen numerous physical characteristics to obtain the most representative physical characteristics with a smaller number, namely the second physical characteristics, so that the evaluation model constructed based on the obtained second physical characteristics can sufficiently estimate the easy shelling property of the pods, and the complexity of the model is low, and the user can estimate the easy shelling property of the pods by testing a limited number of physical characteristics of the pods to be evaluated, thereby providing convenience for research such as developing new varieties.
Preferably, in the step S102, the first round of data analysis is performed based on correlation analysis, and specifically includes the following steps S201 to S202:
Step S201, counting the frequency of all the physical features in the database according to actual measurement data;
In the step, for the physical characteristics with specific values, frequency statistics is carried out according to the value intervals, for example, a plurality of data intervals can be determined according to the mechanical characteristics of seeds, the number of the specific values falling into each data interval in a database is counted, and the proportion of the number of the specific values corresponding to each data interval relative to the total number is calculated; for the physical characteristics of the actual measurement data which are literal descriptions, frequency statistics and proportion calculation are carried out according to each literal description, for example, for the shape of the kernel, the frequency of four shapes of a cylinder, a cone, an ellipse and a triangle are respectively counted, and the proportion is calculated.
Step S202, determining the first physical characteristic according to the result of the frequency statistics and the measured data of the quality index and the correlation analysis.
In this example, the physical characteristics associated with breakage and removal rate may be determined by correlation analysis, including pod sphericity, pod uniformity, pod mechanical properties, constriction, pod fullness, geometric mean diameter, pod reticulation, pod shape, pod mouth, kernel sphericity, kernel uniformity, kernel mechanical properties, and kernel shape.
Preferably, in the step S103, the second round of data analysis is performed on the measured data of the first physical feature based on the measured data of the quality index in the database, so as to divide all the first physical features into N groups, including the following steps S301 to S302:
Step S301, performing a second round of data analysis on the measured data of the first physical features based on a factor analysis method to form N influence factors and the first physical features corresponding to the influence factors;
Step S302, taking the first physical feature corresponding to each influence factor as a group, to obtain N groups.
In the steps S301-S302, the idea that the factor analysis reduces the dimensions of a plurality of influencing factors to form a few influencing factors is fully utilized, the first physical features are grouped, so that the first physical features in the same group have information overlapping, and a foundation is provided for further simplification.
In this embodiment, five influence factors are obtained through factor analysis, and first physical features corresponding to the five influence factors are as follows: a first influencing factor corresponding to pod sphericity, pod uniformity, pod mechanical properties, and constriction, the factor weights of the four being 0.952, 0.913, -0.852, and-0.775, respectively; a second influencing factor, corresponding to the pod turgor and the average geometric diameter of the pods, the factor weights of which are 0.806 and-0.793 respectively; a third influencing factor corresponding to pod reticulation and pod shape with factor weights of 0.784 and 0.704, respectively; a fourth influencing factor, corresponding to a pod mouth, having a factor weight of 0.974; and fifth influencing factors, corresponding to the sphericity, the uniformity, the mechanical properties and the shape of the kernels, wherein the weights of the factors are respectively 0.949, 0.979, 0.976 and-0.691. Correspondingly, all the first physical features are divided into 5 groups, and the first physical features corresponding to each influence factor are individually divided into one group.
Preferably, the selecting at least one representative physical feature from each set of the first physical features in step S104, which is called a second physical feature, includes:
The first physical feature with the greatest factor weight in each group is selected as the representative physical feature, i.e., the second physical feature.
In this example, the five second physical characteristics obtained are pod sphericity, pod plumpness, pod reticulation, pod mouth and kernel sphericity, respectively.
Preferably, the constructing the evaluation model based on the second physical characteristic in the step S105 includes the following steps S401 to S403:
Step S401, weight is distributed to each second physical characteristic, and the comprehensive score of the pods of each variety is calculated according to the weight and the specific numerical value of the second physical characteristic corresponding to the pods of each variety;
step S402, classifying peanuts of all varieties based on the comprehensive scores to obtain three grades of varieties which are respectively three grades of easy shelling, medium difficulty and difficult shelling;
And S403, constructing a grading function corresponding to the grade by adopting actual measurement data of the second physical characteristics of the varieties corresponding to each grade, and waiting for the evaluation model by comprehensively obtaining all the grading functions.
In the steps S401 to S403, the pods of each variety are scored based on the second physical characteristics, the variety is graded based on the comprehensive score obtained by the scoring, and finally a grading function is obtained based on the second physical characteristic data of the variety of each grade, and the pod variety with unknown easy shellability can be effectively predicted based on the obtained evaluation model.
The above-mentioned classification function is in the form of:
Ym＝Am0+Am1X1+Am2X2+Am3X3+Am4X4+Am5X5, Wherein m=1, 2, 3; a m0 is the 0 th coefficient in the m-th level, a m1 is the 1 st coefficient in the m-th level, and so on; x 1-X5 is the measured value of the second physical characteristic, namely, the measured values of pod sphericity, pod plumpness, pod reticulation, pod mouth and kernel sphericity, respectively.
The visual evaluation model comprises 3 grading functions, when the evaluation model is actually used for predicting the easy shelling property of the peanut variety with unknown shelling property, five values of the pod sphericity, the pod plumpness, the pod reticulation, the pod mouth and the kernel sphericity of the peanut are measured and substituted into each grading function to obtain a specific value of Y 1、Y2、Y3, and then the specific value of Y 1、Y2、Y3 is compared with the specific value of Y 1, when Y 1 is the largest, the representative grade is easy shelling, when Y 2 is the largest, the representative grade is medium difficulty, and when Y 3 is the largest, the representative grade is difficult shelling.
The invention also provides a pod physical characteristic-based easy-shelling property evaluation device 500, wherein the easy-shelling property evaluation device 500 can comprise or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to complete the invention, and the pod physical characteristic-based easy-shelling property evaluation method can be realized. Program modules in accordance with the embodiments of the present invention may be referred to as a series of computer program instruction segments capable of performing a particular function, and may be more suitable than the program itself for describing the execution of the pod physical characteristic-based method of evaluating the easy-to-dehulling in a storage medium. The following description will specifically describe functions of each program module of the present embodiment, and as shown in fig. 2, the easy-peel evaluating apparatus 500 includes:
An acquisition module 501 for acquiring a database of experimental data comprising pods of a plurality of varieties, the experimental data comprising variety information, measured data of a plurality of physical characteristics, and measured data of quality indicators;
A first analysis module 502, configured to perform a first round of data analysis on measured data of the physical feature based on measured data of the quality index, to obtain the physical feature that is strongly related to the quality index, which is called a first physical feature;
a second analysis module 503, configured to perform a second round of data analysis on the measured data of the first physical features based on the measured data of the quality index in the database, so as to divide all the first physical features into N groups, where N is an integer greater than 1;
A selection module 504 for selecting at least one representative physical feature, called a second physical feature, from each set of the first physical features;
A model construction module 505 for constructing an evaluation model based on the second physical characteristics.
The details of other embodiments of the method for evaluating the pod physical characteristics based on the pod peeling property evaluating device 500 are described in detail in the previous embodiments, and reference may be made to the corresponding details in the previous embodiments, which are not described herein.
The present embodiment also provides a computer-readable storage medium such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, etc., on which a computer program is stored, which when executed by a processor, performs the corresponding functions. The computer-readable storage medium of the present embodiment is used for storing the easy-dehulling-property-evaluating device 500, which when executed by a processor implements the pod physical-characteristic-based easy-dehulling-property-evaluating method of the present invention.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (7)
1. The method for evaluating the shellability of the pods based on the physical characteristics of the pods is characterized by comprising the following steps of: the method comprises the following steps:
Acquiring a database of experimental data comprising pods of a plurality of varieties, wherein the experimental data comprises variety information, measured data of a plurality of physical characteristics and measured data of quality indexes;
Performing a first round of data analysis on the measured data of the physical features based on the measured data of the quality index to obtain the physical features which are strongly related to the quality index and are called first physical features;
performing a second round of data analysis on the measured data of the first physical features based on the measured data of the quality index in the database to divide all the first physical features into N groups, N being an integer greater than 1;
Selecting at least one representative physical feature from each set of said first physical features, referred to as a second physical feature;
And constructing an evaluation model based on the second physical characteristics.
2. The pod physical characteristic-based method of assessing the easy-to-dehull properties according to claim 1, wherein the first round of data analysis is based on a correlation analysis, comprising in particular:
counting the frequency of all the physical characteristics in the database according to actual measurement data;
And determining the first physical characteristic according to the result of the frequency statistics and the measured data of the quality index and the correlation analysis.
3. The pod physical feature-based easy-dehulling assessment method of claim 1, wherein said second round of data analysis of said first physical feature based on measured data of quality metrics in said database to divide all of said first physical features into N groupings comprises:
performing a second round of data analysis on the measured data of the first physical features based on a factor analysis method to form N influence factors and the first physical features corresponding to the influence factors;
and taking the first physical characteristic corresponding to each influence factor as a group to obtain N groups.
4. A pod physical feature-based method of assessing the vulnerability to shelling according to claim 3, wherein said selecting at least one representative physical feature, called a second physical feature, from each of said first physical features, comprises:
The first physical feature with the greatest factor weight in each group is selected as the representative physical feature, i.e., the second physical feature.
5. The pod physical feature-based easy-dehulling assessment method of claim 1, wherein the constructing an assessment model based on the second physical feature comprises:
Distributing weights to the second physical characteristics, and calculating the comprehensive score of the pods of each variety according to the weights and the specific numerical values of the second physical characteristics corresponding to the pods of each variety;
Classifying peanuts of all varieties based on the comprehensive score to obtain three grades of varieties which are respectively three grades of easy shelling, medium difficulty and difficult shelling;
And constructing a grading function corresponding to each grade by adopting actual measurement data of the second physical characteristics of the variety corresponding to each grade, and waiting for the evaluation model by comprehensively obtaining all the grading functions.
6. The utility model provides a pod physical characteristic-based easy shellability evaluation device which characterized in that it includes:
An acquisition module for acquiring a database of experimental data comprising pods of a plurality of varieties, the experimental data comprising variety information, measured data of a plurality of physical characteristics, and measured data of quality indicators;
The first analysis module is used for carrying out first round data analysis on the measured data of the physical characteristics based on the measured data of the quality indexes to obtain the physical characteristics which are strongly related to the quality indexes and are called first physical characteristics;
The second analysis module is used for carrying out second-round data analysis on the measured data of the first physical features based on the measured data of the quality indexes in the database so as to divide all the first physical features into N groups, wherein N is an integer greater than 1;
A selection module for selecting at least one representative physical feature, called a second physical feature, from each set of the first physical features;
And a model construction module for constructing an evaluation model based on the second physical characteristics.
7. A storage medium having stored therein a computer executable program which when executed by a processor implements the pod physical characteristic-based method of assessing the ease of pod shellability of any of claims 1-5.
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