CN116152006B - Sweet potato climate quality assessment method and system - Google Patents
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Abstract
The invention provides a method and a system for evaluating the climate quality of sweet potatoes, and relates to the technical field of sweet potato quality analysis. First, sweet potato sample data is obtained. The sample data comprise quality data obtained by measuring quality components of the sweet potatoes and climate data in the growing period. And then, analyzing the quality data and the climate data to obtain main quality factors and climate factors of the sweet potatoes, and performing correlation analysis on the quality factors and the climate factors to determine the decisive climate factors of different quality factors. And then, carrying out multiple linear regression analysis on the quality factors and the corresponding decisive climate factors, and establishing quality climate prediction models corresponding to different quality factors. And finally, establishing climate quality models of the sweet potatoes of different types according to the related quality climate prediction models, and carrying out comprehensive quality evaluation on the sweet potatoes to be detected. Thereby providing a basis for sweet potato breeding and production and promoting the regional development and utilization of different types of sweet potatoes.
Description
Technical Field
The invention relates to the technical field of sweet potato quality analysis, in particular to a method and a system for evaluating the climate quality of sweet potatoes.
Background
Sweet potatoes are commonly called sweet potatoes, sweet potatoes and the like, and belong to the family of Convolvulaceae. The method is widely distributed in 120 countries and regions worldwide, the annual planting area reaches 862.4 ten thousand hectares, the annual total yield is 10519.1 ten thousand tons, and the 7 th sweet potato total yield is arranged in the world grain production. China is the largest sweet potato producer in the world, the yield of sweet potato is 4 th in China, and is inferior to rice, wheat and corn, and is the pillar industry of China agriculture. The sweet potato has high nutritive value and health care function. Along with the improvement of the living standard of people, the food structure tends to diversify and health care, and the market of edible sweet potatoes is becoming better. Therefore, the method has important economic value and scientific significance for researching the climate quality of the sweet potato processing suitability.
The existing sweet potato quality evaluation method comprises a principal component analysis method, a fuzzy comprehensive evaluation method, a gray correlation analysis method, an artificial neural network and the like. The main component analysis method is a statistical analysis method which converts a plurality of indexes into a few comprehensive indexes, and can convert the problem of a high-dimensional space into a low-dimensional space for processing, so that the problem becomes simpler and more visual, and the quality condition of the sweet potato is better evaluated. Moreover, the fewer comprehensive indexes are not related to each other, and most of information of the original indexes can be provided. The artificial neural network performs data clustering through learning and simulating the quantitative and qualitative evaluation work of an expert, so that the comprehensive evaluation of the product quality is realized. For example Yu Pingfu et al combine artificial neural network with fuzzy mathematics, evaluate the longan processing quality by 5 shape indexes of the screened taste, particle shape, color, aroma and yield, and construct comprehensive evaluation and grading simulation of the fuzzy artificial neural network. However, in the application process of the method, the climate factors are not considered, and the influence of the climate on the quality of the sweet potato is relatively large, so that a comprehensive evaluation method and model for the quality of the sweet potato based on the climate are needed.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating the climate quality of sweet potatoes, which can evaluate the climate quality of the sweet potatoes, thereby providing a basis for breeding and producing the sweet potatoes and promoting the regional development and utilization of different types of sweet potatoes.
Embodiments of the present invention are implemented as follows:
in a first aspect, an embodiment of the present application provides a method for evaluating the climate quality of sweet potatoes, including:
obtaining sweet potato sample data, wherein the sample data comprises quality data obtained by measuring quality components of sweet potatoes and climate data in a growing period;
according to the quality data and the climate data, main quality factors and climate factors of the sweet potato are obtained through analysis;
carrying out correlation analysis on the quality factors and the climate factors to determine decisive climate factors of different quality factors;
performing multiple linear regression analysis on the quality factors and the corresponding decisive climate factors, and establishing quality climate prediction models corresponding to different quality factors;
and establishing climate quality models of the sweet potatoes of different types according to the related quality climate prediction models, and evaluating the quality of the sweet potatoes to be detected based on the climate quality models.
Based on the first aspect, in some embodiments of the present invention, the step of obtaining the main quality factor and the climate factor of the sweet potato according to the quality data and the climate data analysis includes:
calculating variation coefficients and standard deviations corresponding to various quality components according to the quality data, and performing stability analysis according to the variation coefficients and the standard deviations to obtain main quality factors of the sweet potatoes;
based on the climate data, the spatial distribution characteristics and the time distribution characteristics of the climate factors are analyzed by using a GIS space analysis method, and the correlation among the climate factors is analyzed according to the distribution characteristics to obtain the climate factors.
Based on the first aspect, in some embodiments of the present invention, the step of determining the decisive climate factors of different quality factors by performing a correlation analysis on the quality factors and the climate factors includes:
carrying out correlation analysis on the quality factors and the climate factors by using a Pearson correlation analysis method to obtain correlation coefficients of the quality factors and the climate factors;
performing significance verification on the correlation coefficient by using a t-verification method, and sequencing the climate factors passing the significance verification according to the correlation importance;
determining the decisive climate factors of different quality factors according to the sequencing result.
Based on the first aspect, in some embodiments of the present invention, the step of performing multiple linear regression analysis on the quality factors and the corresponding determinant climate factors to establish the quality climate prediction models corresponding to different quality factors includes:
performing normal distribution verification on quality data of the quality factors, and performing correlation analysis on the quality factors obeying normal distribution by combining with the corresponding decisive climate factors;
establishing a multiple linear regression model according to the correlation analysis result to obtain quality climate prediction models corresponding to different quality factors;
and carrying out recurrent inspection on the quality climate prediction model of the quality factor by utilizing the quality data, and adjusting and optimizing model parameters according to inspection results.
Based on the first aspect, in some embodiments of the present invention, the step of establishing a climate quality model of different types of sweet potatoes according to the relevant quality climate prediction model, and performing quality assessment on the sweet potatoes to be detected based on the climate quality model includes:
extracting main components of sweet potatoes of different types by using a main component analysis method, and calling a related quality climate prediction model;
based on the related quality climate prediction model, establishing climate quality models of sweet potatoes of different types by using a linear analysis method;
and performing quality assessment on the sweet potatoes to be detected by using the climate quality model to obtain a quality assessment result.
Based on the first aspect, in some embodiments of the invention, further comprising: and performing climate quality grade classification according to the historical characteristic data of the sweet potatoes of different types, and determining the corresponding quality grade range.
In a second aspect, embodiments of the present application provide a sweet potato climate quality assessment system, comprising:
the sample data acquisition module is used for acquiring sweet potato sample data, wherein the sample data comprise quality data obtained by measuring quality components of sweet potatoes and climate data in a growing period;
the sample data analysis module is used for analyzing and obtaining main quality factors and climate factors of the sweet potatoes according to the quality data and the climate data;
the correlation analysis module is used for carrying out correlation analysis on the quality factors and the climate factors and determining decisive climate factors of different quality factors;
the regression model building module is used for performing multiple linear regression analysis on the quality factors and the corresponding decisive climate factors and building quality climate prediction models corresponding to different quality factors;
the climate quality evaluation module is used for establishing climate quality models of the sweet potatoes of different types according to the related quality climate prediction models and evaluating the quality of the sweet potatoes to be detected based on the climate quality models.
In a third aspect, embodiments of the present application provide an electronic device comprising a memory for storing one or more programs; a processor. The method as described in any one of the first aspects is implemented when the one or more programs are executed by the processor.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the first aspects above.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
the embodiment of the application provides a method and a system for evaluating the climate quality of sweet potatoes, which are characterized in that first, sweet potato sample data are obtained. The sample data comprise quality data obtained by measuring quality components of the sweet potatoes and climate data in the growing period. And then, analyzing the quality data and the climate data to obtain main quality factors and climate factors of the sweet potatoes, and performing correlation analysis on the quality factors and the climate factors to determine the decisive climate factors of different quality factors. And then, carrying out multiple linear regression analysis on the quality factors and the corresponding decisive climate factors, and establishing quality climate prediction models corresponding to different quality factors. And finally, establishing climate quality models of the sweet potatoes of different types according to the related quality climate prediction models, and evaluating the quality of the sweet potatoes to be detected based on the climate quality models. In the whole, the quality climate prediction model corresponding to different quality factors is established by carrying out correlation analysis on the quality factors and the climate factors, so that various qualities of the sweet potato can be predicted and evaluated according to climate conditions. And the climate quality model of the sweet potatoes of different types is established according to the related quality climate prediction model so as to carry out comprehensive quality evaluation on the sweet potatoes of different types. Thereby providing a basis for sweet potato breeding and production and promoting the regional development and utilization of different types of sweet potatoes.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram showing steps of an embodiment of a method for evaluating climate quality of sweet potato according to the present invention;
FIG. 2 is a graph showing the temperature and ground temperature variation in the middle of the sweet potato growth period (6-10 months) according to an embodiment of the present invention;
FIG. 3 is a graph showing the variation characteristics of precipitation, evaporation and sunshine duration of sweet potato in each ten days of the growing period according to an embodiment of the present invention;
FIG. 4 is a diagram showing a sweet potato starch quality histogram in an embodiment of a method for evaluating the climate quality of sweet potato according to the present invention;
FIG. 5 is a normal diagram of sweet potato starch quality in an embodiment of a method for evaluating the climate quality of sweet potato according to the present invention;
FIG. 6 is a block diagram of a system for evaluating the climate quality of sweet potatoes according to the present invention;
fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 1. a memory; 2. a processor; 3. a communication interface; 11. a sample data analysis module; 12. a sample data analysis module; 13. a correlation analysis module; 14. a regression model building module; 15. and a climate quality evaluation module.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Examples
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The various embodiments and features of the embodiments described below may be combined with one another without conflict.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for evaluating the climate quality of sweet potatoes according to an embodiment of the present application, the method includes the following steps:
step S1: and obtaining sweet potato sample data, wherein the sample data comprise quality data obtained by measuring quality components of the sweet potato and climate data in a growing period.
In the steps, the growth period of the sweet potatoes is mainly the final stage of 5 months later, the seed is inserted, the sweet potatoes are harvested at the bottom of 10 months, and the growing period is more than 140 days. The whole growth period is divided into 4 stages, namely: transplanting into a living period for 20 days; branching and potato forming period is 40 days; the potato is grown for 40 days; and potato blocks expand for 40 days. So as to mainly collect weather data day by day for 6-10 months. Wherein the climate data comprises: temperature, ground temperature, precipitation, barometric pressure, wind speed, evaporation capacity, humidity, barometric pressure, solar hours and other data. The quality components of sweet potato mainly comprise protein, sucrose, dry matter, starch, reducing sugar, etc. For example, when the content of the components is measured, the protein content can be detected by a Kjeldahl nitrogen determination method according to the relevant national food safety standard, the sucrose is measured by a hydrolysis-leine-Elnong method, the dry matter is measured by a direct drying method, the starch is measured by an enzyme-colorimetric method, and the reducing sugar is measured by a potassium permanganate titration method, so that the quality data of the sweet potato sample can be obtained.
Step S2: and analyzing the quality data and the climate data to obtain main quality factors and climate factors of the sweet potato. The method mainly comprises the following steps:
step S2-1: and calculating variation coefficients and standard deviations corresponding to various quality components according to the quality data, and performing stability analysis according to the variation coefficients and the standard deviations to obtain main quality factors of the sweet potatoes.
In the above steps, the coefficient of variation and standard deviation are both indicative of the degree of dispersion, and are used herein to analyze the stability characteristics of different quality components. Illustratively, statistics of the main quality components of sweet potatoes in various counties and cities of Fujian province in 2019-2020 are performed to obtain a quality component characteristic table shown in table 1:
TABLE 1 2019-2020 sweet potato Main quality component content Table for different counties and cities of Fujian province
| Quality component | Proteins | Reducing sugar | Sucrose | Starch | Dry matter |
| Standard deviation of | 0.70 | 0.67 | 0.52 | 2.92 | 3.32 |
| Coefficient of variation | 37.00 | 39.30 | 19.12 | 13.27 | 4.67 |
| Average value of | 1.90 | 1.71 | 2.74 | 21.99 | 71.12 |
| Maximum value | 3.46 | 2.50 | 3.90 | 28.50 | 75.90 |
| Minimum value | 0.86 | 0.53 | 1.70 | 18.30 | 63.60 |
The degree of dispersion of reducing sugars and proteins is greatest, sucrose and starch are inferior and dry matter is most stable from the coefficient of variation. The difference of different quality components of the sweet potato is determined by the variation coefficient, and the grade classification is conveniently classified subsequently due to the large difference, so that the evaluation and grading in the aspect of climate are carried out.
Step S2-2: based on the climate data, the spatial distribution characteristics and the time distribution characteristics of the climate factors are analyzed by using a GIS space analysis method, and the correlation among the climate factors is analyzed according to the distribution characteristics to obtain the climate factors.
In the above steps, the distribution of the climate factors can be statistically analyzed from four aspects: 1. air temperature and ground temperature; 2. precipitation, evaporation and solar hours; 3. humidity and water pressure; 4. barometric pressure and wind speed. By counting the distribution of the climate factors, the change characteristics of different climate factors in the sweet potato growth period are known. For example, please refer to fig. 2 and 3, wherein fig. 2 is a characteristic diagram of temperature and ground temperature change in the sweet potato in the growing period (6-10 months), the abscissa uses julian calendar to record the serial number of each ten days, 1 in the last 6 months, 2 in the middle 6 months, 3 in the last 6 months, and so on. According to the graph, the temperature is in an inverted U-shaped structure, and is gradually increased and then decreased. The highest point is in the middle of 7 months. In the same time point, the highest temperature of the soil surface of 0cm is the highest value of the air temperature and the soil temperature, and is larger than the extreme highest temperature of the air temperature. The average temperature of the soil surface of 0cm is close to the highest value of the air temperature, and the extreme lowest air temperature value is close to the lowest temperature value of the soil surface of 0 cm. It shows that the solar heat absorption and heat storage capacity of the daytime soil are higher than that of the atmosphere, but the difference between the solar heat absorption and heat storage capacity is not great at night. FIG. 3 is a graph showing the characteristics of the variation of precipitation, evaporation and sunshine duration in each ten days of sweet potato growth period. According to the graph, the precipitation amount shows a decreasing trend, and the trend fitting equation is as follows: y= -6.8625x+105.76, i.e. an average every ten days reduction of 6.8622mm; the correlation coefficient r=0.67, passing the significance test of p=0.005. However, the evaporation amount and the sunshine duration show a vibrating structure, and no obvious trend exists. And then, carrying out correlation analysis on the climate factors from the angles of light, temperature, water and humidity to obtain the relevant climate factors. For example: the average air temperature and air pressure, wind speed and other factors are inversely proportional to the ground temperature, water air pressure, humidity, precipitation, evaporation capacity, sunshine hours and other factors. And the average gas temperature and the gas pressure, the ground temperature, the water pressure and the like have higher correlation. It is indicated that the ground temperature is increased with the increase of the air temperature, the air pressure is lowered, the evaporation amount is increased, and the sunshine hours are increased in the growing period of the sweet potato.
Step S3: and carrying out correlation analysis on the quality factors and the climate factors to determine the decisive climate factors of different quality factors.
In the above steps, firstly, the correlation analysis is carried out on the quality factors and the climate factors by using a Pearson correlation analysis method, so as to obtain the correlation coefficients of the quality factors and the climate factors. And then, carrying out significance verification on the correlation coefficient by using a t-verification method, and sequencing the climate factors passing the significance verification according to the correlation importance. And finally, determining the decisive climate factors of different quality factors according to the sequencing result.
For example, referring to table 2, table 2 is a table of correlation analysis of proteins with each climate factor.
TABLE 2 correlation analysis of proteins with individual climatic factors
| Month of month | 6 | 7 | 8 | 9 | 10 |
| Maximum air temperature | 0.09 | -0.04 | -0.45 | -0.57 | -0.58 |
| Number of sunshine hours | 0.42 | 0.15 | -0.33 | -0.48 | -0.32 |
| Ground temperature of 0cm | 0.10 | -0.01 | -0.24 | -0.48 | -0.35 |
| Maximum ground temperature | 0.07 | 0.00 | -0.14 | -0.47 | -0.27 |
| Evaporation capacity | 0.37 | 0.12 | 0.41 | -0.37 | -0.02 |
| Average air temperature | 0.16 | 0.05 | -0.23 | -0.33 | -0.29 |
| Minimum ground temperature | 0.02 | -0.06 | -0.14 | -0.08 | -0.24 |
| Minimum relative humidity | 0.04 | -0.09 | -0.03 | -0.04 | -0.55 |
| Minimum air temperature | 0.13 | 0.04 | 0.00 | -0.02 | -0.08 |
| Wind speed | 0.32 | 0.26 | 0.31 | 0.18 | 0.18 |
| Precipitation amount | -0.60 | -0.34 | 0.15 | 0.21 | -0.28 |
| Relative humidity of | -0.28 | -0.20 | 0.25 | 0.31 | -0.33 |
| Vapor pressure of water | -0.07 | -0.07 | 0.26 | 0.40 | 0.13 |
| Poor in daily life | -0.03 | -0.08 | -0.28 | -0.46 | -0.39 |
| Air pressure | -0.20 | -0.19 | -0.17 | -0.21 | -0.20 |
As can be seen from table 2, 17 climatic factors passing the significance test of p=0.1 for the protein quality of sweet potatoes are mainly concentrated in 9 and 10 months, which indicates that the climatic conditions in the late growth period of sweet potatoes, especially in the root mass expansion period, have an important restriction on the formation of proteins. From the climate factors, the factors such as the precipitation amount at the bottom of 6 months, the highest air temperature of 9-10 months, the minimum relative humidity, the sunshine hours, the ground temperature of 0cm, the highest ground temperature, the evaporation amount, the average air temperature and the like are inversely proportional to the factors, so that the drought environment caused by the factors such as the high precipitation amount at the early stage, the high temperature at the later stage, the high illumination amount, the low relative humidity, the high evaporation amount and the like is not beneficial to the quality formation of protein. Arranged by relative importance, its main influencing factors are: the 6 month precipitation amount is >9-10 months maximum air temperature >10 months minimum relative humidity >9 months sunshine hours >9 months 0cm ground temperature >9 months maximum ground temperature and the like, the significance test of p=0.05 is passed, and the significance test of p=0.01 is passed for the two climate factors of 6 month precipitation amount and 9-10 months maximum air temperature. The decisive climate factors for obtaining the protein quality factor thus include the 6 month precipitation, the 9-10 month maximum air temperature, the 10 month minimum relative humidity, the 9 month solar hours and the 9 month 0cm temperature.
Step S4: and performing multiple linear regression analysis on the quality factors and the corresponding decisive climate factors, and establishing quality climate prediction models corresponding to different quality factors.
In the above steps, firstly, the quality data of the quality factors are subjected to normal distribution verification, and the quality factors obeying the normal distribution are combined with the corresponding decisive climate factors to perform correlation analysis. And then, establishing a multiple linear regression model according to the correlation analysis result to obtain quality climate prediction models corresponding to different quality factors. And finally, performing back-generation inspection on the quality climate prediction model of the quality factor by utilizing the quality data, and adjusting and optimizing model parameters according to inspection results.
Illustratively, the quality climate prediction model for starch quality factors is established as follows:
first, the sweet potato starch quality was checked for normalization using SPSS software, the histograms and normalization are shown in fig. 4 and 5, and the statistics are shown in table 3. From the above, it can be seen that the Shapiro-Wilk normal test statistic is 0.907 and the significance level is 0.145>0.05, so that the dependent variable starch quality is subject to normal distribution, and regression analysis can be performed.
TABLE 3 statistical of starch normal distribution
Then, correlation analysis is performed. As shown in tables 4 and 5, wherein X 1 -X 5 The ground highest temperature is 6-8 months, precipitation is 6 months in middle ten days, sunshine hours are 8 middle and late ten days, sunshine hours are 9 late ten days to 10 middle ten days, and wind speed is 7 months in middle and late ten days.
TABLE 4 correlation analysis of starch quality with decisive climatic factors
It can be seen that the highest ground temperature correlation is best (r= -0.654, p=0.05) for 6-8 months, followed by sun exposure (r= -0.502, p=0.1) for the next ten days in 8.
TABLE 5 regression coefficient and drift diameter coefficient analysis
The direct influence on the starch quality is the highest ground temperature of 6-8 months, which is negative correlation, and the correlation is good, and the influence of the visible air temperature on the starch quality is the greatest, and the starch is unfavorable at high temperature. And secondly, the sunshine hours in the late 9 th to the middle 10 th are positively correlated, namely the sunshine conditions in the swelling period of the sweet potato blocks have promotion effect on starch formation.
Then, establishing a multiple linear regression model of the starch quality factor:
Y＝34.7921-0.2233X 1 +0.0128X 2 -0.0545X 3 +0.0213X 4 +0.0833X 5
then, statistical tests were performed on the established multiple regression equations, and the results are shown in Table 6. Where r=0.6981 and p=0.05, the regression equation is significant.
TABLE 6 regression statistical test
| Multiple R | 0.6981 |
| R Square | 0.4874 |
| Adjusted R Square | 0.1669 |
| Standard error of | 2.7650 |
And finally, performing a back-generation test on the quality climate prediction model of the quality factor by using the quality data. The results were as follows:wherein y is i Representing the actual quality value, +.>And expressing the predicted quality value, substituting the predicted value of the sweet potato quality and sample quality data into the formula, and calculating to obtain the average accuracy rate of sweet potato starch quality fitting of 92.00%.
Step S5: and establishing climate quality models of the sweet potatoes of different types according to the related quality climate prediction models, and evaluating the quality of the sweet potatoes to be detected based on the climate quality models.
In the above steps, first, main components of sweet potatoes of different types are extracted by using a main component analysis method, and relevant quality climate prediction models are called. For example: starch processing sweet potato has the highest demand for starch, the other times. The related research shows that the accumulation of the sweet potato root tuber starch has a very obvious positive correlation with the dry matter rate of the sweet potato root tuber and the content of the crude starch of the sweet potato stem, and has little correlation with the number of branches and the content of soluble sugar of the sweet potato stem. In addition, zhou Zhilin research shows that 2 principal components are extracted through principal component analysis, and the cumulative variance contribution rate reaches 94.62%, wherein the contribution is the maximum potato dry starch content and the potato root tuber dry matter rate. Both reflect all the information of the sweet potato starch synthesis related economy and quality traits. The taste style and nutrition components in the diet are more emphasized by the fresh sweet potato, and the quality of the fresh sweet potato is different from that of the processed sweet potato. Therefore, the requirements for proteins and carbohydrates are highest, and the other times. The research shows that the quality of the fresh food forms a very obvious positive correlation with the contents of protein and sugar, and the product basically reflects all information of the related economy and quality properties of the fresh food of the sweet potato and can be used as a main component factor for forming the quality of the sweet potato. The principal components of different types of sweet potatoes extracted by the principal component analysis method are the quality factors.
For starch processing sweet potatoes, a relevant quality climate prediction model is extracted, and the starch quality climate prediction model is:
Y 1 ＝34.7921-0.2233X 1 +0.0128X 2 -0.0545X 3 +0.0213X 4 +0.0833X 5
wherein X is 1 -X 5 The highest ground temperature is 6-8 months respectively, and the highest ground temperature is 6 ten days in month 6Precipitation, number of sunshine hours in the middle and late days of 8, number of sunshine hours in the middle and late days of 9-10, and wind speed in the middle and late days of 7 months.
Dry matter rate quality climate prediction model:
Y 2 ＝53.2296-1.4093X 1 -0.5422X 2 +0.0518X 3 +0.0333X 4 -0.1254X 5
wherein X is 1 -X 5 The wind speed is 7-8 months, the highest ground temperature is 6-7, the sunshine hours are 6 months, the sunshine hours are 8 months, and the relative humidity is 9 months.
And then, based on the related quality climate prediction model, establishing climate quality models of sweet potatoes of different types by using a linear analysis method. For example, the climate quality model of the starch processed sweet potato is: f (F) 1 ＝0.5Y 1 +0.5Y 2 . Wherein, the two quality weights respectively account for the proportion of 0.5. Wherein F is 1 For the climate quality of processed sweet potato, Y 1 And Y 2 The climatic inversion values for starch and dry matter rate, respectively.
And finally, performing quality assessment on the sweet potato to be detected by using the climate quality model to obtain a quality assessment result.
Furthermore, climate quality grade classification can be performed according to the historical characteristic data of the sweet potatoes of different types, and corresponding quality grade ranges can be determined.
For example, the climate quality grade classification (see table 8) can be performed according to the historical characteristic data (see table 7) of the Fujian province for 30 years, and the climate quality grade classification method of the China weather office can be adopted to classify the climate quality grade classification into three grade grades of qualification, good grade and excellent grade.
TABLE 7 30 year characteristic values of starch processed sweet potato climate quality
| Climate quality | Average value of | Maximum value | Minimum value | Difference value of extremely |
| Processing type | 25.65 | 28.32 | 23.85 | 4.47 |
Table 8 starch processed sweet potato climate quality 30 year characteristic value grading
| Climate quality | Qualified product | Good quality | Excellent quality |
| Processing type | F<24.00 | 24≦F<26 | 26.00≦F |
Therefore, the quality evaluation is carried out on the sweet potatoes to be detected through the climate quality model, and then classification is carried out according to the quality grade, so that visual quality evaluation results can be obtained.
Based on the same inventive concept, the invention further provides a system for evaluating the quality of the sweet potato climate, please refer to fig. 6, fig. 6 is a block diagram of a system for evaluating the quality of the sweet potato climate according to an embodiment of the present application. The system comprises:
the sample data acquisition module 11 is used for acquiring sweet potato sample data, wherein the sample data comprises quality data obtained by measuring quality components of sweet potatoes and climate data in a growing period;
the sample data analysis module 12 is used for obtaining the main quality factors and the climate factors of the sweet potato according to the quality data and the climate data analysis;
the correlation analysis module 13 is used for performing correlation analysis on the quality factors and the climate factors and determining the decisive climate factors of different quality factors;
the regression model building module 14 is used for performing multiple linear regression analysis on the quality factors and the corresponding decisive climate factors thereof, and building quality climate prediction models corresponding to different quality factors;
the climate quality evaluation module 15 is configured to establish climate quality models of different types of sweet potatoes according to the relevant quality climate prediction models, and perform quality evaluation on the sweet potatoes to be detected based on the climate quality models.
Referring to fig. 7, fig. 7 is a block diagram of an electronic device according to an embodiment of the present application. The electronic device comprises a memory 1, a processor 2 and a communication interface 3, wherein the memory 1, the processor 2 and the communication interface 3 are electrically connected with each other directly or indirectly so as to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 1 may be used for storing software programs and modules, such as program instructions/modules corresponding to a sweet potato climate quality evaluation system provided in the embodiments of the present application, and the processor 2 executes the software programs and modules stored in the memory 1, thereby performing various functional applications and data processing. The communication interface 3 may be used for communication of signaling or data with other node devices.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. A method for evaluating the climate quality of sweet potatoes, which is characterized by comprising the following steps:
obtaining sweet potato sample data, wherein the sample data comprises quality data obtained by measuring quality components of sweet potatoes and climate data in a growing period;
according to the quality data and the climate data, main quality factors and climate factors of the sweet potato are obtained through analysis;
performing correlation analysis on the quality factors and the climate factors to determine decisive climate factors of different quality factors;
performing multiple linear regression analysis on the quality factors and the corresponding decisive climate factors, and establishing quality climate prediction models corresponding to different quality factors;
establishing climate quality models of different types of sweet potatoes according to the related quality climate prediction models, and evaluating the quality of the sweet potatoes to be detected based on the climate quality models; comprising the following steps: extracting main components of sweet potatoes of different types by using a main component analysis method, and calling a related quality climate prediction model; based on the related quality climate prediction model, establishing climate quality models of sweet potatoes of different types by using a linear analysis method; and performing quality assessment on the sweet potatoes to be detected by using the climate quality model to obtain a quality assessment result.
2. The method of claim 1, wherein the step of analyzing the quality data and the climate data to obtain the main quality factor and climate factor of the sweet potato comprises:
calculating variation coefficients and standard deviations corresponding to various quality components according to the quality data, and performing stability analysis according to the variation coefficients and the standard deviations to obtain main quality factors of the sweet potatoes;
based on the climate data, the spatial distribution characteristics and the time distribution characteristics of the climate factors are analyzed by using a GIS space analysis method, and the correlation among the climate factors is analyzed according to the distribution characteristics, so that the climate factors are obtained.
3. The method of claim 1, wherein the step of determining the decisive climate factors for different quality factors by performing a correlation analysis of the quality factors and climate factors comprises:
carrying out correlation analysis on the quality factors and the climate factors by using a Pearson correlation analysis method to obtain correlation coefficients of the quality factors and the climate factors;
performing significance verification on the correlation coefficient by using a t-verification method, and sequencing the climate factors passing the significance verification according to the correlation importance;
determining the decisive climate factors of different quality factors according to the sequencing result.
4. The method of claim 1, wherein the step of performing multiple linear regression analysis on the quality factors and their corresponding determinant climate factors to build quality climate prediction models corresponding to different quality factors comprises:
performing normal distribution verification on quality data of the quality factors, and performing correlation analysis on the quality factors obeying normal distribution by combining with the corresponding decisive climate factors;
establishing a multiple linear regression model according to the correlation analysis result to obtain quality climate prediction models corresponding to different quality factors;
and carrying out recurrent inspection on the quality climate prediction model of the quality factor by utilizing the quality data, and adjusting and optimizing model parameters according to inspection results.
5. The method for evaluating the climate quality of sweet potatoes according to claim 1, further comprising: and performing climate quality grade classification according to the historical characteristic data of the sweet potatoes of different types, and determining the corresponding quality grade range.
6. A sweet potato climate quality assessment system, comprising:
the sample data acquisition module is used for acquiring sweet potato sample data, wherein the sample data comprise quality data obtained by measuring quality components of sweet potatoes and climate data in a growing period;
the sample data analysis module is used for analyzing and obtaining main quality factors and climate factors of the sweet potatoes according to the quality data and the climate data;
the correlation analysis module is used for carrying out correlation analysis on the quality factors and the climate factors and determining decisive climate factors of different quality factors;
the regression model building module is used for performing multiple linear regression analysis on the quality factors and the corresponding decisive climate factors and building quality climate prediction models corresponding to different quality factors;
the climate quality evaluation module is used for establishing climate quality models of the sweet potatoes of different types according to the related quality climate prediction models and evaluating the quality of the sweet potatoes to be detected based on the climate quality models; comprising the following steps: extracting main components of sweet potatoes of different types by using a main component analysis method, and calling a related quality climate prediction model; based on the related quality climate prediction model, establishing climate quality models of sweet potatoes of different types by using a linear analysis method; and performing quality assessment on the sweet potatoes to be detected by using the climate quality model to obtain a quality assessment result.
7. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the method of any of claims 1-5 is implemented when the one or more programs are executed by the processor.
8. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-5.
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