Tea tree quality identification method and system

Abstract

The invention discloses a tea tree quality identification method and system. The method comprises the following steps: acquiring a tea tree data set in a preset first time period; sending the tea tree data set in a preset first time period to a preset quality identification module to obtain a quality identification total score; according to a preset score interval in which the quality identification total score falls, determining interval division grades of the quality identification total score; grading according to the interval of the quality identification total score, and determining the quality grade of the corresponding tea tree. According to the method, comprehensive evaluation is carried out from the tea tree to the tea leaves, and then from the smell of the tea leaves and components contained in the tea leaves, and the accuracy of quality identification of the tea trees is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of food quality identification, and more specifically, to a method and system for identifying the quality of tea trees. Background Art

[0002] As consumers' requirements for tea quality continue to increase, accurately identifying the quality of tea trees has become the key to meeting market demand and improving tea competitiveness. Consumers are paying more and more attention to the quality characteristics of tea, such as taste, aroma, and nutritional content. This has prompted the tea industry to require more scientific and accurate identification methods to ensure and improve product quality. At present, the quality identification of tea trees is mainly carried out by corresponding technical personnel, which is limited by the technical level of technical personnel and is interfered by human factors.

[0003] Therefore, the prior art has defects and needs to be improved urgently. Summary of the invention

[0004] In view of the above problems, an object of the present invention is to provide a method and system for identifying the quality of tea trees, which can improve the accuracy of the quality identification of tea trees.

[0005] The first aspect of the present invention provides a method for identifying the quality of tea trees, comprising:

[0006] Acquire a tea tree dataset within a preset first time period;

[0007] Sending the tea tree data set within a preset first time period to a preset quality identification module to obtain a total quality identification score;

[0008] Determine the interval classification level of the total quality appraisal score according to the preset score interval into which the total quality appraisal score falls;

[0009] The quality grade of the corresponding tea tree is determined according to the interval of the total quality appraisal score.

[0010] This plan also includes:

[0011] Obtain soil data information for tea tree planting;

[0012] According to the soil data information of tea tree planting, the elements in the soil and the corresponding element content values ​​are obtained;

[0013] Get the current time information and the corresponding tea tree information;

[0014] According to the current time information and the corresponding tea tree information, determine the standard element content value range of the corresponding tea tree at the current time;

[0015] If the content values ​​of all elements in the soil are within the standard element content value range corresponding to the current time, the tea tree data obtained at the current time is qualified.

[0016] In this solution, the step of sending the tea tree data set within the preset first time period to the preset quality identification module to obtain the total quality identification score specifically includes:

[0017] Dividing the tea tree data set within a preset first time period into a tea tree image data subset, a tea leaf odor data subset, and a tea leaf component data subset;

[0018] Sending the tea tree image data subset to an image quality identification submodule in a preset quality identification module to obtain a tea tree image quality score;

[0019] Sending the tea odor data subset to the odor quality identification submodule in the preset quality identification module to obtain the tea odor quality score;

[0020] Sending the tea ingredient data subset to the ingredient quality identification submodule in the preset quality identification module to obtain the tea ingredient quality score;

[0021] The total quality identification score of the corresponding tea tree is obtained by multiplying the tea tree image quality score by the preset image weight coefficient, adding the tea smell quality score by the preset smell weight coefficient, and adding the tea ingredient quality score by the preset ingredient weight coefficient.

[0022] In this solution, the step of sending the tea tree image data subset to the image quality identification submodule in the preset quality identification module to obtain the tea tree image quality score specifically includes:

[0023] Extract tea leaves images from the tea tree image data subset;

[0024] Extract features and corresponding eigenvalues ​​from tea images;

[0025] Normalizing the characteristic values ​​in the tea image to obtain normalized tea characteristic values;

[0026] The normalized value of the tea feature is multiplied by the weight coefficient of the corresponding feature, and then accumulated to obtain the feature quality score of the corresponding tea image;

[0027] Traversing the tea images in the entire tea tree image data subset to obtain a characteristic quality score set of the tea images;

[0028] Calculate the average value of the characteristic quality scores in the characteristic quality score set of the tea image to obtain the corresponding tea quality score;

[0029] Extract the tree crown image from the tea tree image data subset;

[0030] Annotate the tree crown image to obtain a tree crown annotation map;

[0031] Divide the tree crown annotation map according to regions to obtain multiple tree crown annotation sub-maps;

[0032] Compare and analyze the tree crown annotation sub-image with the preset tree crown annotation sub-image of the corresponding area to obtain the similarity value of the tree crown annotation sub-image;

[0033] Traverse the crown images in the entire tea tree image data subset to obtain the crown annotation sub-image similarity value set;

[0034] The crown annotation sub-graph similarity values ​​in the crown annotation sub-graph similarity value set are averaged to obtain the crown similarity value, and the corresponding crown similarity value is set as the corresponding crown quality score weight coefficient;

[0035] Multiply the crown quality score weight coefficient by the preset score base of the corresponding crown to obtain the crown quality score;

[0036] Multiply the tea quality score by the tea weight coefficient, and add the crown quality score multiplied by the corresponding crown weight coefficient to obtain the corresponding tea tree image quality score.

[0037] In this solution, the step of labeling the crown image in the tea tree image to obtain the crown labeling map specifically includes:

[0038] Extracting tea leaves, tea branches and non-tea leaves in the tree crown image;

[0039] Compare and analyze the tea image in the tea area in the crown image with the preset tea image to obtain a tea similarity value;

[0040] If the tea similarity value is greater than or equal to the preset first similarity threshold, the corresponding tea area is marked according to the preset first value; if the tea similarity value is less than the preset first similarity threshold, the corresponding tea area is marked according to the preset third value;

[0041] Compare and analyze the tea branch image in the tea branch area in the crown image with the preset tea branch image to obtain a tea branch similarity value;

[0042] If the tea branch similarity value is greater than or equal to the preset second similarity threshold, the corresponding tea branch area is marked according to the preset second value; if the tea branch similarity value is less than the preset second similarity threshold, the corresponding tea branch area is marked according to the preset third value;

[0043] The non-tea leaf and tea branch area is marked according to a preset third value.

[0044] In this solution, the step of comparing and analyzing the tree crown annotated sub-image with the preset tree crown annotated sub-image of the corresponding area to obtain the similarity value of the tree crown annotated sub-image specifically includes:

[0045] Extracting the area values ​​marked by the preset first value, the preset second value and the preset third value in the tree crown annotation sub-graph, and setting them as the first area, the second area and the third area respectively;

[0046] Extracting the area values ​​marked by the preset first value, the preset second value and the preset third value in the preset tree crown annotation sub-image of the corresponding area, and setting them as the preset first area, the preset second area and the preset third area respectively;

[0047] The first area is compared with the preset first area to obtain a first ratio; the second area is compared with the preset second area to obtain a second ratio; the third area is compared with the preset third area to obtain a third ratio;

[0048] The minimum value among the first ratio, the second ratio and the third ratio is extracted, and is set as the crown labeled sub-image similarity value between the crown labeled sub-image and a preset crown labeled sub-image of the corresponding area.

[0049] In this solution, the step of sending the tea odor data subset to the odor quality identification submodule in the preset quality identification module to obtain the tea odor quality score specifically includes:

[0050] Extracting smell data from the tea smell data subset;

[0051] Extracting odor components and corresponding odor component content values ​​from odor data;

[0052] Compare and analyze the odor components in the odor data with the preset odor components. If they are consistent, the corresponding odor components are saved; if they are inconsistent, the corresponding odor components are deleted;

[0053] Determine the corresponding odor score according to the preset odor content range that the corresponding odor component content value falls into;

[0054] Multiply different odor scores by the weight ratio of the corresponding odor, and then add them up to get the score of the corresponding odor data;

[0055] Traverse the entire tea odor data subset to obtain a score set of odor data;

[0056] The scores of the odor data in the score set of the odor data are averaged to obtain the corresponding tea odor quality score.

[0057] In this solution, the step of sending the tea ingredient data subset to the ingredient quality identification submodule in the preset quality identification module to obtain the tea ingredient quality score specifically includes:

[0058] Extract tea ingredient data from the tea ingredient data subset;

[0059] Extract tea ingredients and content values ​​of corresponding tea ingredients from tea ingredient data;

[0060] Compare and analyze the tea ingredients with the preset tea ingredients. If they are consistent, the corresponding tea ingredients will be saved; if they are inconsistent, the tea ingredients will be deleted;

[0061] Determine the score of the corresponding tea component according to the preset tea component content range within which the content value of the corresponding tea component falls;

[0062] Multiply the score of the tea component by the weight ratio of the corresponding element, and then add them up to get the score of the corresponding tea component;

[0063] Traverse the entire tea ingredient data subset to obtain the tea ingredient score set;

[0064] The scores of the tea components are concentrated and the average value is calculated to obtain the corresponding tea component quality score.

[0065] A second aspect of the present invention provides a tea tree quality identification system, comprising a memory and a processor, wherein the memory stores a program of a tea tree quality identification method.

[0066] The invention discloses a tea tree quality identification method and system, which conducts comprehensive evaluation from tea trees to tea leaves, and then to the smell of tea leaves and components contained therein, thereby improving the accuracy of tea tree quality identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a method for identifying the quality of tea trees according to the present invention is shown;

[0068] Figure 2 A flowchart showing the steps of labeling a tree crown image according to the present invention is shown;

[0069] Figure 3 A block diagram of a tea tree quality identification system of the present invention is shown. DETAILED DESCRIPTION

[0070] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0072] Figure 1 A flow chart of a method for identifying tea tree quality according to the present invention is shown.

[0073] like Figure 1 As shown, the present invention discloses a method for identifying the quality of tea trees, comprising:

[0074] S101, obtaining a tea tree dataset within a preset first time period;

[0075] S102, sending the tea tree data set within a preset first time period to a preset quality identification module to obtain a total quality identification score;

[0076] S103, determining the interval classification level of the total quality appraisal score according to the preset score interval into which the total quality appraisal score falls;

[0077] S104, dividing the grades according to the interval of the total score of the quality identification, and determining the quality grade of the corresponding tea tree.

[0078] According to an embodiment of the present invention, the preset first time period is a continuous time period, for example, the preset first time period is 30 days, and the corresponding tea tree data set is a tea tree data set within a continuous 30-day period, and the preset quality identification module stores a method and system for processing tea tree data in the tea tree data set to obtain a total quality identification score; and then divides the total quality identification score into a plurality of preset score intervals by a preset score base, for example, if the preset score base is 2, the preset score interval can be divided into (0, 2], (2, 4 ], (4, 6]…(n-2, n], where n is an even number, and then set a division level for each preset score interval, wherein the larger the score in the preset score interval, the higher the division level of the corresponding preset score interval, for example, the preset score interval (0, 2] is set to the first level, the preset score interval (2, 4] is set to the second level, and so on, and then associate the interval division level with the quality level of the tea tree, the higher the interval division level, the higher the quality level of the corresponding tea tree, wherein each interval division level corresponds to a quality level of a tea tree.

[0079] According to an embodiment of the present invention, it also includes:

[0080] Obtain soil data information for tea tree planting;

[0081] According to the soil data information of tea tree planting, the elements in the soil and the corresponding element content values ​​are obtained;

[0082] Get the current time information and the corresponding tea tree information;

[0083] According to the current time information and the corresponding tea tree information, determine the standard element content value range of the corresponding tea tree at the current time;

[0084] If the content values ​​of all elements in the soil are within the standard element content value range corresponding to the current time, the tea tree data obtained at the current time is qualified.

[0085] It should be noted that when obtaining tea tree data, the soil data for tea tree planting is obtained in advance. The elements in the soil include water, nitrogen, phosphorus and other elements. For example, nitrogen can promote plant growth, and phosphorus helps root development, etc. Different plants or the same plant in different time periods have different requirements for nutrients. Therefore, the corresponding standard element content value range is matched according to the time and tea tree information. The standard element content value range is used to ensure that the current tea tree is in a normal living environment, thereby improving the accuracy of the currently acquired tea tree data.

[0086] According to an embodiment of the present invention, the step of sending the tea tree data set within the preset first time period to a preset quality identification module to obtain a total quality identification score specifically includes:

[0087] Dividing the tea tree data set within a preset first time period into a tea tree image data subset, a tea leaf odor data subset, and a tea leaf component data subset;

[0088] Sending the tea tree image data subset to an image quality identification submodule in a preset quality identification module to obtain a tea tree image quality score;

[0089] Sending the tea odor data subset to the odor quality identification submodule in the preset quality identification module to obtain the tea odor quality score;

[0090] Sending the tea ingredient data subset to the ingredient quality identification submodule in the preset quality identification module to obtain the tea ingredient quality score;

[0091] The total quality identification score of the corresponding tea tree is obtained by multiplying the tea tree image quality score by the preset image weight coefficient, adding the tea smell quality score by the preset smell weight coefficient, and adding the tea ingredient quality score by the preset ingredient weight coefficient.

[0092] According to an embodiment of the present invention, the preset quality identification module is divided into an image quality identification submodule, an odor quality identification submodule and an ingredient quality identification submodule, wherein the image quality identification submodule analyzes the image of the tea tree, the odor quality identification submodule analyzes the odor of the tea leaves, and the ingredient quality identification submodule analyzes the ingredients in the tea leaves. By conducting a comprehensive analysis from the three aspects of color, aroma and taste, the accuracy of tea tree quality identification is improved.

[0093] According to an embodiment of the present invention, the step of sending the tea tree image data subset to the image quality identification submodule in the preset quality identification module to obtain the tea tree image quality score specifically includes:

[0094] Extract tea leaves images from the tea tree image data subset;

[0095] Extract features and corresponding eigenvalues ​​from tea images;

[0096] Normalizing the characteristic values ​​in the tea image to obtain normalized tea characteristic values;

[0097] The normalized value of the tea feature is multiplied by the weight coefficient of the corresponding feature, and then accumulated to obtain the feature quality score of the corresponding tea image;

[0098] Traversing the tea images in the entire tea tree image data subset to obtain a characteristic quality score set of the tea images;

[0099] Calculate the average value of the characteristic quality scores in the characteristic quality score set of the tea image to obtain the corresponding tea quality score;

[0100] Extract the tree crown image from the tea tree image data subset;

[0101] Annotate the tree crown image to obtain a tree crown annotation map;

[0102] Divide the tree crown annotation map according to regions to obtain multiple tree crown annotation sub-maps;

[0103] Compare and analyze the tree crown annotation sub-image with the preset tree crown annotation sub-image of the corresponding area to obtain the similarity value of the tree crown annotation sub-image;

[0104] Traverse the crown images in the entire tea tree image data subset to obtain the crown annotation sub-image similarity value set;

[0105] The crown annotation sub-graph similarity values ​​in the crown annotation sub-graph similarity value set are averaged to obtain the crown similarity value, and the corresponding crown similarity value is set as the corresponding crown quality score weight coefficient;

[0106] Multiply the crown quality score weight coefficient by the preset score base of the corresponding crown to obtain the crown quality score;

[0107] Multiply the tea quality score by the tea weight coefficient, and add the crown quality score multiplied by the corresponding crown weight coefficient to obtain the corresponding tea tree image quality score.

[0108] It should be noted that the tea tree image quality score is mainly evaluated through the visual effect of the tea tree. The features in the tea image include the size, shape, color, etc. of the corresponding tea leaves. For example, if the shape of the tea leaves is complete and uniform and the color is bright green, the corresponding tea image quality score will be higher.

[0109] Figure 2 A flow chart showing the steps of labeling a tree crown image according to the present invention is shown.

[0110] like Figure 2 As shown, according to an embodiment of the present invention, the step of labeling the crown image in the tea tree image to obtain the crown labeling map specifically includes:

[0111] S201, extracting tea leaves, tea branches and non-tea leaves and tea branches in the tree crown image;

[0112] S202, comparing and analyzing the tea image in the tea area in the crown image with the preset tea image to obtain a tea similarity value;

[0113] S203, if the tea similarity value is greater than or equal to the preset first similarity threshold, the corresponding tea area is marked according to the preset first value; if the tea similarity value is less than the preset first similarity threshold, the corresponding tea area is marked according to the preset third value;

[0114] S204, comparing and analyzing the tea branch image in the tea branch area in the crown image with the preset tea branch image to obtain a tea branch similarity value;

[0115] S205, if the tea branch similarity value is greater than or equal to the preset second similarity threshold, the corresponding tea branch area is marked according to the preset second value; if the tea branch similarity value is less than the preset second similarity threshold, the corresponding tea branch area is marked according to the preset third value;

[0116] S206: Mark the non-tea leaf and tea branch area according to a preset third value.

[0117] According to an embodiment of the present invention, by analyzing the crown image, it is evaluated whether the overall shape of the corresponding tea tree is symmetrical and whether the branches are reasonable, so as to assess the crown quality score. The non-tea leaf tea branch area is the gap area of ​​the crown. For example, if there is a defect in the tea leaves, the corresponding tea leaf similarity value will be less than the preset first similarity threshold, and the corresponding defective tea leaf area will be marked according to the third value. For example, if there is no tea leaf on the tea branch, the corresponding tea branch similarity value will be less than the preset second similarity threshold, and the corresponding tea branch will be set as a necrotic tea branch, and the corresponding necrotic tea branch area will be marked according to the third value.

[0118] According to an embodiment of the present invention, the step of comparing and analyzing the tree crown labeled sub-graph with a preset tree crown labeled sub-graph of a corresponding area to obtain a similarity value of the tree crown labeled sub-graph specifically includes:

[0119] Extracting the area values ​​marked by the preset first value, the preset second value and the preset third value in the tree crown annotation sub-graph, and setting them as the first area, the second area and the third area respectively;

[0120] Extracting the area values ​​marked by the preset first value, the preset second value and the preset third value in the preset tree crown annotation sub-image of the corresponding area, and setting them as the preset first area, the preset second area and the preset third area respectively;

[0121] The first area is compared with the preset first area to obtain a first ratio; the second area is compared with the preset second area to obtain a second ratio; the third area is compared with the preset third area to obtain a third ratio;

[0122] The minimum value among the first ratio, the second ratio and the third ratio is extracted, and is set as the crown labeled sub-image similarity value between the crown labeled sub-image and a preset crown labeled sub-image of the corresponding area.

[0123] It should be noted that, by using the preset camera equipment, the tea tree is photographed from the side to ensure that the tea tree image contains the entire crown image of the corresponding tea tree. The area values ​​marked by the preset first value, preset second value and preset third value in the preset crown annotation submap of different regions are inconsistent. For example, the preset first value in the preset crown annotation submap corresponding to the crown top area accounts for a larger proportion, and the preset first value in the preset crown annotation submap closer to the ground area accounts for a smaller proportion.

[0124] According to an embodiment of the present invention, the step of sending the tea odor data subset to the odor quality identification submodule in the preset quality identification module to obtain the tea odor quality score specifically includes:

[0125] Extracting smell data from the tea smell data subset;

[0126] Extracting odor components and corresponding odor component content values ​​from odor data;

[0127] Compare and analyze the odor components in the odor data with the preset odor components. If they are consistent, the corresponding odor components are saved; if they are inconsistent, the corresponding odor components are deleted;

[0128] Determine the corresponding odor score according to the preset odor content range that the corresponding odor component content value falls into;

[0129] Multiply different odor scores by the weight ratio of the corresponding odor, and then add them up to get the score of the corresponding odor data;

[0130] Traverse the entire tea odor data subset to obtain a score set of odor data;

[0131] The scores of the odor data in the score set of the odor data are averaged to obtain the corresponding tea odor quality score.

[0132] It should be noted that the smell emitted by the tea tree is obtained by a preset electronic nose or aroma analyzer or other equipment, and the smell is detected and identified to obtain the smell components and the corresponding smell component content values; the unique smell is saved, and then the smell content values ​​are divided into ranges, wherein each smell content range corresponds to an smell score, and the smell content value ranges corresponding to different smells are divided differently, and the corresponding smell scores are also inconsistent; the tea smell data subset contains tea smell data at different time points, and the corresponding tea smell quality score is obtained by averaging the scores of the smell data in the tea smell data subset, thereby improving the accuracy of the tea smell quality score and reducing detection errors and contingency.

[0133] According to an embodiment of the present invention, the step of sending the tea ingredient data subset to the ingredient quality identification submodule in the preset quality identification module to obtain the tea ingredient quality score specifically includes:

[0134] Extract tea ingredient data from the tea ingredient data subset;

[0135] Extract tea ingredients and content values ​​of corresponding tea ingredients from tea ingredient data;

[0136] Compare and analyze the tea ingredients with the preset tea ingredients. If they are consistent, the corresponding tea ingredients will be saved; if they are inconsistent, the tea ingredients will be deleted;

[0137] Determine the score of the corresponding tea component according to the preset tea component content range within which the content value of the corresponding tea component falls;

[0138] Multiply the score of the tea component by the weight ratio of the corresponding element, and then add them up to get the score of the corresponding tea component;

[0139] Traverse the entire tea ingredient data subset to obtain the tea ingredient score set;

[0140] The scores of the tea components are concentrated and the average value is calculated to obtain the corresponding tea component quality score.

[0141] It should be noted that the tea ingredients include tea polyphenols, amino acids, caffeine and other ingredients. Multiple tea ingredient content ranges are set for each type of tea ingredient, and each tea ingredient content range corresponds to a score, so that the score of the corresponding tea ingredient can be determined according to the preset tea ingredient content range within which the tea ingredient content value falls; the tea ingredient data subset includes tea ingredient data collected at different times.

[0142] Figure 3 A block diagram of a tea tree quality identification system of the present invention is shown.

[0143] like Figure 3 As shown, the second aspect of the present invention provides a tea tree quality identification system 3, comprising a memory 31 and a processor 32, wherein the memory stores a program of a tea tree quality identification method.

[0144] The invention discloses a tea tree quality identification method and system, which conducts comprehensive evaluation from tea trees to tea leaves, and then to the smell of tea leaves and components contained therein, thereby improving the accuracy of tea tree quality identification.

[0145] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0146] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0147] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0148] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0149] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for identifying the quality of tea trees, characterized in that: include: Acquire a tea tree dataset within a preset first time period; Sending the tea tree data set within a preset first time period to a preset quality identification module to obtain a total quality identification score; Determine the interval classification level of the total quality appraisal score according to the preset score interval into which the total quality appraisal score falls; The quality grade of the corresponding tea tree is determined according to the interval of the total quality appraisal score.

2. A method for identifying tea tree quality according to claim 1, characterized in that: Also includes: Obtain soil data information for tea tree planting; According to the soil data information of tea tree planting, the elements in the soil and the corresponding element content values ​​are obtained; Get the current time information and the corresponding tea tree information; According to the current time information and the corresponding tea tree information, determine the standard element content value range of the corresponding tea tree at the current time; If the content values ​​of all elements in the soil are within the standard element content value range corresponding to the current time, the tea tree data obtained at the current time is qualified.

3. A method for identifying tea tree quality according to claim 1, characterized in that: The step of sending the tea tree data set within the preset first time period to a preset quality identification module to obtain a total quality identification score specifically includes: Dividing the tea tree data set within a preset first time period into a tea tree image data subset, a tea leaf odor data subset, and a tea leaf component data subset; Sending the tea tree image data subset to an image quality identification submodule in a preset quality identification module to obtain a tea tree image quality score; Sending the tea odor data subset to the odor quality identification submodule in the preset quality identification module to obtain the tea odor quality score; Sending the tea ingredient data subset to the ingredient quality identification submodule in the preset quality identification module to obtain the tea ingredient quality score; The total quality identification score of the corresponding tea tree is obtained by multiplying the tea tree image quality score by the preset image weight coefficient, adding the tea smell quality score by the preset smell weight coefficient, and adding the tea ingredient quality score by the preset ingredient weight coefficient.

4. A method for identifying tea tree quality according to claim 3, characterized in that: The step of sending the tea tree image data subset to the image quality identification submodule in the preset quality identification module to obtain the tea tree image quality score specifically includes: Extract tea leaves images from the tea tree image data subset; Extract features and corresponding eigenvalues ​​from tea images; Normalizing the characteristic values ​​in the tea image to obtain normalized tea characteristic values; The normalized value of the tea feature is multiplied by the weight coefficient of the corresponding feature, and then accumulated to obtain the feature quality score of the corresponding tea image; Traversing the tea images in the entire tea tree image data subset to obtain a characteristic quality score set of the tea images; Calculate the average value of the characteristic quality scores in the characteristic quality score set of the tea image to obtain the corresponding tea quality score; Extract the tree crown image from the tea tree image data subset; Annotate the tree crown image to obtain a tree crown annotation map; Divide the tree crown annotation map according to regions to obtain multiple tree crown annotation sub-maps; Compare and analyze the crown annotation sub-image with the preset crown annotation sub-image of the corresponding area to obtain the similarity value of the crown annotation sub-image; Traverse the crown images in the entire tea tree image data subset to obtain the crown annotation sub-image similarity value set; The crown annotation sub-graph similarity values ​​in the crown annotation sub-graph similarity value set are averaged to obtain the crown similarity value, and the corresponding crown similarity value is set as the corresponding crown quality score weight coefficient; Multiply the crown quality score weight coefficient by the preset score base of the corresponding crown to obtain the crown quality score; Multiply the tea quality score by the tea weight coefficient, and add the crown quality score multiplied by the corresponding crown weight coefficient to obtain the corresponding tea tree image quality score.

5. A method for identifying tea tree quality according to claim 4, characterized in that: The step of labeling the crown image in the tea tree image to obtain the crown labeling map specifically includes: Extracting tea leaves, tea branches and non-tea leaves in the tree crown image; Compare and analyze the tea image in the tea area in the crown image with the preset tea image to obtain a tea similarity value; If the tea similarity value is greater than or equal to the preset first similarity threshold, the corresponding tea area is marked according to the preset first value; if the tea similarity value is less than the preset first similarity threshold, the corresponding tea area is marked according to the preset third value; Compare and analyze the tea branch image in the tea branch area in the crown image with the preset tea branch image to obtain a tea branch similarity value; If the tea branch similarity value is greater than or equal to the preset second similarity threshold, the corresponding tea branch area is marked according to the preset second value; if the tea branch similarity value is less than the preset second similarity threshold, the corresponding tea branch area is marked according to the preset third value; The non-tea leaf and tea branch area is marked according to a preset third value.

6. A method for identifying tea tree quality according to claim 4 or 5, characterized in that: The step of comparing and analyzing the tree crown annotated sub-graph with the preset tree crown annotated sub-graph of the corresponding area to obtain the similarity value of the tree crown annotated sub-graph specifically includes: Extracting the area values ​​marked by the preset first value, the preset second value and the preset third value in the tree crown annotation sub-graph, and setting them as the first area, the second area and the third area respectively; Extracting the area values ​​marked by the preset first value, the preset second value and the preset third value in the preset tree crown annotation sub-image of the corresponding area, and setting them as the preset first area, the preset second area and the preset third area respectively; The first area is compared with the preset first area to obtain a first ratio; the second area is compared with the preset second area to obtain a second ratio; the third area is compared with the preset third area to obtain a third ratio; The minimum value among the first ratio, the second ratio and the third ratio is extracted, and is set as the crown labeled sub-image similarity value between the crown labeled sub-image and a preset crown labeled sub-image of the corresponding area.

7. A method for identifying tea tree quality according to claim 3, characterized in that: The step of sending the tea odor data subset to the odor quality identification submodule in the preset quality identification module to obtain the tea odor quality score specifically includes: Extracting smell data from the tea smell data subset; Extracting odor components and corresponding odor component content values ​​from odor data; Compare and analyze the odor components in the odor data with the preset odor components. If they are consistent, the corresponding odor components are saved; if they are inconsistent, the corresponding odor components are deleted; Determine the corresponding odor score according to the preset odor content range that the corresponding odor component content value falls into; Multiply different odor scores by the weight ratio of the corresponding odor, and then add them up to get the score of the corresponding odor data; Traverse the entire tea odor data subset to obtain a score set of odor data; The scores of the odor data in the score set of the odor data are averaged to obtain the corresponding tea odor quality score.

8. A method for identifying tea tree quality according to claim 3, characterized in that: The step of sending the tea ingredient data subset to the ingredient quality identification submodule in the preset quality identification module to obtain the tea ingredient quality score specifically includes: Extract tea ingredient data from the tea ingredient data subset; Extract tea ingredients and content values ​​of corresponding tea ingredients from tea ingredient data; Compare and analyze the tea ingredients with the preset tea ingredients. If they are consistent, the corresponding tea ingredients will be saved; if they are inconsistent, the tea ingredients will be deleted; Determine the score of the corresponding tea component according to the preset tea component content range within which the content value of the corresponding tea component falls; Multiply the score of the tea component by the weight ratio of the corresponding element, and then add them up to get the score of the corresponding tea component; Traverse the entire tea ingredient data subset to obtain the tea ingredient score set; The scores of the tea components are concentrated and the average value is calculated to obtain the corresponding tea component quality score.

9. A tea tree quality identification system, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a method program for identifying the quality of tea trees as claimed in any one of claims 1 to 8.

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