Chinese wolfberry phenotype information extraction method, electronic equipment and storage medium

Abstract

The invention relates to a Chinese wolfberry phenotype information extraction method, electronic equipment and a storage medium. The method comprises the following steps: acquiring position information of a Chinese wolfberry park, scanning Chinese wolfberry in the Chinese wolfberry park, acquiring Chinese wolfberry phenotype information, storing the Chinese wolfberry phenotype information, and establishing a Chinese wolfberry phenotype database; performing data slicing on the Chinese wolfberry phenotype information according to a position area in the position information, establishing a spatial index relationship, and establishing an affiliation relationship in combination with Chinese wolfberry growth data; and inputting data information on the search page, and obtaining the searched Chinese wolfberry phenotype information and Chinese wolfberry growth data. According to the method, the Chinese wolfberry phenotype database applied to the independent Chinese wolfberry orchard is constructed, Chinese wolfberry phenotype information in space and cycle time is stored, and Chinese wolfberry phenotype information in a target area or a target cycle and corresponding Chinese wolfberry growth data can be effectively extracted.

Description

Technical Field

[0001] The present invention relates to the technical field of information extraction, and in particular to a method for extracting wolfberry phenotypic information, an electronic device and a storage medium. Background Art

[0002] Lycium barbarum phenotype refers to the characteristics of Lycium barbarum determined or influenced by itself and the external environment, reflecting its growth and development process, genetic characteristics, physiological and biochemical characteristics, etc. Lycium barbarum phenotype research mainly obtains high-quality and repeatable trait data, and quantitatively analyzes the interaction effect between genotype and environment and its impact on major traits related to yield, quality, and stress resistance.

[0003] At present, the extraction of wolfberry fruit phenotypic information mainly relies on manual work, using vernier calipers to measure the longitudinal and transverse diameters of wolfberries, estimate the aspect ratio of wolfberries, and determine the wolfberry fruit shape index, which is then summarized to achieve the extraction of wolfberry fruit phenotypic information. The shortcomings of the existing information extraction methods are mainly manifested in: first, low efficiency, and it is impossible to obtain sufficient phenotypic information in a short time; second, it is impossible to overcome human errors and the measurement data accuracy is not high; third, it lacks three-dimensionality and cannot be measured, analyzed and observed from multiple angles; fourth, it lacks storage capacity and cannot reflect the differences in space and cycle time in the wolfberry phenotypic information. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for extracting wolfberry phenotypic information, an electronic device and a storage medium, to construct a wolfberry phenotypic database applied to an independent wolfberry orchard itself, to store wolfberry phenotypic information in space and cycle time, and to effectively extract wolfberry phenotypic information of a target area or target cycle and corresponding wolfberry growth data.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for extracting wolfberry phenotypic information, comprising:

[0007] Acquire location information of a wolfberry garden, scan the wolfberries in the wolfberry garden, acquire wolfberry phenotypic information, store the wolfberry phenotypic information, and establish a wolfberry phenotypic database;

[0008] Slice the wolfberry phenotypic information according to the location area in the location information, establish a spatial index relationship, and combine it with the wolfberry growth data to establish a subordinate relationship;

[0009] Input data information on the search page to obtain the searched wolfberry phenotypic information and wolfberry growth data.

[0010] Optionally, scanning the wolfberries in the wolfberry garden includes:

[0011] Setting a scanning cycle, and selecting wolfberry fruits with obvious surface features and no diseases or insect pests in each scanning cycle;

[0012] The wolfberry fruit is scanned to obtain point cloud data, and the miscellaneous points are removed. The unscanned parts are scanned again, and the point cloud data of the wolfberry fruit is gradually improved by completing the point cloud information to obtain complete point cloud data.

[0013] Optionally, obtaining the wolfberry phenotypic information includes:

[0014] Converting the complete point cloud data into a wolfberry STP format file, and making a cross-section diagram and a cross-section diagram of wolfberry according to the wolfberry STP format file, so as to mark the length and width of the cross-section and the cross-section of wolfberry, respectively, so as to obtain the wolfberry phenotypic information;

[0015] The wolfberry phenotypic information includes: the length, width, surface area and volume of the wolfberry.

[0016] Optionally, establishing the spatial index relationship includes:

[0017] Extracting the location information, selecting a point in the wolfberry garden as an origin according to the location information, and establishing a spatial coordinate system;

[0018] According to the spatial coordinate system, the wolfberry garden is spatially divided to generate a plurality of location areas, the wolfberry phenotypic information is data sliced ​​according to the corresponding location areas, and the spatial index relationship is established.

[0019] Optionally, establishing a subordinate relationship includes:

[0020] The wolfberry growth data of each day in each scanning cycle is obtained, and a subordinate relationship is established between the wolfberry growth data of each day in each scanning cycle and the complete point cloud data of the corresponding cycle.

[0021] Optionally, the wolfberry growth data includes: soil temperature, soil moisture, light intensity, plant height and height under branches in the area.

[0022] Optionally, obtaining the searched wolfberry phenotypic information and wolfberry growth data includes:

[0023] Data information is input on the search page, the input data information is converted into JAVA language through JS language, and sent to the wolfberry phenotype database through SQL statements in JAVA language, and the searched wolfberry phenotype information and wolfberry growth data are generated in the wolfberry phenotype database, that is, the wolfberry phenotype information of the target area or target period and the corresponding wolfberry growth data.

[0024] To achieve the above object, the present invention further provides an electronic device, comprising:

[0025] at least one processor, and a memory communicatively coupled to the at least one processor;

[0026] The memory stores instructions, and the instructions are executed by the at least one processor, so that the method for extracting wolfberry phenotype information is implemented when the at least one processor executes the instructions.

[0027] To achieve the above object, the present invention also provides a computer-readable storage medium, comprising:

[0028] The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the wolfberry phenotypic information extraction method.

[0029] The beneficial effects of the present invention are:

[0030] The method for constructing a wolfberry fruit phenotype group database based on three-dimensional point cloud scanning technology can batch measure wolfberry fruit phenotype group data, save manpower and reduce errors.

[0031] The method of the present invention constructs a wolfberry fruit phenotype group database based on three-dimensional point cloud scanning technology, and then constructs a three-dimensional model of mature wolfberry fruit to obtain a wolfberry phenotype database applied to an independent individual wolfberry orchard itself, storing wolfberry phenotype information in space and cycle time.

[0032] The present invention pre-processes the data, adopts spatial index, and slices the data, so that subsequent operations can quickly obtain the required minimum data set, less data needs to be processed, less memory is occupied, and the wolfberry phenotypic information of the target area or target period and the corresponding wolfberry growth data can be effectively extracted. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 The present invention is a flowchart of a method for extracting wolfberry phenotypic information according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 As shown, this embodiment discloses a method for extracting wolfberry phenotypic information, including: obtaining location information of a wolfberry garden, scanning the wolfberries in the wolfberry garden, obtaining wolfberry phenotypic information, storing the wolfberry phenotypic information, and establishing a wolfberry phenotypic database; slicing the wolfberry phenotypic information according to the location area in the location information, establishing a spatial index relationship, and establishing a subordinate relationship in combination with the wolfberry growth data; inputting data information on a search page to obtain the searched wolfberry phenotypic information and wolfberry growth data.

[0038] Scanning the wolfberries in the wolfberry garden includes: setting a scanning cycle, selecting wolfberry fruits with obvious surface features and no diseases or insect pests in each scanning cycle; scanning the wolfberry fruits to obtain point cloud data, and removing noise, re-scanning the unscanned parts, and using the point cloud information completion method to gradually improve the point cloud data of the wolfberry fruits and obtain complete point cloud data.

[0039] Acquiring wolfberry phenotypic information includes: converting the complete point cloud data into a wolfberry STP format file, making a cross-section diagram and a cross-section diagram of the wolfberry according to the wolfberry STP format file, and marking the length and width of the cross-section and cross-section of the wolfberry, respectively, so as to obtain the wolfberry phenotypic information; wherein the wolfberry phenotypic information includes: the length and width, surface area and volume of the wolfberry, specifically:

[0040] Scanning steps: Screening wolfberries: Scanners screen wolfberry fruits that are obviously free of pests and diseases; Wolfberry scanning: 1) Scanning complete mature wolfberries and mature wolfberry sections indoors; 2) Assemble the OKIO-5M scanner, which has a scanning accuracy of 0.03mm and a scanning range of 3mm-400mm, calibrate it using the 2390 calibration plate, and perform calibration according to the software regulations until the calibration is successful; 3) Spray the selected samples with the imaging agent DPT-5, attach marking points on the turntable, and use plasticine to fix the samples to the turntable with the marking points; 4) Use the 3DScan-OKIO software to rotate the turntable for initial scanning. Scan the whole body in one step, rotate 30° once, and scan about 6 times to obtain the complete point cloud data of wolfberry, remove the surrounding noise for observation, scan the details that have not been scanned again, and use the multi-step scanning method to complete all the data that can be scanned and merge them to obtain triangular face data. Import the obtained face data into Geomagic Wrap for processing until the face surface is smooth and without holes. After data processing, import it into Geomagic Design X for entity creation; 5) Create engineering drawings: i) Make wolfberry catalog engineering drawings: Create a new engineering drawing in Unigraphics NX, use wolfberry STP 1) Create a detail list based on the data, which includes the serial number, quantity, and code. Create a 31*22.5 grid in the drawing and mark the sequence. Open the entity data processed by Geomagic Design X in Unigraphics NX according to the detail list, and place the fruit orthographic projection view according to the sequence number. 2) Create a cross-sectional view: Open the complete wolfberry STP format file in Unigraphics NX, create a new engineering drawing, place two basic views and five projection views, one basic view is used to mark the length and width of the wolfberry; the other basic view and five projection views are used to create a cross-sectional view. The five cross-sectional views are marked with length, width, and height. iii) Make a cross-section view of wolfberry: create a new engineering drawing in UnigraphicsNX, use the cross-section wolfberry STP room to make a detail column, draw a 13.66*22 grid and mark the sequence, open the wolfberry cross-section view according to the detail column, and place the wolfberry cross-section basic view according to the sequence number; mark the length and width of the internal structure of the wolfberry in each view; c. Measure the length, width, surface area and volume of the complete wolfberry: use GeomagicWrap to open the solid model that has been aligned in GeomagicDesignX, convert the STP data into STL data, measure the length, width, volume and surface area data respectively and record them.

[0041] To slice the data, you first need to determine the slicing level. If the slicing level is too low, the number of tiles is small, but the data in each tile is too large, which is not conducive to subsequent processing; on the contrary, if the slicing level is too high, the number of tiles is too large. Although there is less data in the tile, the number of tiles involved in subsequent processing may be too large, which will also affect efficiency. Therefore, it is necessary to determine a moderate level, which should not be too large or too small.

[0042] When slicing data, the "append" method is used to store the slicing results, so that the results of the last processing can be retained and continued next time, achieving incremental processing.

[0043] Establishing a spatial index relationship includes: extracting location information, selecting a point in the wolfberry garden as the origin based on the location information, and establishing a spatial coordinate system; according to the spatial coordinate system, spatially dividing the wolfberry garden to generate a number of location areas, slicing the wolfberry phenotypic information according to the corresponding location areas, and establishing a spatial index relationship.

[0044] Establishing the subordinate relationship includes: obtaining the wolfberry growth data of each day in each scanning cycle, and establishing a subordinate relationship between the wolfberry growth data of each day in each scanning cycle and the complete point cloud data of the corresponding cycle.

[0045] The wolfberry growth data include: soil temperature, soil moisture, light intensity, plant height and height under branches in the area.

[0046] The methods for obtaining soil temperature, soil moisture, light intensity, plant height and height under branches vary depending on the measurement conditions and accuracy requirements. The following are the methods for obtaining these parameters:

[0047] Soil temperature measurement method: Using weather station data: For large-scale wolfberry gardens, more accurate and continuous soil temperature data can be obtained through weather stations or soil temperature monitoring systems.

[0048] Soil moisture measurement methods: Resistive method: Use a resistive sensor to measure the resistance value in the soil to determine soil moisture. When soil moisture increases, the conductivity in the soil increases and the resistance value decreases; conversely, the resistance value increases. This method is fast and accurate, but requires specialized equipment. Capacitive method: Use the soil as the two electrodes of a capacitor to measure the change in capacitance between the soil and an external electrode to determine soil moisture. This method has the advantages of high sensitivity and fast response. Infrared method and microwave method: These two methods use infrared detectors and different propagation characteristics of microwave signals in the soil to determine soil moisture. They both have the advantages of fast measurement speed and non-destructive soil samples, but their applicability may vary depending on the soil type.

[0049] Light intensity measurement method: Obtain light intensity data through weather stations or satellite data: Obtain light intensity data through weather stations or satellite remote sensing technology. These data usually have higher accuracy and continuity.

[0050] Plant height measurement method: Laser rangefinder: Using laser technology to measure the height of wolfberry plants has the advantages of fast measurement speed and high accuracy. It is suitable for scenarios that require high-precision measurement.

[0051] Branch height measurement method: Use modern technologies such as ground-based laser radar to quickly obtain the three-dimensional features of wolfberry plants, and use post-processing software to automatically extract parameters such as branch height. This method has the advantages of high efficiency and high precision.

[0052] Acquiring the searched wolfberry phenotypic information and wolfberry growth data includes: inputting data information on a search page, converting the input data information into JAVA language through JS language, sending the data information to a wolfberry phenotypic database through SQL statements in JAVA language, and generating the searched wolfberry phenotypic information and wolfberry growth data in the wolfberry phenotypic database, that is, the wolfberry phenotypic information and corresponding wolfberry growth data of a target area or a target period.

[0053] When using JAVA language, it is generally necessary to convert the data input in JS language into data that can be recognized by JAVA language. This data is JSON data, which links JS language and JAVA language together, and the database can be queried through JAVA language. Therefore, when querying, it is necessary to use JAVA language and SQL language together to query the data in the database, and the queried data will be converted into JSON data, which can be recognized by JS language and converted into displayable data.

[0054] When extracting data, the data in the database can be further improved to achieve better extraction results. JS language, JAVA language, and JSON data are the languages ​​that must be used to extract data. They can better understand database problems and improve database extraction speed.

[0055] This embodiment also discloses an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions, and the instructions are executed by the at least one processor, so that when the at least one processor executes the instructions, the method for extracting wolfberry phenotypic information as claimed in any one of claims 1 to 7 is implemented.

[0056] This embodiment also discloses a computer-readable storage medium, including: the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the method for extracting wolfberry phenotypic information as claimed in any one of claims 1 to 7.

[0057] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for extracting wolfberry phenotypic information, characterized in that: include: Acquire location information of a wolfberry garden, scan the wolfberries in the wolfberry garden, acquire wolfberry phenotypic information, store the wolfberry phenotypic information, and establish a wolfberry phenotypic database; Slice the wolfberry phenotypic information according to the location area in the location information, establish a spatial index relationship, and combine it with the wolfberry growth data to establish a subordinate relationship; Input data information on the search page to obtain the searched wolfberry phenotypic information and wolfberry growth data.

2. The method for extracting wolfberry phenotypic information according to claim 1, characterized in that: Scanning the wolfberries in the wolfberry garden includes: Setting a scanning cycle, and selecting wolfberry fruits with obvious surface features and no diseases or insect pests in each scanning cycle; The wolfberry fruit is scanned to obtain point cloud data, and the miscellaneous points are removed. The unscanned parts are scanned again, and the point cloud data of the wolfberry fruit is gradually improved by completing the point cloud information to obtain complete point cloud data.

3. The method for extracting wolfberry phenotypic information according to claim 2, characterized in that: Acquiring the wolfberry phenotypic information includes: Converting the complete point cloud data into a wolfberry STP format file, and making a cross-section diagram and a cross-section diagram of wolfberry according to the wolfberry STP format file, so as to mark the length and width of the cross-section and the cross-section of wolfberry, respectively, so as to obtain the wolfberry phenotypic information; The wolfberry phenotypic information includes: the length, width, surface area and volume of the wolfberry.

4. The method for extracting wolfberry phenotypic information according to claim 1, characterized in that: Establishing the spatial index relationship includes: Extracting the location information, selecting a point in the wolfberry garden as an origin according to the location information, and establishing a spatial coordinate system; According to the spatial coordinate system, the wolfberry garden is spatially divided to generate a plurality of location areas, the wolfberry phenotypic information is data sliced ​​according to the corresponding location areas, and the spatial index relationship is established.

5. The method for extracting wolfberry phenotypic information according to claim 2, characterized in that: Establishing an affiliation involves: The wolfberry growth data of each day in each scanning cycle is obtained, and a subordinate relationship is established between the wolfberry growth data of each day in each scanning cycle and the complete point cloud data of the corresponding cycle.

6. The method for extracting wolfberry phenotypic information according to claim 1, characterized in that: The wolfberry growth data include: soil temperature, soil moisture, light intensity, plant height and height under branches in the area.

7. The method for extracting wolfberry phenotypic information according to claim 1, characterized in that: Obtain the searched wolfberry phenotypic information and wolfberry growth data including: Data information is input on the search page, the input data information is converted into JAVA language through JS language, and sent to the wolfberry phenotype database through SQL statements in JAVA language, and the searched wolfberry phenotype information and wolfberry growth data are generated in the wolfberry phenotype database, that is, the wolfberry phenotype information of the target area or target period and the corresponding wolfberry growth data.

8. An electronic device, characterized in that include: at least one processor, and a memory communicatively coupled to the at least one processor; Wherein, the memory stores instructions, and the instructions are executed by the at least one processor, so that when the at least one processor executes the instructions, the method for extracting wolfberry phenotypic information according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that include: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the method for extracting wolfberry phenotypic information according to any one of claims 1 to 7.

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