Grape planting comprehensive management system based on GIS

Abstract

The invention provides a GIS-based grape planting integrated management system, and belongs to the technical field of management systems. The data acquisition module is used for acquiring basic geographic information of a grape base based on a GIS map technology and recording farming operation information of grapes in each growth stage of the grape base; the agricultural condition monitoring data module is used for collecting meteorological data and soil moisture content data of a target grape base in real time through Internet of Things equipment; the pest and disease analysis and prediction module is used for performing pest and disease analysis and prediction on the target grape base based on agricultural condition monitoring data and agricultural operation information; the pest and disease treatment module is used for formulating a corresponding treatment scheme according to an expert database based on a pest and disease analysis result report and early warning; and the data visual module is used for displaying the farming operation information, the farming condition monitoring data, the pest and disease damage analysis result and the pest and disease damage treatment tracking result in a visual mode by the system, and providing visual decision support. And the efficiency and scientificity of agricultural production are improved through refined farming operation and pest control.

Description

Technical Field

[0001] The invention relates to the technical field of management systems, and in particular to a GIS-based integrated grape planting management system. Background Art

[0002] At present, grape cultivation is one of the industries with important economic value in agricultural production around the world. Especially in areas with suitable climatic conditions, such as the Mediterranean climate zone and temperate zones, grape cultivation has become one of the main agricultural activities. Grapes are not only an important source of fruit consumption, but are also widely used in the production of wine, juice, dried fruits and other products. However, the management of grape cultivation faces many challenges, including environmental changes, pest and disease control, soil management, irrigation and fertilization, etc. Improper management will cause the problem of yield decline.

[0003] Therefore, the present invention proposes a GIS-based integrated grape planting management system. Summary of the invention

[0004] The present invention provides a GIS-based integrated grape planting management system, which is used to build a highly integrated and precise agricultural management platform by integrating GIS maps, the Internet of Things and big data analysis technology. The system can help growers monitor the operation of grape bases in real time, carry out refined farming operations and pest control, and improve the efficiency and scientificity of agricultural production.

[0005] In one aspect, the present invention provides a GIS-based integrated grape planting management system, comprising: Data acquisition module: Based on GIS map technology, it obtains the basic geographic information of the target grape base and records the agricultural operation information of grapes at each growth stage of the target grape base; Agricultural condition monitoring data module: collects meteorological data and soil moisture data of the target grape base in real time through IoT devices to form agricultural condition monitoring data; Pest and disease analysis and prediction module: Based on agricultural monitoring data and agricultural operation information, the system conducts pest and disease analysis and prediction on the target grape base to obtain pest and disease analysis results; Pest and disease treatment module: Based on the results of pest and disease analysis, the system reports and warns, and formulates corresponding treatment plans according to the expert database; Data visualization module: The system displays agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results in a visual manner to provide intuitive decision support.

[0006] On the other hand, the data acquisition module includes: Target determination unit: determine the target grape base for research and divide the basic monitoring scope of the target grape base; GIS unit: Select the preset GIS map technology, obtain basic geographic information based on the basic monitoring range of the target grape base, and import the target grape base into a two-dimensional geographic view, wherein the minimum unit value of the coordinate can be customized by the user; Basic planting information unit: obtain basic information on grape planting according to the preset information of the target grape base.

[0007] On the other hand, the data acquisition module further includes: Farming operation unit: records the farming operations of the target grape base at each growth stage according to the growth cycle of grapes, and enters the farming operations into the system; Database unit: Build a planting database and store agricultural operations in the system's planting database using time and growth stage as joint primary keys.

[0008] On the other hand, the agricultural monitoring data module includes: Target setting unit: clarify the types of agricultural data that need to be collected, including meteorological data and soil moisture data; Sensor type unit: select the sensor type of the detection item according to the type of agricultural data; Matching unit: based on basic geographic information, select all sensor installation locations, obtain the maximum matching degree between any sensor installation location and any sensor type as the matching degree of the sensor installation location, sort all sensor installation locations according to the matching degree, and generate a list consistent with the number of sensor types, which is the list of optimal sensor installation locations; Installation unit: based on the matching relationship between any sensor installation position and sensor type in the sensor optimal installation position list, install the sensor and obtain agricultural monitoring data in real time; Select IoT devices to connect to all sensors of the target grape base and transmit agricultural monitoring data into the system.

[0009] On the other hand, the pest analysis and prediction module includes: Image analysis unit: based on agricultural monitoring data and agricultural operation information, obtain an original image group of any plant grape growth stage, perform image denoising standard processing on any original image in the original image group, and obtain a standard image; Each element of the standard image is gray-scaled to obtain a gray-scale image of the standard image, and a gray-level co-occurrence matrix is ​​constructed based on the pixel value of each pixel. ,in Indicates the pixel with horizontal coordinate i and vertical coordinate j in the grayscale image in a given direction and the symbiosis value of the corresponding pixel at distance d; The symbiotic characteristic value of any pixel point of the grayscale image obtained based on the symbiotic relationship matrix is: ;in, represents the co-occurrence feature value of the pixel with horizontal coordinate i and vertical coordinate j in the grayscale image, Represents the pixel value of the pixel with abscissa i and ordinate j. Indicates the distance direction of the pixel point with horizontal coordinate i and vertical coordinate j and the pixel value of the pixel at distance d, ( ) represents the texture function, n represents the number of pixels in n directions, and m represents the maximum distance is m; The symbiotic characteristic values ​​of all pixels of the grayscale image are obtained, and the possible pests and diseases in the grayscale image are analyzed by comparing the symbiotic characteristic values ​​with the symbiotic characteristic values ​​of any pest and disease in the system expert database.

[0010] On the other hand, the pest analysis and prediction module further includes: Pest and disease analysis unit: matching the symbiotic feature values ​​of the original image group of the growth stage with the pest and disease type according to the pest and disease type, and if the number of successful matches is greater than the number of preset images, determining that the plant has the pest and disease; Risk level unit: Based on the identified pest and disease plant conditions, the risk level of the plant is obtained as follows: ;in, Indicates the risk level of the plant in question, ( ) represents the level conversion function, represents the weight coefficient of pest area, represents the weight coefficient of healthy days, represents the weight coefficient of environmental factors, Indicates the maximum area of ​​pests and diseases on the plant, represents the total area of ​​the plant, ( ) represents the health conversion function, Indicates the number of days that the plant has been exposed to pests and diseases, represents the kth environmental parameter, q represents a total of q environmental parameters, R( ) represents the parameter conversion function, represents the standard planting value of the kth environmental parameter; Preservation unit: construct a pest and disease database, and store the identified pest and disease types, risk levels and plant location codes in the pest and disease database.

[0011] On the other hand, the pest control module comprises: Report generation unit: according to the preset time interval, retrieve the pest data from the pest database through the interface, input the pest data based on the report template, and output the pest analysis report, which includes the pest type, risk level and location code of the plant; Early warning unit: The system automatically matches the relevant basic prevention and control measures in the expert database according to the pest and disease types in the pest and disease analysis report. The system formulates personalized treatment plans based on the dynamic factors of grape varieties, environmental parameters, and growth stages; Tracking unit: The system supports the follow-up tracking and evaluation of personalized treatment plans to ensure the effectiveness of the treatment measures.

[0012] On the other hand, the data visualization module includes: Visualization unit: Based on different data types, design visualization chart layouts of corresponding data types to display agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results in a visual way; Screening unit: provides interactive functions, allowing users to select different time ranges, growth stages, and data types for screening as needed, and view the changing trends and specific conditions of related data; Real-time update unit: The big data visualization platform combines the GIS map of the target grape base to obtain and update data of all data types in real time, providing intuitive decision support for growers.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a GIS-based integrated grape planting management system, which is used to build a highly integrated and precise agricultural management platform by integrating GIS maps, the Internet of Things and big data analysis technology. The system can help growers monitor the operation of grape bases in real time, carry out refined farming operations and pest control, and improve the efficiency and scientificity of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a structural schematic diagram of a GIS-based integrated grape planting management system provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the architecture of the integrated management system for grape planting provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0017] Embodiment 1: like Figure 1 As shown, an embodiment of the present invention provides a GIS-based integrated grape planting management system, including: Data acquisition module: Based on GIS map technology, it obtains the basic geographic information of the target grape base and records the agricultural operation information of grapes at each growth stage of the target grape base; Agricultural condition monitoring data module: collects meteorological data and soil moisture data of the target grape base in real time through IoT devices to form agricultural condition monitoring data; Pest and disease analysis and prediction module: Based on agricultural monitoring data and agricultural operation information, the system conducts pest and disease analysis and prediction on the target grape base to obtain pest and disease analysis results; Pest and disease treatment module: Based on the results of pest and disease analysis, the system reports and warns, and formulates corresponding treatment plans according to the expert database; Data visualization module: The system displays agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results in a visual manner to provide intuitive decision support.

[0018] In this embodiment, GIS mapping technology is a technology used to capture, store, manage, analyze, display and interpret geographic spatial data.

[0019] In this embodiment, the target grape base refers to a specific area or farm for grape cultivation in an agricultural management system.

[0020] In this embodiment, basic geographic information refers to geographic spatial data related to the target grape base obtained through GIS technology, such as geographical location, topography, soil characteristics, etc.

[0021] In this embodiment, the growth stage refers to the different growth and development processes of grapes from planting to harvesting.

[0022] In this embodiment, the agricultural operation information refers to all operations and activity records related to crop management during the grape planting process.

[0023] In this embodiment, the IoT device refers to a hardware device that is connected via the Internet and can automatically collect, transmit and process data.

[0024] In this embodiment, the target grape base refers to a specific grape planting area that is concerned with, monitored, or managed in the grape planting management system.

[0025] In this embodiment, meteorological data refers to various data related to climate and weather conditions collected in real time by meteorological monitoring equipment, such as temperature, precipitation, and humidity.

[0026] In this embodiment, the soil moisture data refers to various data related to soil moisture, reflecting the state of water in the soil and evaluating the water reserve, evaporation and infiltration of the soil within a specific time.

[0027] In this embodiment, agricultural monitoring data refers to data related to agricultural production that is collected, recorded and analyzed in real time through various technical means, including soil moisture data and meteorological data.

[0028] In this embodiment, pest and disease analysis and prediction refers to analyzing agricultural monitoring data, historical agricultural operation information, meteorological data, soil information, etc., using data analysis and machine learning algorithms to predict possible pest and disease conditions in grape planting bases.

[0029] In this embodiment, the pest and disease analysis result is a pest and disease prediction report obtained by analyzing the agricultural monitoring data, agricultural operation information and related historical data of the target grape base.

[0030] In this embodiment, the expert database is a database that integrates the knowledge, suggestions and best practices of multiple agricultural experts, researchers, technicians, etc.

[0031] In this embodiment, visualization is to display complex data and information in the form of graphics, images, maps, charts, etc., so that users can understand and analyze data more intuitively and quickly.

[0032] In this embodiment, the architecture of the grape planting integrated management system based on GIS is as follows: Figure 2 shown.

[0033] The working principle and beneficial effects of the above technical solution are: through GIS map technology and IoT devices, the geographical, meteorological and soil data of the grape base are obtained in real time, and pest and disease prediction and analysis are carried out in combination with agricultural operation information. The system automatically generates early warning reports and provides expert processing solutions, and ultimately supports accurate decision-making through data visualization, improving the efficiency and sustainability of grape planting management.

[0034] Embodiment 2: Based on the above embodiment 1, the data acquisition module includes: Target determination unit: determine the target grape base for research and divide the basic monitoring scope of the target grape base; GIS unit: Select the preset GIS map technology, obtain basic geographic information based on the basic monitoring range of the target grape base, and import the target grape base into a two-dimensional geographic view, wherein the minimum unit value of the coordinate can be customized by the user; Basic planting information unit: obtain basic information on grape planting according to the preset information of the target grape base.

[0035] In this embodiment, the basic monitoring range refers to the selected monitoring and management area range for a certain grape base.

[0036] In this embodiment, the two-dimensional geographic view refers to a visualization interface that displays geographic information in the form of a plane map.

[0037] In this embodiment, the minimum coordinate unit value refers to the accuracy of coordinate representation in map space data in a geographic information system.

[0038] In this embodiment, the preset information refers to the basic data and parameters related to grape planting that are determined in advance by the system before starting to monitor and analyze the target grape base, including: grape variety, irrigation information, planting density, etc.

[0039] In this embodiment, the basic information of grape planting refers to key data and parameters related to grape planting.

[0040] The working principle and beneficial effects of the above technical solution are: determine the target grape base and its monitoring range through GIS technology, import geographic information and display it in a two-dimensional view, allowing users to customize coordinate units. By obtaining basic information on grape planting, accurate monitoring and data management are achieved, effectively improving the scientificity and operability of planting management.

[0041] Embodiment 3: Based on the above embodiment 1, the data acquisition module further includes: Farming operation unit: records the farming operations of the target grape base at each growth stage according to the growth cycle of grapes, and enters the farming operations into the system; Database unit: Build a planting database and store agricultural operations in the system's planting database using time and growth stage as joint primary keys.

[0042] In this embodiment, the growth cycle refers to the entire process from the planting of grape seedlings to the full maturity and picking of grape fruits.

[0043] In this embodiment, the planting database is a database system for storing, managing and analyzing various data in the agricultural production process.

[0044] In this embodiment, the joint primary key refers to a primary key composed of two or more fields in the database.

[0045] The working principle and beneficial effects of the above technical solution are: by recording the agricultural operations of each growth stage of grapes and storing them in the planting database, using time and growth stage as joint primary keys for management, accurate agricultural data tracking and analysis are achieved, which helps to improve management efficiency and scientific decision-making.

[0046] Embodiment 4: Based on the above embodiment 1, the agricultural condition monitoring data module includes: Target setting unit: clarify the types of agricultural data that need to be collected, including meteorological data and soil moisture data; Sensor type unit: select the sensor type of the detection item according to the type of agricultural data; Matching unit: based on basic geographic information, select all sensor installation locations, obtain the maximum matching degree between any sensor installation location and any sensor type as the matching degree of the sensor installation location, sort all sensor installation locations according to the matching degree, and generate a list consistent with the number of sensor types, which is the list of optimal sensor installation locations; Installation unit: based on the matching relationship between any sensor installation position and sensor type in the sensor optimal installation position list, install the sensor and obtain agricultural monitoring data in real time; Select IoT devices to connect to all sensors of the target grape base and transmit agricultural monitoring data into the system.

[0047] In this embodiment, the agricultural data type refers to various data types that can reflect various factors such as the agricultural environment, crop growth conditions, soil conditions, etc. during the agricultural production process.

[0048] In this embodiment, the meteorological data type refers to data reflecting various meteorological elements in the atmosphere, including: temperature, humidity, precipitation, etc.

[0049] In this embodiment, the soil moisture data type refers to the data type related to soil moisture and related conditions, including: soil moisture, soil temperature, soil density, etc.

[0050] In this embodiment, the sensor type refers to hardware equipment used to collect information such as farmland environment, meteorological conditions, and soil moisture.

[0051] In this embodiment, the maximum matching degree is an indicator for measuring the degree of fit between the sensor installation position and the sensor type.

[0052] In this embodiment, the optimal installation position list is a sorted list including the best installation position selected for each sensor type.

[0053] The working principle and beneficial effects of the above technical solution are: by clarifying the type of agricultural data collected, selecting appropriate sensors, and optimizing the sensor installation location based on geographic information, accurate deployment and efficient data collection can be achieved. Through the access of IoT devices, agricultural data can be obtained and transmitted in real time to improve the accuracy of monitoring and management efficiency.

[0054] Embodiment 5: Based on the above embodiment 1, the pest analysis and prediction module includes: Image analysis unit: based on agricultural monitoring data and agricultural operation information, obtain an original image group of any plant grape growth stage, perform image denoising standard processing on any original image in the original image group, and obtain a standard image; Each element of the standard image is gray-scaled to obtain a gray-scale image of the standard image, and a gray-level co-occurrence matrix is ​​constructed based on the pixel value of each pixel. ,in Indicates the pixel with horizontal coordinate i and vertical coordinate j in the grayscale image in a given direction and the symbiosis value of the corresponding pixel at distance d; The symbiotic characteristic value of any pixel point of the grayscale image obtained based on the symbiotic relationship matrix is: ;in, represents the co-occurrence feature value of the pixel with horizontal coordinate i and vertical coordinate j in the grayscale image, Represents the pixel value of the pixel with abscissa i and ordinate j. Indicates the distance direction of the pixel point with horizontal coordinate i and vertical coordinate j and the pixel value of the pixel at distance d, ( ) represents the texture function, n represents the number of pixels in n directions, and m represents the maximum distance is m; The symbiotic characteristic values ​​of all pixels of the grayscale image are obtained, and the possible pests and diseases in the grayscale image are analyzed by comparing the symbiotic characteristic values ​​with the symbiotic characteristic values ​​of any pest and disease in the system expert database.

[0055] In this embodiment, the original image group refers to a group of original image data of plants at different growth stages captured and collected in the agricultural monitoring system.

[0056] In this embodiment, the denoising standard process is a technique in image processing, which is mainly used to reduce noise in an image to ensure that subsequent processing is more accurate.

[0057] In this embodiment, the standard image refers to an image that has been subjected to denoising processing.

[0058] In this embodiment, grayscale processing is a common technology in image processing, which is used to convert a color image into a grayscale image.

[0059] In this embodiment, the grayscale image refers to an image that has been subjected to image denoising and standardization processing, and each pixel value represents brightness information.

[0060] In this embodiment, the gray level co-occurrence matrix is ​​a method used in image processing to describe the spatial relationship between gray values ​​of pixels in an image, and is particularly used for texture analysis, capturing the spatial distribution and correlation of pixel pairs in an image, thereby extracting features related to image texture.

[0061] In this embodiment, the symbiosis relationship value refers to the frequency of occurrence of the grayscale value of a certain pixel point and the grayscale value of the corresponding pixel point at a specified direction and distance in the grayscale symbiosis matrix.

[0062] In this embodiment, the co-occurrence feature value represents the spatial relationship and texture pattern between different pixel gray levels in the image.

[0063] The working principle and beneficial effects of the above technical solution are: through image denoising, grayscale processing and grayscale co-occurrence matrix analysis, the texture features of grape plants are extracted and compared with the characteristic values ​​of pests and diseases in the expert database, so as to realize automatic identification and monitoring of pests and diseases and improve the accuracy and efficiency of agricultural management.

[0064] Embodiment 6: Based on the above embodiment 5, the pest analysis and prediction module further includes: Pest and disease analysis unit: matching the symbiotic feature values ​​of the original image group of the growth stage with the pest and disease type according to the pest and disease type, and if the number of successful matches is greater than the number of preset images, determining that the plant has the pest and disease; Risk level unit: Based on the identified pest and disease plant conditions, the risk level of the plant is obtained as follows: ;in, Indicates the risk level of the plant in question, ( ) represents the level conversion function, represents the weight coefficient of pest area, represents the weight coefficient of healthy days, represents the weight coefficient of environmental factors, Indicates the maximum area of ​​pests and diseases on the plant, represents the total area of ​​the plant, ( ) represents the health conversion function, Indicates the number of days that the plant has been exposed to pests and diseases, represents the kth environmental parameter, q represents a total of q environmental parameters, R( ) represents the parameter conversion function, represents the standard planting value of the kth environmental parameter; Preservation unit: construct a pest and disease database, and store the identified pest and disease types, risk levels and plant location codes in the pest and disease database.

[0065] In this embodiment, the preset number of images refers to a threshold value of the number of original images used to match the identified pest and disease types when the system determines whether the plant has pests and diseases.

[0066] In this embodiment, the risk level is a quantitative evaluation of the plant disease and insect pest situation.

[0067] In this embodiment, the level conversion function converts a set of original parameter values ​​into a final risk level.

[0068] In this embodiment, the health conversion function is a mathematical model used to assess and quantify the health status of a plant.

[0069] In this embodiment, the parameter conversion function is a function used to convert some raw data (such as pest and disease area, healthy days, environmental factors, etc.) into standardized numerical values.

[0070] In this embodiment, the pest database is a database system for storing and managing pest related information.

[0071] In this embodiment, the location code refers to a code information used to uniquely identify the location of the plant.

[0072] The working principle and beneficial effects of the above technical solution are: by matching the symbiotic feature values ​​of the plant image with the type of pests and diseases, combining the pest and disease area, healthy days and environmental factors, the risk level of the plant is calculated, and the pest and disease type, risk level and location code are stored in the database to achieve accurate monitoring and real-time recording, thereby improving agricultural management efficiency.

[0073] Embodiment 7: Based on the above embodiment 6, the pest and disease treatment module includes: Report generation unit: according to the preset time interval, retrieve the pest data from the pest database through the interface, input the pest data based on the report template, and output the pest analysis report, which includes the pest type, risk level and location code of the plant; Early warning unit: The system automatically matches the relevant basic prevention and control measures in the expert database according to the pest and disease types in the pest and disease analysis report. The system formulates personalized treatment plans based on the dynamic factors of grape varieties, environmental parameters, and growth stages; Tracking unit: The system supports the follow-up tracking and evaluation of personalized treatment plans to ensure the effectiveness of the treatment measures.

[0074] In this embodiment, the preset time interval refers to a set fixed time period for collecting pest and disease data.

[0075] In this embodiment, the report template is a standardized format in the pest and disease treatment module, and is used to organize and display key information in the pest and disease analysis report.

[0076] In this embodiment, the pest and disease analysis report is a detailed report automatically generated based on the pest and disease monitoring system, and is used to analyze and summarize the pest and disease conditions suffered by crops (grapes) within a certain period of time.

[0077] In this embodiment, the basic means of prevention and control refer to basic prevention and control measures taken for specific pests and diseases based on the type, characteristics and environmental factors of the pests and diseases.

[0078] In this embodiment, the personalized treatment plan refers to a set of targeted prevention and control measures customized according to the specific data in the pest and disease analysis report and the actual situation of grape planting (such as grape variety, environmental parameters, growth stage, etc.).

[0079] In this embodiment, tracking and evaluation is a key link in the pest control module, which aims to ensure that the implemented personalized treatment plan can achieve the expected effect and continuously monitor and optimize the effects of pest control measures.

[0080] The working principle and beneficial effects of the above technical solution are: generating reports and warnings through pest and disease analysis results, automatically matching prevention and control measures from the expert database, and formulating personalized treatment plans based on dynamic factors to ensure subsequent tracking and evaluation, and improve the accuracy and effectiveness of pest and disease control.

[0081] Embodiment 8: Based on the above embodiment 1, the data visualization module includes: Visualization unit: Based on different data types, design visualization chart layouts of corresponding data types to display agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results in a visual way; Screening unit: provides interactive functions, allowing users to select different time ranges, growth stages, and data types for screening as needed, and view the changing trends and specific conditions of related data; Real-time update unit: The big data visualization platform combines the GIS map of the target grape base to obtain and update data of all data types in real time, providing intuitive decision support for growers.

[0082] In this embodiment, data types refer to different types of data collected, processed and displayed in the system, including: farming operation information, agricultural monitoring data, and pest and disease analysis results.

[0083] In this embodiment, the visual chart layout includes: a bar chart, a scatter chart, a radar chart, etc.

[0084] In this embodiment, the interactive function refers to a function in which a user interacts with the system in real time so as to dynamically adjust data display, screening and analysis results according to user needs.

[0085] In this embodiment, the change trend refers to the increase or decrease of data between different time points as time goes by.

[0086] In this embodiment, the specific situation refers to detailed information related to the filtering conditions and data types selected by the user. For example, the user can select a specific time period, grape growth stage, or a specific agricultural monitoring data type (such as temperature, humidity, pests and diseases, etc.), and the system will display relevant detailed data based on these selections.

[0087] The working principle and beneficial effects of the above technical solution are: displaying agricultural operations, pest and disease analysis and treatment tracking results through visual charts, supporting interactive screening and real-time data updates, and combining GIS maps to provide grape growers with intuitive decision support, thereby improving the efficiency and accuracy of agricultural management.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A GIS-based integrated grape planting management system, characterized in that: include: Data acquisition module: Based on GIS map technology, it obtains the basic geographic information of the target grape base and records the agricultural operation information of grapes at each growth stage of the target grape base; Agricultural condition monitoring data module: collects meteorological data and soil moisture data of the target grape base in real time through IoT devices to form agricultural condition monitoring data; Pest and disease analysis and prediction module: Based on agricultural monitoring data and agricultural operation information, the system conducts pest and disease analysis and prediction on the target grape base to obtain pest and disease analysis results; Pest and disease treatment module: Based on the results of pest and disease analysis, the system reports and warns, and formulates corresponding treatment plans according to the expert database; Data visualization module: The system displays agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results in a visual manner to provide intuitive decision support.

2. The GIS-based integrated grape planting management system according to claim 1, characterized in that: The data acquisition module comprises: Target determination unit: determine the target grape base for research and divide the basic monitoring scope of the target grape base; GIS unit: Select the preset GIS map technology, obtain basic geographic information based on the basic monitoring range of the target grape base, and import the target grape base into a two-dimensional geographic view, wherein the minimum unit value of the coordinate can be customized by the user; Basic planting information unit: obtain basic information on grape planting according to the preset information of the target grape base.

3. The GIS-based integrated grape planting management system according to claim 1, characterized in that: The data acquisition module further includes: Farming operation unit: records the farming operations of the target grape base at each growth stage according to the growth cycle of grapes, and enters the farming operations into the system; Database unit: Build a planting database and store agricultural operations in the system's planting database using time and growth stage as joint primary keys.

4. The GIS-based integrated grape planting management system according to claim 1, characterized in that: The agricultural monitoring data module includes: Target setting unit: clarify the types of agricultural data that need to be collected, including meteorological data and soil moisture data; Sensor type unit: select the sensor type of the detection item according to the type of agricultural data; Matching unit: based on basic geographic information, select all sensor installation locations, obtain the maximum matching degree between any sensor installation location and any sensor type as the matching degree of the sensor installation location, sort all sensor installation locations according to the matching degree, and generate a list consistent with the number of sensor types, which is the list of optimal sensor installation locations; Installation unit: based on the matching relationship between any sensor installation position and sensor type in the sensor optimal installation position list, install the sensor and obtain agricultural monitoring data in real time; Select IoT devices to connect to all sensors of the target grape base and transmit agricultural monitoring data into the system.

5. The GIS-based integrated grape planting management system according to claim 1, characterized in that: The pest analysis and prediction module includes: Image analysis unit: based on agricultural monitoring data and agricultural operation information, obtain an original image group of any plant grape growth stage, perform image denoising standard processing on any original image in the original image group, and obtain a standard image; Each element of the standard image is gray-scaled to obtain a gray-scale image of the standard image, and a gray-level co-occurrence matrix is ​​constructed based on the pixel value of each pixel. ,in Indicates the pixel with horizontal coordinate i and vertical coordinate j in the grayscale image in a given direction and the symbiosis value of the corresponding pixel at distance d; The symbiotic characteristic value of any pixel point of the grayscale image obtained based on the symbiotic relationship matrix is: ;in, represents the co-occurrence feature value of the pixel with horizontal coordinate i and vertical coordinate j in the grayscale image, Represents the pixel value of the pixel with abscissa i and ordinate j. Indicates the distance direction of the pixel point with horizontal coordinate i and vertical coordinate j and the pixel value of the pixel at distance d, ( ) represents the texture function, n represents the number of pixels in n directions, and m represents the maximum distance is m; The symbiotic characteristic values ​​of all pixels of the grayscale image are obtained, and the symbiotic characteristic values ​​of any pests and diseases in the system expert database are compared to analyze the pests and diseases that may exist in the grayscale image.

6. The GIS-based integrated grape planting management system according to claim 5, characterized in that: The pest analysis and prediction module also includes: Pest and disease analysis unit: matching the symbiotic feature values ​​of the original image group of the growth stage with the pest and disease type according to the pest and disease type, and if the number of successful matches is greater than the number of preset images, determining that the plant has the pest and disease; Risk level unit: Based on the identified pest and disease plant conditions, the risk level of the plant is obtained as follows: ;in, Indicates the risk level of the plant in question, ( ) represents the level conversion function, represents the weight coefficient of pest area, represents the weight coefficient of healthy days, represents the weight coefficient of environmental factors, Indicates the maximum area of ​​pests and diseases on the plant, represents the total area of ​​the plant, ( ) represents the health conversion function, Indicates the number of days that the plant has been exposed to pests and diseases, represents the kth environmental parameter, q represents a total of q environmental parameters, R( ) represents the parameter conversion function, represents the standard planting value of the kth environmental parameter; Preservation unit: construct a pest and disease database, and store the identified pest and disease types, risk levels and plant location codes in the pest and disease database.

7. The GIS-based integrated grape planting management system according to claim 6, characterized in that: The pest and disease treatment module comprises: Report generation unit: according to the preset time interval, retrieve the pest data from the pest database through the interface, input the pest data based on the report template, and output the pest analysis report, which includes the pest type, risk level and location code of the plant; Early warning unit: The system automatically matches the relevant basic prevention and control measures in the expert database according to the pest and disease types in the pest and disease analysis report. The system formulates personalized treatment plans based on the dynamic factors of grape varieties, environmental parameters, and growth stages; Tracking unit: The system supports the follow-up tracking and evaluation of personalized treatment plans to ensure the effectiveness of the treatment measures.

8. The GIS-based integrated grape planting management system according to claim 1, characterized in that: The data visualization module includes: Visualization unit: Based on different data types, design visualization chart layouts of corresponding data types to display agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results in a visual way; Screening unit: provides interactive functions, allowing users to select different time ranges, growth stages, and data types for screening as needed, and view the changing trends and specific conditions of related data; Real-time update unit: The big data visualization platform combines the GIS map of the target grape base to obtain and update data of all data types in real time, providing intuitive decision support for growers.

Images