A GIS-based integrated grape planting management system

Abstract

The present invention provides a GIS-based integrated grape cultivation management system, belonging to the technical field of management systems. The system includes the following modules: a data acquisition module, which uses GIS mapping technology to acquire basic geographic information about grape bases and record agricultural operation information for grapes at each growth stage; an agricultural monitoring data module, which uses Internet of Things (IoT) devices to collect real-time meteorological and soil moisture data for target grape bases; a pest and disease analysis and prediction module, which analyzes and predicts pests and diseases for target grape bases based on agricultural monitoring data and agricultural operation information; a pest and disease treatment module, which reports and issues warnings based on pest and disease analysis results and formulates corresponding treatment plans based on an expert database; and a data visualization module, which visualizes agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results, providing intuitive decision support. Refined agricultural operations and pest and disease control improve the efficiency and scientific nature of agricultural production.

Description

Technical Field

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

[0002] Currently, grape cultivation is one of the most economically important agricultural sectors worldwide, particularly in regions with favorable climatic conditions, such as the Mediterranean and temperate zones, where it has become a major agricultural activity. Grapes are not only an important source of fruit for consumption, but are also widely used in the production of wine, juice, dried fruit, and other products. However, grape cultivation faces numerous challenges, including environmental changes, pest and disease control, soil management, irrigation, and fertilization. Improper management can lead to yield declines.

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

[0004] This invention provides a GIS-based integrated grape cultivation management system. By integrating GIS maps, the Internet of Things, and big data analysis technologies, it creates a highly integrated and precise agricultural management platform. This system helps growers monitor the operation of their grape bases in real time, enabling them to conduct refined agricultural operations and pest control, thereby improving the efficiency and scientific nature of agricultural production.

[0005] In one aspect, the present invention provides a GIS-based integrated grape planting management system, comprising:

[0006] Data acquisition module: Based on GIS map technology, it obtains the basic geographical information of the target grape base and records the agricultural operation information of the grapes at each growth stage of the target grape base;

[0007] Agricultural monitoring data module: collects meteorological data and soil moisture data of the target grape base in real time through IoT devices, which together form agricultural monitoring data;

[0008] Pest and disease analysis and prediction module: Based on agricultural monitoring data and farming operation information, the system analyzes and predicts pests and diseases for the target grape base and obtains pest and disease analysis results;

[0009] Pest and disease treatment module: Based on the results of pest and disease analysis, the system reports and issues early warnings, and formulates corresponding treatment plans according to the expert database;

[0010] 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, providing intuitive decision support.

[0011] On the other hand, the data acquisition module includes:

[0012] Target determination unit: determine the target grape base for research and divide the basic monitoring scope of the target grape base;

[0013] GIS unit: Select the preset GIS mapping technology to 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, where the minimum coordinate unit value can be customized by the user;

[0014] Basic planting information unit: obtains basic information on grape planting based on the preset information of the target grape base.

[0015] On the other hand, the data acquisition module further includes:

[0016] Farming operation unit: records the farming operations of the target grape base at each growth stage according to the grape growth cycle, and enters the farming operations into the system;

[0017] 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.

[0018] On the other hand, the agricultural monitoring data module includes:

[0019] Target setting unit: clearly define the types of agricultural data that need to be collected, including meteorological data and soil moisture data;

[0020] Sensor type unit: select the sensor type of the detection item according to the type of agricultural data;

[0021] 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 with the same number of sensor types. The list is the optimal sensor installation location list;

[0022] 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;

[0023] Select IoT devices to connect to all sensors in the target grape base and transmit agricultural monitoring data into the system.

[0024] On the other hand, the pest and disease analysis and prediction module includes:

[0025] Image analysis unit: Based on agricultural monitoring data and agricultural operation information, obtain a group of original images of any grape plant at any growth stage, and perform standard image denoising processing on any original image in the group of original images to obtain a standard image;

[0026] Perform grayscale processing on each element of the standard image to obtain a grayscale image of the standard image, and construct a grayscale co-occurrence matrix based on the pixel value of each pixel ,in Indicates the pixel point with horizontal coordinate i and vertical coordinate j in the grayscale image in a given direction and the symbiotic relationship value of the corresponding pixel at distance d;

[0027] The symbiotic eigenvalue of any pixel point of the grayscale image obtained based on the symbiotic relationship matrix is:

[0028] ;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 point with horizontal coordinate i and vertical coordinate j, Indicates the distance direction of the pixel point with horizontal coordinate i and vertical coordinate j And the pixel value of the pixel point at distance d, ( ) represents the texture function, n represents the number of pixels in n directions, and m represents the maximum distance is m;

[0029] The symbiotic characteristic values ​​of all pixels in the grayscale image are obtained, and the possible pests and diseases in the grayscale image are analyzed by comparing them with the symbiotic characteristic values ​​of any pest and disease in the system expert database.

[0030] On the other hand, the pest and disease analysis and prediction module also includes:

[0031] a 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 determining that the plant has the pest and disease if the number of successful matches is greater than a preset number of images;

[0032] Risk level unit: Based on the identified pest and disease plant conditions, the risk level of the plant is obtained as follows:

[0033] ;in, Indicates the risk level of the plant in question, ( ) represents the level conversion function, represents the area weight coefficient of pests and diseases, 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 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;

[0034] Preservation unit: Build 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.

[0035] On the other hand, the pest and disease treatment module includes:

[0036] Report generation unit: retrieves pest and disease data from the pest and disease database through the interface at preset time intervals, inputs pest and disease data based on a report template, and outputs a pest and disease analysis report, which includes the pest and disease type, risk level, and location code of the plant;

[0037] Early Warning Unit: The system automatically matches the relevant basic prevention and control measures in the expert database based on the pest and disease analysis report type. The system also develops personalized treatment plans based on the grape variety, environmental parameters, and dynamic factors of the growth stage.

[0038] Tracking unit: The system supports the follow-up tracking and evaluation of personalized treatment plans to ensure the effectiveness of the disposal measures.

[0039] On the other hand, the data visualization module includes:

[0040] Visualization unit: Based on different data types, design visualization chart layouts corresponding to the data types to visualize agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results;

[0041] Screening unit: provides interactive functions, allowing users to select different time ranges, growth stages, and data types as needed to screen and view the changing trends and specific conditions of related data;

[0042] 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.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] This invention provides a GIS-based integrated grape cultivation management system. By integrating GIS maps, the Internet of Things, and big data analysis technologies, it creates a highly integrated and precise agricultural management platform. This system helps growers monitor the operation of their grape bases in real time, enabling them to conduct refined agricultural operations and pest control, thereby improving the efficiency and scientific nature of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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 any creative work.

[0046] Figure 1 This is a schematic structural diagram of a GIS-based integrated grape planting management system provided by an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the architecture of the integrated grape planting management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0049] Example 1:

[0050] like Figure 1 As shown, an embodiment of the present invention provides a GIS-based integrated grape planting management system, including:

[0051] Data acquisition module: Based on GIS map technology, it obtains the basic geographical information of the target grape base and records the agricultural operation information of the grapes at each growth stage of the target grape base;

[0052] Agricultural monitoring data module: collects meteorological data and soil moisture data of the target grape base in real time through IoT devices, which together form agricultural monitoring data;

[0053] Pest and disease analysis and prediction module: Based on agricultural monitoring data and farming operation information, the system analyzes and predicts pests and diseases for the target grape base and obtains pest and disease analysis results;

[0054] Pest and disease treatment module: Based on the results of pest and disease analysis, the system reports and issues early warnings, and formulates corresponding treatment plans according to the expert database;

[0055] 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, providing intuitive decision support.

[0056] In this embodiment, GIS mapping technology is a technology used to capture, store, manage, analyze, display and interpret geographic spatial data.

[0057] In this embodiment, the target grape base refers to a specific area or farm for grape cultivation in an agricultural management system.

[0058] 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.

[0059] In this embodiment, the growth stage refers to the different growth and development processes of grapes from planting to harvesting.

[0060] In this embodiment, the agricultural operation information refers to all operations and activity records related to crop management during the grape planting process.

[0061] 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.

[0062] In this embodiment, the target grape base refers to a specific grape planting area that is the focus of, monitored, or managed in the grape planting management system.

[0063] 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.

[0064] In this embodiment, 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.

[0065] 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.

[0066] In this embodiment, pest and disease analysis and prediction refers to predicting possible pest and disease conditions in grape planting bases by analyzing agricultural monitoring data, historical agricultural operation information, meteorological data, soil information, etc., using data analysis and machine learning algorithms.

[0067] 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.

[0068] In this embodiment, the expert database is a database that integrates the knowledge, suggestions and best practices of multiple agricultural experts, researchers, technicians, etc.

[0069] In this embodiment, visualization is to present 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.

[0070] In this embodiment, the architecture of the grape planting integrated management system based on GIS is as follows: Figure 2 shown.

[0071] The working principle and beneficial effects of this technical solution are as follows: GIS mapping technology and IoT devices are used to obtain real-time geographic, meteorological, and soil data from grape production bases, and combined with agricultural operation information to predict and analyze pests and diseases. The system automatically generates early warning reports and provides expert treatment solutions. Ultimately, data visualization supports accurate decision-making, improving the efficiency and sustainability of grape cultivation management.

[0072] Example 2:

[0073] Based on the above embodiment 1, the data acquisition module includes:

[0074] Target determination unit: determine the target grape base for research and divide the basic monitoring scope of the target grape base;

[0075] GIS unit: Select the preset GIS mapping technology to 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, where the minimum coordinate unit value can be customized by the user;

[0076] Basic planting information unit: obtains basic information on grape planting based on the preset information of the target grape base.

[0077] In this embodiment, the basic monitoring range refers to the monitoring and management area selected for a certain grape base.

[0078] In this embodiment, the two-dimensional geographic view refers to a visualization interface that displays geographic information in the form of a plane map.

[0079] In this embodiment, the minimum unit value of coordinates refers to the accuracy of coordinate representation in map space data in a geographic information system.

[0080] In this embodiment, the preset information refers to basic data and parameters related to grape cultivation 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.

[0081] In this embodiment, the basic information of grape cultivation refers to key data and parameters related to grape cultivation.

[0082] The working principle and beneficial effects of this technical solution are as follows: Target grape bases and their monitoring areas are identified through GIS technology, geographic information is imported, and displayed in a two-dimensional view, allowing users to customize coordinate units. By acquiring basic grape cultivation information, precise monitoring and data management are achieved, effectively improving the scientific nature and operability of cultivation management.

[0083] Example 3:

[0084] Based on the above embodiment 1, the data acquisition module further includes:

[0085] Farming operation unit: records the farming operations of the target grape base at each growth stage according to the grape growth cycle, and enters the farming operations into the system;

[0086] 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.

[0087] In this embodiment, the growth cycle refers to the entire process from the planting of grape seedlings to the full maturity and harvesting of grape fruits.

[0088] In this embodiment, the planting database is a database system for storing, managing and analyzing various data in the agricultural production process.

[0089] In this embodiment, the joint primary key refers to a primary key composed of two or more fields in the database.

[0090] The working principle and beneficial effects of the above technical solution are as follows: by recording agricultural operations at each growth stage of grapes and storing them in a cultivation database, using time and growth stage as a joint primary key for management, accurate agricultural data tracking and analysis are achieved, helping to improve management efficiency and scientific decision-making.

[0091] Example 4:

[0092] Based on the above embodiment 1, the agricultural monitoring data module includes:

[0093] Target setting unit: clearly define the types of agricultural data that need to be collected, including meteorological data and soil moisture data;

[0094] Sensor type unit: select the sensor type of the detection item according to the type of agricultural data;

[0095] 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 with the same number of sensor types. The list is the optimal sensor installation location list;

[0096] 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;

[0097] Select IoT devices to connect to all sensors in the target grape base and transmit agricultural monitoring data into the system.

[0098] 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, and soil conditions during the agricultural production process.

[0099] In this embodiment, the meteorological data type refers to data reflecting various meteorological elements in the atmosphere, including temperature, humidity, precipitation, etc.

[0100] In this embodiment, the soil moisture data type refers to data types related to soil moisture and related conditions, including soil moisture, soil temperature, soil density, etc.

[0101] In this embodiment, the sensor type refers to hardware equipment used to collect information such as farmland environment, meteorological conditions, and soil moisture.

[0102] In this embodiment, the maximum matching degree is an indicator for measuring the degree of compatibility between the sensor installation position and the sensor type.

[0103] In this embodiment, the optimal installation position list is a sorted list containing the best installation position selected for each sensor type.

[0104] The working principle and beneficial effects of this technical solution are as follows: By clarifying the type of agricultural data to be collected, selecting appropriate sensors, and optimizing sensor installation locations based on geographic information, precise deployment and efficient data collection are achieved. Through the integration of IoT devices, agricultural data can be acquired and transmitted in real time, improving monitoring accuracy and management efficiency.

[0105] Example 5:

[0106] Based on the above embodiment 1, the pest and disease analysis and prediction module includes:

[0107] Image analysis unit: Based on agricultural monitoring data and agricultural operation information, obtain a group of original images of any grape plant at any growth stage, and perform standard image denoising processing on any original image in the group of original images to obtain a standard image;

[0108] Perform grayscale processing on each element of the standard image to obtain a grayscale image of the standard image, and construct a grayscale co-occurrence matrix based on the pixel value of each pixel ,in Indicates the pixel point with horizontal coordinate i and vertical coordinate j in the grayscale image in a given direction and the symbiotic relationship value of the corresponding pixel at distance d;

[0109] The symbiotic eigenvalue of any pixel point of the grayscale image obtained based on the symbiotic relationship matrix is:

[0110] ;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 point with horizontal coordinate i and vertical coordinate j, Indicates the distance direction of the pixel point with horizontal coordinate i and vertical coordinate j And the pixel value of the pixel point at distance d, ( ) represents the texture function, n represents the number of pixels in n directions, and m represents the maximum distance is m;

[0111] The symbiotic characteristic values ​​of all pixels in the grayscale image are obtained, and the possible pests and diseases in the grayscale image are analyzed by comparing them with the symbiotic characteristic values ​​of any pest and disease in the system expert database.

[0112] 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 an agricultural monitoring system.

[0113] 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.

[0114] In this embodiment, the standard image refers to an image that has been subjected to denoising processing.

[0115] In this embodiment, grayscale processing is a common technology in image processing, which is used to convert a color image into a grayscale image.

[0116] In this embodiment, the grayscale image refers to an image that has been subjected to image denoising and normalization processing, and each pixel value represents brightness information.

[0117] In this embodiment, the gray level co-occurrence matrix is ​​a method used in image processing to describe the spatial relationship of pixel gray values ​​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 the image texture.

[0118] In this embodiment, the symbiosis relationship value refers to the frequency of occurrence of the grayscale value of a pixel point in a grayscale co-occurrence matrix at a specified direction and distance relative to the grayscale value of the corresponding pixel point.

[0119] In this embodiment, the co-occurrence feature value represents the spatial relationship and texture pattern between different pixel gray levels in the image.

[0120] 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.

[0121] Example 6:

[0122] Based on the above embodiment 5, the pest and disease analysis and prediction module further includes:

[0123] a 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 determining that the plant has the pest and disease if the number of successful matches is greater than a preset number of images;

[0124] Risk level unit: Based on the identified pest and disease plant conditions, the risk level of the plant is obtained as follows:

[0125] ;in, Indicates the risk level of the plant in question, ( ) represents the level conversion function, represents the area weight coefficient of pests and diseases, 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 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;

[0126] Preservation unit: Build 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.

[0127] 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.

[0128] In this embodiment, the risk level is a quantitative evaluation of the plant disease and insect pest situation.

[0129] In this embodiment, the level conversion function converts a set of original parameter values ​​into a final risk level.

[0130] In this embodiment, the health conversion function is a mathematical model used to evaluate and quantify the health status of a plant.

[0131] 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.

[0132] In this embodiment, the pest and disease database is a database system for storing and managing pest and disease related information.

[0133] In this embodiment, the location code refers to a coding information used to uniquely identify the location of the plant.

[0134] 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 pest and disease, 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.

[0135] Example 7:

[0136] Based on the above embodiment 6, the pest and disease treatment module includes:

[0137] Report generation unit: retrieves pest and disease data from the pest and disease database through the interface at preset time intervals, inputs pest and disease data based on a report template, and outputs a pest and disease analysis report, which includes the pest and disease type, risk level, and location code of the plant;

[0138] Early Warning Unit: The system automatically matches the relevant basic prevention and control measures in the expert database based on the pest and disease analysis report type. The system also develops personalized treatment plans based on the grape variety, environmental parameters, and dynamic factors of the growth stage.

[0139] Tracking unit: The system supports the follow-up tracking and evaluation of personalized treatment plans to ensure the effectiveness of the disposal measures.

[0140] In this embodiment, the preset time interval refers to a set fixed time period for collecting pest and disease data.

[0141] 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.

[0142] In this embodiment, the pest and disease analysis report is a detailed report automatically generated by 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.

[0143] In this embodiment, basic prevention and control means 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.

[0144] In this embodiment, the personalized treatment plan refers to a set of targeted prevention and control measures customized based on the specific data in the pest and disease analysis report and the actual conditions of grape cultivation (such as grape variety, environmental parameters, growth stage, etc.).

[0145] In this embodiment, tracking and evaluation is a key link in the pest and disease treatment module, which aims to ensure that the implemented personalized treatment plan can achieve the expected results and continuously monitor and optimize the effects of pest and disease control measures.

[0146] 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 methods 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.

[0147] Example 8:

[0148] Based on the above embodiment 1, the data visualization module includes:

[0149] Visualization unit: Based on different data types, design visualization chart layouts corresponding to the data types to visualize agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results;

[0150] Screening unit: provides interactive functions, allowing users to select different time ranges, growth stages, and data types as needed to screen and view the changing trends and specific conditions of related data;

[0151] 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.

[0152] 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.

[0153] In this embodiment, the visual chart layout includes: a bar chart, a scatter plot, a radar chart, etc.

[0154] 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.

[0155] In this embodiment, the change trend refers to the increase or decrease of data between different time points over time.

[0156] 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.

[0157] 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-making support, thereby improving the efficiency and accuracy of agricultural management.

[0158] 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 various 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 geographical information of the target grape base and records the agricultural operation information of the grapes at each growth stage of the target grape base; Agricultural monitoring data module: collects meteorological data and soil moisture data of the target grape base in real time through IoT devices, which together form agricultural monitoring data; Pest and disease analysis and prediction module: Based on agricultural monitoring data and farming operation information, the system analyzes and predicts pests and diseases for the target grape base and obtains pest and disease analysis results; Pest and disease treatment module: Based on the results of pest and disease analysis, the system reports and issues early warnings, 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, providing intuitive decision support; The pest and disease analysis and prediction module includes: Image analysis unit: Based on agricultural monitoring data and agricultural operation information, obtain a group of original images of any grape plant at any growth stage, and perform standard image denoising processing on any original image in the group of original images to obtain a standard image; Perform grayscale processing on each element of the standard image to obtain a grayscale image of the standard image, and construct a grayscale co-occurrence matrix based on the pixel value of each pixel ,in Indicates the pixel point with horizontal coordinate i and vertical coordinate j in the grayscale image in a given direction and the symbiotic relationship value of the corresponding pixel at distance d; The symbiotic eigenvalue 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 point with horizontal coordinate i and vertical coordinate j, Indicates the distance direction of the pixel point with horizontal coordinate i and vertical coordinate j And the pixel value of the pixel point 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 in the grayscale image are obtained, and the possible pests and diseases in the grayscale image are analyzed by comparing them with the symbiotic characteristic values ​​of any pest and disease in the system expert database.

2. The GIS-based integrated grape planting management system according to claim 1, characterized in that: 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 mapping technology to 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, where the minimum coordinate unit value can be customized by the user; Basic planting information unit: obtains basic information on grape planting based on 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 grape growth cycle, 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: clearly define 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 with the same number of sensor types. The list is the optimal sensor installation location list; 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 in 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 and disease analysis and prediction module also includes: a 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 determining that the plant has the pest and disease if the number of successful matches is greater than a preset number of images; 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 area weight coefficient of pests and diseases, 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 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: Build 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.

6. The GIS-based integrated grape planting management system according to claim 5, characterized in that: The pest and disease treatment module includes: Report generation unit: retrieves pest and disease data from the pest and disease database through the interface at preset time intervals, inputs pest and disease data based on a report template, and outputs a pest and disease analysis report, which includes the pest and disease 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 based on the pest and disease analysis report type. The system also develops personalized treatment plans based on the grape variety, environmental parameters, and dynamic factors of the growth stage. Tracking unit: The system supports the follow-up tracking and evaluation of personalized treatment plans to ensure the effectiveness of the disposal measures.

7. 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 corresponding to the data types to visualize agricultural operation information, agricultural monitoring data, pest and disease analysis results, and pest and disease treatment tracking results; Screening unit: provides interactive functions, allowing users to select different time ranges, growth stages, and data types as needed to screen 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.

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