Urban tree information rapid acquisition method and system

Abstract

The invention relates to the technical field of tree information acquisition, in particular to an urban tree information rapid acquisition method and system, and the method comprises the following steps: obtaining the geographic coordinates of each tree in a city through a GNSS receiver, recording the accuracy and timestamp information of each position, executing data synchronization, and generating urban tree coordinate data. According to the method, the GNSS receiver and the microwave remote sensing equipment are introduced, high-precision and multi-dimensional analysis of tree data is ensured, the GNSS receiver provides accurate coordinates and timestamps, the real-time performance and accuracy of geographic data are enhanced, and the microwave remote sensing technology accurately evaluates the age and health condition of the tree by analyzing the dielectric constant of the tree, so that the reliability of the tree is improved. The image sensor capable of automatically adjusting the focal length and the exposure parameters can capture the color and the shape of the leaves in detail, the detail degree and the accuracy of disease and insect pest diagnosis are improved, tree information mapping is updated in real time, the efficiency of environment monitoring and urban planning is improved, and powerful support is provided for management of an urban ecological system.

Description

Technical Field

[0001] The present invention relates to the technical field of tree information collection, and particularly to a method and system for quickly collecting urban tree information. Background Art

[0002] The technical field of tree information collection involves using various methods and systems to collect and analyze data on urban trees, combining remote sensing technology, geographic information systems, data analysis, and environmental science to provide precise information such as the location, species, health status, and growth parameters of trees. By using technologies such as satellite images, drones, laser scanning, or ordinary digital photography, a large amount of tree data can be quickly collected. In addition, this field also includes data processing and analysis technologies that can automatically identify tree species and evaluate the health status of trees, which is crucial for urban planning, environmental monitoring, and ecological protection.

[0003] Among them, the method for quickly collecting urban tree information refers to a technical method designed specifically for quickly and efficiently collecting and analyzing urban tree data. Its main uses include, but are not limited to, monitoring the urban greening status, planning urban green spaces, managing urban forest resources, and evaluating and preventing environmental risks caused by tree health problems. By quickly collecting tree information, urban planners and environmental scientists can better understand and manage the urban ecosystem, improve the quality of the urban living environment, and at the same time provide data support for the sustainable development of the city.

[0004] The existing technologies mainly rely on satellite images, drones, and laser scanning, etc. Although they have a wide coverage, they have deficiencies in real-time data update and precise capture of individual tree data. The limitations lie in the timeliness and detail resolution of the data, and they cannot effectively capture the minute changes in the rapidly changing urban environment, resulting in a lag in data processing and affecting the timely assessment and fine management of the tree health status. For example, the update frequency and resolution limitations of satellite and drone images may miss the initial stage of tree diseases, leading to delays in response measures to diseases and increasing the challenges and risks of urban greening management. Summary of the Invention

[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art, and to propose a method and system for quickly collecting urban tree information.

[0006] To achieve the above purpose, the present invention adopts the following technical scheme: A method for quickly collecting urban tree information, comprising the following steps: S1: Obtain the geographical coordinates of each tree in the city through a GNSS receiver, record the accuracy and timestamp information of each location, perform data synchronization, and generate urban tree coordinate data; S2: Combine the urban tree coordinate data, guide the microwave remote sensing device to aim at the target tree, emit microwave signals from different angles and frequencies, measure the time delay and intensity change of the returned signals, calculate the tree dielectric constant, and obtain the measured value of the tree dielectric constant; S3: Based on the measured value of the tree dielectric constant, define the tree age estimation rule, score each dielectric constant value, perform summary processing on the scoring data, judge the age range of the tree according to the set threshold, and obtain the predicted tree age result; S4: Based on the urban tree coordinate data, combine with an image sensor, automatically adjust the focal length and exposure parameters, take pictures of the tree, analyze the color and shape changes of the tree leaves, and generate the basic health data of the tree; S5: Based on the basic health data of the tree, capture the detailed images of the leaves, analyze the color distribution and texture changes in the images, identify the signs of different diseases on the leaves through visual information, evaluate the health status of the tree, and generate the pest and disease diagnosis result; S6: Based on the urban tree coordinate data, the predicted tree age result and the tree pest and disease diagnosis result, update the geographical location map of the tree in real time, comprehensively evaluate and mark the health status and growth environment of the tree, and generate the real-time urban tree information mapping result.

[0007] As a further solution of the present invention, the urban tree coordinate data includes geographical location coordinates, accuracy level and time record, the measured value of the tree dielectric constant includes dielectric constant value, signal delay data and signal intensity data, the predicted tree age result includes age scoring record, age interval classification result and scoring summary data, the basic health data of the tree includes leaf color feature record and leaf shape feature record, the pest and disease diagnosis result includes color distribution feature, texture change feature and health anomaly index, and the real-time urban tree information mapping result includes geographical location update record, health status identification and environmental assessment result.

[0008] As a further solution of the present invention, the specific steps of obtaining the geographical coordinates of each tree in the city through a GNSS receiver, recording the accuracy and timestamp information of each location, and performing data synchronization to generate urban tree coordinate data are as follows: S101: Use a GNSS receiver to measure the geographical coordinates of each tree within the city range, synchronously record the accuracy and timestamp of each coordinate point, correct signal drift, update the positioning data, and generate accurate tree coordinate information; S102: Based on the accurate tree coordinate information, clean the data, delete coordinate outliers, format the coordinate data into a unified format, perform data standardization, and obtain formatted tree data; S103: Based on the formatted tree data, perform data synchronization through the urban database interface, upload the cleaned and formatted data, verify data integrity, update database records, and obtain urban tree coordinate data.

[0009] As a further solution of the present invention, in combination with the urban tree coordinate data, guide the microwave remote sensing device to aim at the target tree, emit microwave signals from different angles and frequencies, measure the time delay and intensity change of the returned signal, calculate the dielectric constant of the tree, and the specific steps for obtaining the measured value of the tree dielectric constant are as follows: S201: Based on the urban tree coordinate data, adjust the direction and focus of the microwave remote sensing device to aim at the target tree, adjust the device angle to capture the optimal signal, ensure that the microwave covers the target area from multiple angles, and obtain microwave orientation data; S202: Based on the microwave orientation data, adjust the device configuration to emit microwaves from different frequencies, monitor the returned signals at multiple frequencies, record the time delay and signal intensity of each frequency, and obtain microwave signal response data; S203: Based on the microwave signal response data, calculate the time delay and intensity change of the returned signals at different frequencies, deduce the dielectric constant of the tree through data, repeat the measurement to verify the consistency of the data, and obtain the measured value of the tree dielectric constant.

[0010] As a further solution of the present invention, based on the measured value of the tree dielectric constant, define the tree age estimation rule, score each dielectric constant value, perform summary processing on the scored data, and judge the age range of the tree according to the set threshold to obtain the specific steps of the predicted tree age result as follows: S301: Based on the measured value of the tree dielectric constant, define the correlation scoring standard between the tree age and the dielectric constant, assign age-related scores to each measured value according to the numerical size, reveal the direct correlation between each score and the dielectric constant, and obtain the tree age scoring data; S302: Based on the tree age scoring data, perform summary of the tree scores, identify the differences in scores between different tree species through aggregation processing, adjust the abnormal scoring data, and optimize the consistency of the scores through data purification to obtain the summary scoring result; S303: Based on the summary scoring result, according to the preset age determination threshold, determine the age interval of the scoring data of each tree, classify the age range of each tree by comparing the relationship between the score and the threshold, and obtain the predicted tree age result.

[0011] As a further solution of the present invention, based on the urban tree coordinate data, in combination with an image sensor, automatically adjust the focal length and exposure parameters, take pictures of the trees, analyze the color and shape changes of the tree leaves, and the specific steps for generating the basic health data of the trees are as follows: S401: Based on the urban tree coordinate data, configure the image sensor, automatically adjust the focal length and exposure parameters, match the position and lighting conditions of each tree, ensure the clarity and exposure balance of the image, and obtain the optimized image capture setting results; S402: Based on the optimized image capture setting results, take photos of each tree, capture the color and shape of the tree leaves, and record the tree morphology to obtain the tree morphology image data; S403: Based on the tree morphology image data, analyze the leaf color changes and shape abnormalities, evaluate the health status of the tree, and obtain the basic tree health data.

[0012] As a further solution of the present invention, based on the basic tree health data, capture the detailed images of the tree leaves, analyze the color distribution and texture changes in the images, identify the signs of different diseases on the leaves through visual information, evaluate the health status of the tree, and the specific steps for generating the pest and disease diagnosis results are as follows. S501: Based on the basic tree health data, analyze the leaf details, optimize the exposure and contrast to highlight the leaf texture and color differences, and obtain the fine tree leaf image data; S502: Based on the fine tree leaf image data, enhance the color and texture contrast of the image, analyze the color distribution and texture changes on the leaf surface, identify potential health problems, including spots, cracks or discolored areas, and obtain the tree leaf health feature analysis data; S503: Based on the tree leaf health feature analysis data, compare the leaf health features with the known disease features, evaluate the size of the spots, the depth of the cracks and the range of the discolored areas on the leaves through edge detection, establish the actual signs of each disease, and obtain the tree pest and disease diagnosis results.

[0013] As a further solution of the present invention, for the edge detection, according to the formula: ; Determine the edge information of the leaf image , where and are the pixel position coordinates in the image, is the standard deviation of the Gaussian filter, which controls the smoothness of the filter, and are the offset parameters, which are used to adjust the position of the filter center, is the filter intensity adjustment parameter.

[0014] As a further solution of the present invention, based on the urban tree coordinate data, the predicted tree age results, and the tree pest and disease diagnosis results, the geographical location map of the trees is updated in real time, and the health status and growth environment of the trees are comprehensively evaluated and marked. The specific steps for generating the real-time urban tree information mapping result are as follows: S601: Based on the urban tree coordinate data, start map update, import the latest coordinate data, adjust the map zoom and annotation parameters to ensure that the position of each tree is accurately displayed, synchronize the geographical location information to ensure that the data is consistent with the actual geographical features, and obtain the updated tree geographical location map; S602: Based on the updated tree geographical location map, combined with the predicted tree age results, classify each tree by age using the map marking tool, apply color coding to distinguish trees of different age groups, and automatically update the age annotation on the map to obtain the tree age annotation map; S603: Use the tree age annotation map and the tree pest and disease diagnosis results to evaluate the health status and growth environment, mark the trees affected by diseases, use icons and colors to highlight the affected areas, provide real-time visual analysis, and generate the real-time urban tree information mapping result.

[0015] An urban tree information rapid acquisition system includes: The geographic coordinate acquisition module uses a GNSS receiver to measure the geographic coordinates of each tree within the urban area, synchronously records the accuracy and timestamp of each coordinate point, cleans the data, and performs data synchronization through the urban database interface to obtain the urban tree coordinate data; The dielectric constant measurement module adjusts the direction and focus of the microwave remote sensing device based on the urban tree coordinate data, adjusts the device configuration to emit microwaves at different frequencies, records the time delay and signal intensity of each frequency, calculates the time delay and intensity change of the return signal at different frequencies, and estimates the tree dielectric constant to obtain the tree dielectric constant measurement value; The tree age scoring module defines the correlation scoring standard between the tree age and the dielectric constant based on the tree dielectric constant measurement value, conducts tree scoring aggregation, identifies the differences in scoring among different tree species through aggregation processing, and determines the age interval of the scoring data for each tree according to the preset age determination threshold to obtain the predicted tree age results; The tree morphology acquisition module configures an image sensor based on the urban tree coordinate data, automatically adjusts the focal length and exposure parameters, matches the position and lighting conditions of each tree, takes photos of each tree, captures the color and shape of the tree leaves, and records the tree morphology to obtain the tree morphology image data; The leaf health analysis module analyzes the leaf color changes and shape abnormalities based on the tree morphological image data, evaluates the basic health status of the trees, optimizes the exposure and contrast to highlight the leaf texture and color differences, and obtains fine leaf image data; The tree pest and disease diagnosis module analyzes the color distribution and texture changes on the leaf surface based on the fine leaf image data, identifies potential health problems, including spots, cracks or discolored areas, compares the leaf health characteristics with the known disease characteristics, evaluates the size of the spots, the depth of the cracks and the range of the discolored areas on the leaves, and obtains the tree pest and disease diagnosis results; The urban tree information mapping module starts map updates, synchronizes geographical location information based on the urban tree coordinate data, classifies each tree into different age groups in combination with the predicted tree age results, uses color coding to distinguish trees of different age groups, combines the tree pest and disease diagnosis results, evaluates the health status and growth environment, marks the trees affected by diseases, and generates real-time urban tree information mapping results.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by introducing a GNSS receiver and a microwave remote sensing device, the high-precision and multi-dimensional analysis of tree data is ensured. The GNSS receiver provides accurate coordinates and timestamps, enhancing the real-time and accuracy of geographical data. The microwave remote sensing technology accurately evaluates the tree age and health status by analyzing the tree dielectric constant. The image sensor that automatically adjusts the focal length and exposure parameters can capture the color and shape of the leaves in detail, improving the detail and accuracy of pest and disease diagnosis. The real-time updated tree information mapping promotes the efficiency of environmental monitoring and urban planning, providing strong support for the management of urban ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the schematic diagram of the step flow of the present invention; Figure 2 is the flow chart of step S1 of the present invention; Figure 3 is the flow chart of step S2 of the present invention; Figure 4 is the flow chart of step S3 of the present invention; Figure 5 is the flow chart of step S4 of the present invention; Figure 6 is the flow chart of step S5 of the present invention; Figure 7 is the flow chart of step S6 of the present invention; Figure 8 is the system module diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0020] Please refer to Figure 1 , a method for rapid acquisition of urban tree information, comprising the following steps: S1: Obtain the geographical coordinates of each tree in the city through a GNSS receiver, record the accuracy and timestamp information of each location, perform data synchronization, and generate urban tree coordinate data; S2: Combine the urban tree coordinate data to guide the microwave remote sensing device to aim at the target tree, emit microwave signals from different angles and frequencies, measure the time delay and intensity change of the returned signal, calculate the tree dielectric constant, and obtain the measured value of the tree dielectric constant; S3: Based on the measured value of the tree dielectric constant, define the tree age estimation rule, score each dielectric constant value, perform summary processing on the scored data, and judge the age range of the tree according to the set threshold to obtain the predicted tree age result; S4: Based on the urban tree coordinate data, combine with an image sensor, automatically adjust the focal length and exposure parameters, take pictures of the trees, analyze the color and shape changes of the tree leaves, and generate the basic health data of the trees; S5: Based on the basic health data of the trees, capture the detailed images of the leaves, analyze the color distribution and texture changes in the images, identify the different disease signs on the leaves through visual information, evaluate the health status of the trees, and generate the pest and disease diagnosis results; S6: Based on the urban tree coordinate data, the predicted tree age result and the tree pest and disease diagnosis result, update the geographical location map of the trees in real time, comprehensively evaluate and mark the health status and growth environment of the trees, and generate the real-time urban tree information mapping result.

[0021] Urban tree coordinate data include geographic location coordinates, accuracy level and time records; tree dielectric constant measurement values ​​include dielectric constant value, signal delay data and signal strength data; predicted tree age results include age score records, age range classification results and score summary data; basic tree health data include leaf color characteristic records and leaf shape characteristic records; pest and disease diagnosis results include color distribution characteristics, texture change characteristics and health abnormality indicators; real-time urban tree information mapping results include geographic location update records, health status identification and environmental assessment results.

[0022] See also Figure 2 , the specific steps of S1 are: S101: Use GNSS receivers to measure the geographic coordinates of each tree in the city, synchronously record the accuracy and timestamp of each coordinate point, correct signal drift, update positioning data, and generate accurate tree coordinate information; Use a GNSS receiver to measure the geographic coordinates of each tree in the city. First, initialize the device to ensure that the receiver's time and geocoding system are synchronized, and calibrate the receiver's geographic location to ensure accurate signal reception. Next, record data for each measurement point, including coordinates (longitude and latitude), accuracy indicators, and timestamps. The received raw data is affected by various environmental factors, such as building obstructions and climatic conditions, so a signal quality assessment is required to filter out valid data with signal strength that meets the standard. Use a sliding time window method for valid data, and calculate the average value of the signal every set time period (such as every 10 seconds) to reduce errors and improve the accuracy of location data. The corrected data is output in the set format to generate accurate tree coordinate information.

[0023] S102: Based on the accurate information of tree coordinates, clean the data, delete coordinate abnormal values, format the coordinate data into a unified format, perform data standardization, and obtain formatted tree data; Based on the precise information of tree coordinates, a preliminary check of the data is first performed to identify coordinate data that deviates significantly from the normal range. Outliers are caused by equipment errors, signal reflections, or operational errors. Statistical analysis methods, such as IQR (interquartile range) or standard deviation analysis, are used to determine the boundaries of normal data and delete data points that exceed these boundaries. Subsequently, the retained data is formatted in a unified manner, such as converting all coordinate data into decimal form and arranging and classifying them according to a specific template. To ensure data consistency and comparability, all data are standardized and quantified in a unified manner, such as adjusting the accuracy to five decimal places. Finally, the standardized formatted tree data is output and prepared for further data synchronization and analysis.

[0024] S103: Based on the formatted tree data, perform data synchronization through the urban database interface, upload the cleaned and formatted data, verify the data integrity, update the database records, and obtain the urban tree coordinate data; Based on the formatted tree data, establish a data interface with the urban database to ensure efficient data communication, automatically execute the data upload task, and batch upload the locally cleaned and formatted tree data to the urban database. Before uploading, perform an integrity check on each data item to ensure no missing fields or formatting errors. After the data is uploaded, check the integrity and accuracy of the data through the built-in verification program of the database, such as using SQL constraints or data verification scripts to ensure the validity of the data. After the data verification passes, the database will automatically update the existing tree records to ensure the latest status of the database information. Finally, obtain the updated urban tree coordinate data from the database for subsequent urban planning and greening management.

[0025] Please refer to Figure 3 , the specific steps of S2 are as follows: S201: Based on the urban tree coordinate data, adjust the direction and focus of the microwave remote sensing device, aim at the target tree, adjust the device angle to capture the optimal signal, ensure that the microwave covers the target area from multiple angles, and obtain the microwave directional data; Based on the urban tree coordinate data, first set the basic operation parameters of the microwave remote sensing device. The operator uses the known tree coordinate data to adjust the direction and focus of the remote sensing device through the control interface to ensure that the target tree is within the central field of view of the device. Use the electronic control system to finely adjust the direction of the remote sensing device, change its pitch angle and yaw angle to cover different heights and angles of the target tree, so as to capture the reflected signal to the maximum extent. During the adjustment process, ensure that the angle after each adjustment can cover the target area from multiple directions by real-time monitoring of the feedback information, increasing the dimension and accuracy of data collection. After the multi-angle coverage is completed, conduct a preliminary evaluation of the data quality of each angle, and select the data with the optimal signal as the basis for subsequent processing. The system can obtain multi-angle and high-precision microwave directional data, providing a solid foundation for further analysis.

[0026] S202: Based on the microwave directional data, adjust the device configuration to emit microwaves at different frequencies, monitor the return signals at multiple frequencies, record the time delay and signal intensity of each frequency, and obtain the microwave signal response data; Based on the microwave orientation data, the operator adjusts the frequency setting of the microwave emission through the control system of the remote sensing device. According to the pre-set frequency range, the system automatically adjusts the emission frequency step by step from low frequency to high frequency, and collects the return signals at each set frequency. The data collected includes the time delay and signal strength at each frequency point, and these data will be recorded in a specially designed data collection module. For each frequency, the system analyzes the quality of the return signal through an enhanced signal processing algorithm and records the specific parameter settings when the signal is the strongest. In addition, the system also monitors environmental factors such as temperature and humidity in real time, and adjusts the transmission parameters of the microwave signal to compensate for the impact of environmental changes on the signal strength and propagation speed. After completing the steps, a set of microwave signal response data containing detailed signal response characteristics at different frequencies will be obtained, preparing for the next step of dielectric constant calculation.

[0027] S203: Based on the microwave signal response data, calculate the time delay and intensity change of the return signals at different frequencies, deduce the dielectric constant of the tree through data calculation, repeat the measurement to verify the consistency of the data, and obtain the measured value of the tree dielectric constant; Based on the microwave signal response data, first use data analysis to calculate the relationship between the time delay and signal strength of the return signals at each frequency. Through these data, use the physical modeling method to deduce the dielectric constant value corresponding to each frequency. To ensure the accuracy and consistency of the calculation, for each frequency setting, repeat the measurement multiple times and compare the consistency of each measurement result. This process is executed by an automated control system to ensure that each measurement is carried out under the same environment and settings. Through this repeated measurement and comparison, abnormal data can be effectively screened out and the obtained data can be ensured to be highly consistent. Finally, by synthesizing the results at each frequency, use the weighted average method to calculate the final measured value of the tree dielectric constant, which can reflect the electrical properties of the tree and is of great significance for studying the physiological and ecological characteristics of the tree.

[0028] Please refer to Figure 4 , the specific steps of S3 are as follows: S301: Based on the measured value of the tree dielectric constant, define the correlation scoring criteria between the tree age and the dielectric constant, assign age-related scores to each measured value according to the numerical size, reveal the direct correlation between each score and the dielectric constant, and obtain the tree age scoring data; Based on the measured values of the tree dielectric constant, it is first necessary to establish a quantitative scoring system to measure the correlation between the dielectric constant and the tree age. Through historical data analysis, the statistical correlation between the dielectric constant and the tree age is determined. A linear regression model is adopted, with the dielectric constant as the independent variable and the tree age as the dependent variable, to establish a prediction model. According to the results of the regression analysis, a correlation scoring criterion is generated, which includes age-related scores corresponding to different dielectric constant values. On this basis, a specific age-related score is assigned to each measured dielectric constant value, and in this way, the direct correlation between each score and the dielectric constant is revealed. After performing these steps, all the score data are collected and sorted to form tree age scoring data, which will be used for further analysis and evaluation.

[0029] S302: Based on the tree age scoring data, conduct a summary of the tree scores. Identify the differences in scores among different tree species through aggregation processing, adjust the abnormal score data, optimize the consistency of the scores through data purification, and obtain the summary score result; Based on the tree age scoring data, conduct a detailed data summary and analysis. First, identify the differences in scores among different tree species through data aggregation processing. Use the clustering analysis method to divide the trees into several groups according to the dielectric constant and age scores, and analyze the score differences within and between each group. Through these analyses, discover and adjust the abnormal score data that deviate significantly from the group average value to optimize the consistency of the scores. Then, summarize the adjusted data again, and obtain the average score of each group and the overall score of the entire data set through arithmetic mean or weighted mean methods. Finally, output the summary score result after data purification and optimization, providing a basis for the next age determination.

[0030] S303: Based on the summary score result, according to the preset age determination threshold, determine the age range of each tree's score data, classify the age range of each tree by comparing the relationship between the score and the threshold, and obtain the predicted tree age result; Based on the summary score result, conduct the classification and prediction of the tree age. Analyze the score data of each tree according to the previously set age determination threshold. Adopt the threshold segmentation method to compare the score data of each tree with the preset threshold. If the score of a tree is higher than a certain threshold, it is classified into an older age range; if the score is lower than the threshold, it is classified into a younger range. In this way, a specific age range is assigned to each tree. In addition, to improve the accuracy of the classification, multiple thresholds can be adopted to divide the trees into more detailed age levels, collect and sort the classification results of the age ranges of all trees, and output the final predicted tree age result.

[0031] Please refer to Figure 5 , the specific steps of S4 are as follows: S401: Based on the urban tree coordinate data, configure the image sensor, automatically adjust the focal length and exposure parameters, match the position and lighting conditions of each tree, ensure the clarity and exposure balance of the image, and obtain the optimized image capture setting results; Based on the urban tree coordinate data, first configure the preliminary settings of the image sensor. The operator inputs the urban tree coordinate data into the image capture system, and the system automatically locates the specific position of each tree according to the coordinates. Then, according to the lighting conditions at each position, automatically adjust the focal length and exposure parameters of the image sensor. The system uses ambient light sensing technology to detect the current ambient light intensity in real time and adjusts the camera's ISO, shutter speed, and aperture size according to the detection results to adapt to different lighting conditions and ensure the clarity and exposure balance of the image. In addition, by optimizing the color balance and contrast of the image, the image quality is further improved. After the adjustment is completed, the system will save the optimal image capture settings for each tree, providing accurate configuration information for subsequent image acquisition work.

[0032] S402: Based on the optimized image capture setting results, take photos of each tree, capture the color and shape of the tree leaves, and record the tree morphology to obtain the tree morphology image data; Based on the optimized image capture setting results, the operator starts taking pictures of each tree. The image sensor automatically captures high-definition photos of the trees according to the preset optimized settings. During the shooting process, special attention is paid to capturing the color and shape characteristics of the tree leaves to ensure that the details are accurately recorded. Photos of each tree are taken from different angles, including front, side, and top views, to record the tree morphology in all directions. After the shooting is completed, all image data are classified and stored according to the tree identification and shooting time. Each group of images includes a complete morphological record of the tree, which is used to evaluate the growth status and environmental adaptability of the tree.

[0033] S403: Based on the tree morphology image data, analyze the leaf color changes and shape abnormalities, evaluate the health status of the trees, and obtain the basic tree health data; Based on the tree morphology image data, analyze the health status of the trees. Analyze the color and shape of the collected leaf images, automatically identify the RGB values of the leaf colors, and compare them with the standard color range of healthy leaves to identify color changes, which may indicate nutrient deficiencies or disease attacks. At the same time, analyze the shape of the leaves, such as whether there are cracks, curls, or abnormal growth patterns. These shape abnormalities are usually related to environmental stress or biological attacks. Combining the analysis results of color and shape, the system evaluates the health status of each tree and generates a detailed health assessment report. Finally, these data are summarized to form the basic health data of the trees, providing a scientific basis for urban greening management.

[0034] Please refer to Figure 6, the specific steps of S5 are as follows: S501: Based on the basic tree health data, analyze the leaf details, optimize the exposure and contrast to highlight the leaf texture and color differences, and obtain the fine leaf image data; Based on the basic tree health data, optimize the acquired leaf images. According to the key information recorded in the health data, such as the lighting conditions and leaf reflection characteristics, adjust the exposure level and contrast of the image to make the texture and color differences of the leaves more prominent, enhancing the visual contrast between chlorophyll and non-chlorophyll components, especially in the vein and leaf margin parts. In addition, apply local contrast enhancement algorithms, such as local histogram equalization, to ensure that the details of each area can be accurately presented. After careful adjustment, every detail in the image, including tiny color differences and texture changes, can be clearly captured and recorded, and the generated fine leaf image data is used for further health analysis.

[0035] S502: Based on the fine leaf image data, enhance the image color and texture contrast, analyze the color distribution and texture changes on the leaf surface, identify potential health problems, including spots, cracks or discolored areas, and obtain the leaf health characteristic analysis data; Based on the fine leaf image data, conduct a more in-depth image analysis to identify and evaluate the health status of the leaves. By enhancing the color and texture contrast on the leaf surface, the differences between healthy and damaged areas become more obvious. Using image segmentation techniques, such as threshold-based segmentation, separate the normal green areas in the image from the abnormal color areas (such as yellowing, withering or red spots). At the same time, detect the tiny cracks and spots on the leaf surface, which can not only identify potential health problems, such as leaf spot disease or water deficiency symptoms, but also quantitatively evaluate the overall health status of the leaves. All this information is integrated into the leaf health characteristic analysis data, providing a scientific basis for the final disease diagnosis.

[0036] S503: Based on the leaf health characteristic analysis data, compare the leaf health characteristics with the known disease characteristics, evaluate the size of the spots, the depth of the cracks and the range of the discolored areas on the leaf through edge detection, establish the actual signs of each disease, and obtain the tree pest and disease diagnosis results; Edge detection, according to the formula: ; Determine the edge information of the leaf image , where, and are the pixel position coordinates in the image, is the standard deviation of the Gaussian filter, controlling the smoothness of the filter, and are the offset parameters, used to adjust the position of the filter center so that a specific area can be enhanced for processing, is a filtering intensity adjustment parameter that allows the standard deviation to be dynamically changed to adapt to the detailed features of specific regions of the image.

[0037] The execution process is as follows: First, by adding an offset parameter and , it allows the filter to intensify in a specific region of the image, focusing on special disease signs on the leaves, such as around spots or cracks. Secondly, the filtering intensity adjustment parameter is introduced to dynamically adjust the value according to the size and shape of the disease area, improving the adaptability and flexibility of the filter. Finally, Canny edge detection is performed on the image processed by the above parameters to determine the boundaries of the spot size, crack depth, and discolored area on the leaves, thereby more accurately evaluating and diagnosing tree diseases.

[0038] Please refer to Figure 7 , and the specific steps of S6 are as follows: S601: Based on the urban tree coordinate data, start map update, import the latest coordinate data, adjust the map zoom and annotation parameters to ensure the accurate display of the location of each tree, synchronize the geographical location information to ensure the consistency of the data with the actual geographical features, and obtain the updated tree geographical location map; Based on the urban tree coordinate data, first start the map update program and import the latest tree coordinate data into the map update system. The system automatically verifies and synchronizes the data to ensure that each data point matches the actual geographical features. Next, adjust the map zoom level and annotation parameters, such as icon size and label font, to ensure that the location of each tree can be accurately and clearly displayed on the map. The map update program uses advanced rendering techniques to dynamically adjust the map view, optimizing the visual effect and user interaction experience. In addition, the geographical location information is synchronized in real time to ensure that the displayed data is consistent with the actual geographical features in the Geographic Information System (GIS), generating and saving the updated tree geographical location map, providing a basis for further data analysis and utilization.

[0039] S602: Based on the updated tree geographical location map, combined with the predicted tree age results, classify each tree by age using the map marking tool, apply color coding to distinguish trees of different age groups, and automatically update the age annotation on the map to obtain the tree age annotation map; Based on the updated geographical location map of the trees, combined with the predicted tree age results, map marking is carried out to classify each tree into different age groups. The system automatically applies different color codings according to the tree age data to distinguish trees of different ages. For example, dark green is used to represent old trees, and light green is used to represent young trees. The color coding not only increases the visual recognition of the map but also enables users to quickly understand the age distribution of the trees. With the input of new data, the age markings on the map will be automatically updated to keep the information up-to-date, and the generated tree age marking map provides an intuitive and practical analysis tool for urban greening management and planning.

[0040] S603: Utilize the tree age marking map and the results of tree pest and disease diagnosis to evaluate the health status and growth environment, mark the trees affected by diseases, highlight the affected areas using icons and colors, provide real-time visual analysis, and generate real-time urban tree information mapping results; Utilize the tree age marking map and the results of tree pest and disease diagnosis to conduct a comprehensive evaluation of the health status and growth environment. Combine the pest and disease diagnosis results with the tree age data to mark the trees affected by diseases. Use different icons and colors to highlight the affected areas. For example, use red warning marks for the most severely affected trees and yellow marks for slightly affected trees. The visual marking method not only provides real-time analysis of the tree health status but also promotes quick and effective disease management decisions. All evaluation and marking processes are carried out on a dynamically updated map to ensure that all information is real-time and accurate, generating the final real-time urban tree information mapping results, which provide an important decision support tool for urban management departments.

[0041] Please refer to Figure 8 , a rapid urban tree information acquisition system, including: The geographical coordinate acquisition module uses a GNSS receiver to measure the geographical coordinates of each tree within the urban area, synchronously records the accuracy and timestamp of each coordinate point, cleans the data, and performs data synchronization through the urban database interface to obtain urban tree coordinate data; The dielectric constant measurement module, based on the urban tree coordinate data, adjusts the direction and focus of the microwave remote sensing device, adjusts the device configuration to emit microwaves at different frequencies, records the time delay and signal intensity of each frequency, calculates the time delay and intensity change of the return signal of the different frequencies, and estimates the dielectric constant of the trees to obtain the measured values of the dielectric constant of the trees; The tree age scoring module, based on the measured values of the dielectric constant of the trees, defines the correlation scoring criteria between the tree age and the dielectric constant, conducts a summary of the tree scores, identifies the differences in scores between different tree species through aggregation processing, and determines the age range of each tree's score data according to the preset age determination threshold to obtain the predicted tree age results; Based on the urban tree coordinate data, the tree morphology acquisition module configures an image sensor, automatically adjusts the focal length and exposure parameters, matches the position and lighting conditions of each tree, takes photos of each tree, captures the color and shape of the tree leaves, and records the tree morphology to obtain tree morphology image data; Based on the tree morphology image data, the leaf health analysis module analyzes the leaf color changes and shape abnormalities, evaluates the basic health status of the trees, optimizes the exposure and contrast to highlight the leaf texture and color differences, and obtains fine leaf image data; Based on the fine leaf image data, the tree pest and disease diagnosis module analyzes the color distribution and texture changes on the leaf surface, identifies potential health problems, including spots, cracks or discolored areas, compares the leaf health characteristics with the known disease characteristics, evaluates the size of the spots, the depth of the cracks and the range of the discolored areas on the leaves, and obtains the tree pest and disease diagnosis results; Based on the urban tree coordinate data, the urban tree information mapping module initiates map updates, synchronizes geographical location information, classifies each tree by age group in combination with the predicted tree age results, uses color coding to distinguish trees of different age groups, combines the tree pest and disease diagnosis results, evaluates the health status and growth environment, marks the trees affected by diseases, and generates real-time urban tree information mapping results.

[0042] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for quickly collecting urban tree information, characterized in that: The following steps are involved: Obtain the geographic coordinates of each tree in the city through a GNSS receiver, record the accuracy and timestamp information of each location, perform data synchronization, and generate urban tree coordinate data; In combination with the urban tree coordinate data, guide the microwave remote sensing equipment to aim at the target tree, transmit microwave signals from different angles and frequencies, measure the time delay and intensity change of the return signal, calculate the tree dielectric constant, and obtain the tree dielectric constant measurement value; Based on the tree dielectric constant measurement value, a tree age estimation rule is defined, each dielectric constant value is scored, the scoring data is summarized, and the age range of the tree is determined according to a set threshold to obtain a predicted tree age result; Based on the urban tree coordinate data, combined with the image sensor, the focus and exposure parameters are automatically adjusted, photos of the trees are taken, the color and shape changes of the tree leaves are analyzed, and the basic health data of the trees is generated; Based on the basic tree health data, capture detailed images of leaves, analyze color distribution and texture changes in the images, identify differential disease signs on leaves through visual information, evaluate the health status of trees, and generate disease and insect pest diagnosis results; Based on the urban tree coordinate data, predicted tree age results and tree disease and insect pest diagnosis results, the geographical location map of the trees is updated in real time, the health status and growth environment of the trees are comprehensively evaluated and marked, and real-time urban tree information mapping results are generated.

2. The method for quickly collecting urban tree information according to claim 1, characterized in that: The urban tree coordinate data includes geographic location coordinates, accuracy level and time records, the tree dielectric constant measurement values ​​include dielectric constant values, signal delay data and signal strength data, the predicted tree age results include age score records, age range classification results and score summary data, the tree basic health data includes leaf color feature records and leaf shape feature records, the pest and disease diagnosis results include color distribution features, texture change features and health abnormality indicators, and the real-time urban tree information mapping results include geographic location update records, health status identification and environmental assessment results.

3. The method for quickly collecting urban tree information according to claim 1, characterized in that: The specific steps to obtain the geographic coordinates of each tree in the city through the GNSS receiver, record the accuracy and timestamp information of each location, perform data synchronization, and generate the coordinate data of urban trees are as follows: Use GNSS receivers to measure the geographic coordinates of each tree in the city, synchronously record the accuracy and timestamp of each coordinate point, correct signal drift, update positioning data, and generate accurate tree coordinate information; Based on the precise information of the tree coordinates, the data is cleaned, abnormal coordinate values ​​are deleted, the coordinate data is formatted into a unified format, data standardization is performed, and formatted tree data is obtained; Based on the formatted tree data, data synchronization is performed through the city database interface, the cleaned and formatted data is uploaded, the data integrity is verified, the database records are updated, and the city tree coordinate data is obtained.

4. The method for quickly collecting urban tree information according to claim 1, characterized in that: Combined with the urban tree coordinate data, guide the microwave remote sensing equipment to the target tree, transmit microwave signals from different angles and frequencies, measure the time delay and intensity change of the return signal, calculate the tree dielectric constant, and obtain the specific steps of the tree dielectric constant measurement value as follows: Based on the urban tree coordinate data, adjust the direction and focus of the microwave remote sensing equipment, aim at the target tree, adjust the angle of the equipment to capture the optimal signal, ensure that the microwave covers the target area from multiple angles, and obtain microwave directional data; Based on the microwave directional data, adjusting the equipment configuration to transmit microwaves from different frequencies, monitoring the return signals at multiple frequencies, recording the time delay and signal strength of each frequency, and obtaining microwave signal response data; Based on the microwave signal response data, the time delay and intensity change of the difference frequency return signal are calculated, the dielectric constant of the tree is inferred through the data, and repeated measurements are made to verify the consistency of the data to obtain the measurement value of the dielectric constant of the tree.

5. The method for quickly collecting urban tree information according to claim 1, characterized in that: Based on the tree dielectric constant measurement value, the tree age estimation rule is defined, each dielectric constant value is scored, the scoring data is summarized, and the age range of the tree is determined according to the set threshold. The specific steps for obtaining the predicted tree age result are: Based on the tree dielectric constant measurement values, a correlation scoring standard between tree age and dielectric constant is defined, an age-related score is assigned to each measurement value according to the numerical value, a direct correlation between each score and dielectric constant is revealed, and tree age scoring data is obtained; Based on the tree age scoring data, tree scoring is summarized, differences in scores between different tree species are identified through aggregation processing, abnormal scoring data are adjusted, consistency of scores is optimized through data purification, and a summary scoring result is obtained; Based on the summarized scoring results, the age range of the scoring data of each tree is determined according to a preset age determination threshold, and the age range of each tree is classified by comparing the relationship between the score and the threshold to obtain the predicted tree age result.

6. The method for quickly collecting urban tree information according to claim 1, characterized in that: Based on the urban tree coordinate data, combined with the image sensor, the focus and exposure parameters are automatically adjusted, the photos of the trees are taken, the color and shape changes of the tree leaves are analyzed, and the specific steps of generating the basic health data of the trees are as follows: Based on the urban tree coordinate data, an image sensor is configured to automatically adjust focus and exposure parameters to match the position and lighting conditions of each tree, ensure image clarity and balanced exposure, and obtain an optimized image capture setting result; Based on the optimized image capture setting result, taking a photo of each tree, capturing the color and shape of the tree leaves, and recording the tree morphology to obtain tree morphology image data; Based on the tree morphological image data, the color changes and shape anomalies of leaves are analyzed, the health status of the trees is evaluated, and the basic health data of the trees is obtained.

7. The method for quickly collecting urban tree information according to claim 1, characterized in that: Based on the basic tree health data, the detailed images of leaves are captured, the color distribution and texture changes in the images are analyzed, the differential disease signs on the leaves are identified through visual information, the health status of the trees is evaluated, and the specific steps of generating the disease and insect diagnosis results are as follows: Based on the basic health data of the tree, analyze leaf details, optimize exposure and contrast to highlight leaf texture and color differences, and obtain fine leaf image data; Based on the fine leaf image data, enhance the image color and texture contrast, analyze the color distribution and texture changes on the leaf surface, identify potential health problems, including spots, cracks or discolored areas, and obtain leaf health feature analysis data; Based on the leaf health characteristic analysis data, the leaf health characteristics are compared with known disease characteristics, and the spot size, crack depth and range of discoloration areas on the leaves are evaluated through edge detection to establish the actual signs of each disease and obtain tree disease and insect pest diagnosis results.

8. The method for quickly collecting urban tree information according to claim 7, characterized in that: The edge detection is based on the formula: ； Determine the edge information of the leaf image ,in, and is the pixel position coordinate in the image, is the standard deviation of the Gaussian filter, which controls the smoothness of the filter. and is the offset parameter used to adjust the position of the filter center. Adjust the parameter for filter strength.

9. The method for quickly collecting urban tree information according to claim 1, characterized in that: Based on the urban tree coordinate data, tree age prediction results and tree pest diagnosis results, the geographical location map of the trees is updated in real time, the health status and growth environment of the trees are comprehensively evaluated and marked, and the specific steps of generating real-time urban tree information mapping results are as follows: Based on the urban tree coordinate data, start map update, import the latest coordinate data, adjust map zoom and annotation parameters to ensure that the location of each tree is accurately displayed, synchronize geographic location information to ensure that the data is consistent with the actual geographic features, and obtain an updated tree geographic location map; Based on the updated tree geographic location map and in combination with the predicted tree age result, each tree is classified into age groups through a map marking tool, color coding is applied to distinguish trees of different age groups, and the age marking on the map is automatically updated to obtain a tree age marking map; The tree age annotation map and tree disease and insect pest diagnosis results are used to evaluate the health status and growth environment, mark trees affected by diseases, use icons and colors to highlight the affected areas, provide real-time visual analysis, and generate real-time urban tree information mapping results.

10. A rapid urban tree information collection system, characterized in that: According to a method for quickly collecting urban tree information according to any one of claims 1 to 9, the system comprises: The geographic coordinate acquisition module uses a GNSS receiver to measure the geographic coordinates of each tree within the city, synchronously records the accuracy and timestamp of each coordinate point, cleans the data, performs data synchronization through the city database interface, and obtains the coordinate data of urban trees; The dielectric constant measurement module adjusts the direction and focus of the microwave remote sensing device based on the urban tree coordinate data, adjusts the device configuration to transmit microwaves from the difference frequency, records the time delay and signal strength of each frequency, calculates the time delay and strength change of the difference frequency return signal, calculates the tree dielectric constant, and obtains the tree dielectric constant measurement value; The tree age scoring module defines the correlation scoring standard between the tree age and the dielectric constant based on the tree dielectric constant measurement value, summarizes the tree scores, identifies the differences in scores between different tree species through aggregation processing, and determines the age range of the score data of each tree according to the preset age determination threshold to obtain the predicted tree age result; The tree morphology acquisition module configures an image sensor based on the urban tree coordinate data, automatically adjusts the focus and exposure parameters, matches the position and lighting conditions of each tree, takes photos of each tree, captures the color and shape of the tree leaves, and records the tree morphology to obtain tree morphology image data; The leaf health analysis module analyzes the leaf color changes and shape anomalies based on the tree morphology image data, evaluates the basic health status of the tree, optimizes the exposure and contrast to highlight the leaf texture and color differences, and obtains fine leaf image data; The tree disease and insect pest diagnosis module analyzes the color distribution and texture changes on the leaf surface based on the fine leaf image data, identifies potential health problems, including spots, cracks or discolored areas, compares the leaf health characteristics with known disease characteristics, evaluates the size of spots, crack depth and the range of discolored areas on the leaves, and obtains tree disease and insect pest diagnosis results; The urban tree information mapping module starts map updates based on the urban tree coordinate data, synchronizes geographic location information, classifies each tree into age groups based on the predicted tree age results, applies color coding to distinguish trees of different age groups, evaluates health conditions and growth environments based on tree disease and insect pest diagnosis results, marks trees affected by diseases, and generates real-time urban tree information mapping results.

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