Ancient and famous tree health data acquisition method and system

Abstract

The invention discloses an ancient and famous tree health data acquisition method and system. The method comprises the following steps: acquiring image data; performing feature extraction on the image data to obtain first, second and third image color feature values and first, second and third pixel color feature values; calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensities of different areas; performing calculation according to the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value to obtain a leaf area index; calculating according to the first pixel color characteristic value, the sunlight intensity of different regions, the leaf area index and a pre-stored meteorological condition influence parameter to obtain an ancient and famous tree health index; and analyzing according to the health index of the ancient and famous trees to obtain the health condition of the ancient and famous trees. According to the method, the influence of environment and weather on ancient tree health assessment is considered, and comprehensive data can be obtained.

Description

Technical Field

[0001] The present invention relates to the field of data collection, and in particular to a method and system for collecting health data of ancient and famous trees. Background Art

[0002] Ancient trees refer to woody plants over 50 years old (trees over 50 years old); famous trees refer to trees that have been identified by government agencies or forestry authorities at all levels because of their beautiful shape, great age, peculiar name, historical legends, important value as timber, roots, bark or branches and leaves, or important scientific research value. Due to the long-term influence of historical, cultural and natural factors, ancient trees and famous trees are regarded as precious, rare, magical and divine objects by people. They have great influence and appeal among the masses and play a huge role in inheriting and increasing regional culture and spiritual civilization. However, with the acceleration of industrial modernization and urbanization, a huge number of ancient trees and famous trees have been destroyed. For example, illegal logging, natural disasters and abnormal deaths are common.

[0003] In order to effectively improve the protection of ancient and famous trees, it is necessary to regularly assess the health of ancient and famous trees. The health assessment of ancient and famous trees is inseparable from the collection of various data on the trees' own conditions and environmental factors.

[0004] In one existing technology, the health of ancient trees and famous trees is predicted by collecting environmental impact data and the crown area and leaf area of ​​ancient trees and famous trees. However, this technology overemphasizes the impact of environmental factors on the health of ancient trees and famous trees. It only collects data on leaves and evaluates the survival status of ancient trees and famous trees based on the information collected from the leaves. In summary, in the prior art, only leaf data is collected to conduct health assessment of ancient trees and famous trees, while the impact of other factors on the ancient trees and famous trees is ignored, resulting in incomplete health data obtained. Summary of the invention

[0005] The present invention provides a method and system for collecting data on ancient and famous trees, which can comprehensively consider the impact of growth environment and meteorological conditions on health assessment of ancient and famous trees, thereby obtaining comprehensive health data and improving assessment accuracy.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a method for collecting data on ancient and famous trees, comprising: Acquire image data; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels; Extracting features from the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value, and a third pixel color feature value; Calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas; Calculate according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value to obtain a leaf area index; Calculating according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters to obtain the ancient and famous tree health index; The health status of the ancient trees and famous trees is obtained by analyzing the health index of the ancient trees and famous trees.

[0007] In an optional implementation of the first aspect, the calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas includes: Calculate according to the first image color feature value and the first pixel color feature value to obtain The first pixel color feature mean and the image pixel grayscale value; Calculating according to the image pixel grayscale value to obtain an average image pixel grayscale value; Calculating according to the average image pixel grayscale value to obtain pixel color variance; Calculating according to the pixel color variance and the average image pixel grayscale value to obtain a first reflection index; The illumination intensity of different areas is obtained by calculation based on the number of image pixels, the first pixel color feature value, the image pixel gray value, the average image pixel gray value, the first pixel color feature mean value and the first reflection index.

[0008] In an optional implementation of the first aspect, the calculation based on the number of image pixels, the first pixel color feature value, the image pixel gray value, the first pixel color feature mean value and the first reflectance index to obtain the illumination intensity of different areas includes: The first reflection index and the light intensity in different areas are obtained by the following formula: in, represents the first reflection index, represents the average image pixel gray value, represents the pixel color variance, Indicates the intensity of sunlight in different areas. Indicates the number of image pixels, Indicates the first region index value, represents the first pixel color feature value, represents the mean value of the first pixel color feature, Represents the grayscale value of image pixels In an optional implementation of the first aspect, the calculating according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value, and the third pixel color characteristic value to obtain the leaf area index includes: Calculate according to the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value to obtain the bark image pixel grayscale value and the leaf image pixel grayscale value; Calculate according to the grayscale value of the bark image pixel, the grayscale value of the leaf image pixel, the number of the bark image pixel and the number of the leaf image pixel to obtain a second reflection index and a third reflection index; The leaf area index is obtained by calculating according to the second reflectance index, the third reflectance index, the number of image pixels, the second image color characteristic value and the third image color characteristic value.

[0009] In an optional implementation of the first aspect, the calculating according to the second reflectance index, the third reflectance index, the number of image pixels, the second image color feature value, and the third image color feature value to obtain the leaf area index includes: The second reflectance index, third reflectance index and leaf area index are obtained by the following formula: in, represents the color feature value of the second image, Indicates the color feature value of the third image represents the second reflection index, represents the third reflection index, Represents the number of pixels in the bark image, Indicates the number of pixels in the leaf image, Indicates the first region index value, Indicates the second region index value, Represents the grayscale value of the bark image pixel, Represents the grayscale value of the leaf image pixel, Indicates the number of image pixels, Represents leaf area index.

[0010] In an optional implementation of the first aspect, the ancient and famous tree health index is obtained by calculating according to the first pixel color feature value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters, including: The health index of ancient and famous trees is obtained by the following formula: in, Indicates the health index of ancient and famous trees, Indicates the number of ancient and famous trees collected in advance, Indicates the intensity of sunlight in different areas. represents the first pixel color feature value, Represents the grayscale value of image pixels, Indicates the pre-stored meteorological conditions affecting the parameters, represents the leaf area index, The total number of image regions, Indicates the first region index value, Indicates the third region index value, Indicates the number of image pixels.

[0011] In an optional implementation of the first aspect, the pre-stored meteorological condition influencing parameters are obtained by adding the meteorological condition parameters to the growth environment parameters of the ancient and famous trees.

[0012] In a second aspect, the present invention provides a health data collection system for ancient and famous trees, comprising: An image acquisition module, used to acquire image data; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels; An image extraction module is used to extract features from the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value, and a third pixel color feature value; a parameter analysis module, configured to calculate, based on the image data, the first image color characteristic value and the first pixel color characteristic value, to obtain sunlight intensity in different areas; a data calculation module, configured to calculate according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value to obtain a leaf area index; A data output module, used to calculate the health index of ancient and famous trees according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters; The evaluation module is used to evaluate the ancient and famous trees according to their health index to obtain the health status of the ancient and famous trees.

[0013] In an optional implementation of the second aspect, the calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas includes: Calculate according to the first image color feature value and the first pixel color feature value to obtain The first pixel color feature mean and the image pixel grayscale value; Calculating according to the image pixel grayscale value to obtain an average image pixel grayscale value; Calculating according to the average image pixel grayscale value to obtain pixel color variance; Calculating according to the pixel color variance and the average image pixel grayscale value to obtain a first reflection index; The illumination intensity of different areas is obtained by calculation based on the number of image pixels, the first pixel color feature value, the image pixel gray value, the average image pixel gray value, the first pixel color feature mean value and the first reflection index.

[0014] In an optional implementation of the second aspect, the calculation based on the number of image pixels, the first pixel color feature value, the image pixel gray value, the first pixel color feature mean value and the first reflectance index to obtain the illumination intensity of different areas includes: The first reflection index and the light intensity in different areas are obtained by the following formula: in, represents the first reflection index, Average image pixel gray value, represents the pixel color variance, Indicates the intensity of sunlight in different areas. Indicates the number of image pixels, Indicates the first region index value, represents the first pixel color feature value, represents the mean value of the first pixel color feature, Represents the grayscale value of image pixels In an optional implementation of the second aspect, the calculating according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value, and the third pixel color characteristic value to obtain the leaf area index includes: Calculate according to the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value to obtain the bark image pixel grayscale value and the leaf image pixel grayscale value; Calculate according to the grayscale value of the bark image pixel, the grayscale value of the leaf image pixel, the number of the bark image pixel and the number of the leaf image pixel to obtain a second reflection index and a third reflection index; The leaf area index is obtained by calculating according to the second reflectance index, the third reflectance index, the number of image pixels, the second image color characteristic value and the third image color characteristic value.

[0015] In an optional implementation of the second aspect, the calculating according to the second reflectance index, the third reflectance index, the number of image pixels, the second image color feature value, and the third image color feature value to obtain the leaf area index includes: The second reflectance index, third reflectance index and leaf area index are obtained by the following formula: in, represents the color feature value of the second image, represents the color feature value of the second image, represents the second reflection index, represents the third reflection index, Represents the number of pixels in the bark image, Indicates the number of pixels in the leaf image, Indicates the first region index value, Indicates the second region index value, Represents the grayscale value of the bark image pixel, Represents the grayscale value of the leaf image pixel, Indicates the number of image pixels, Represents leaf area index.

[0016] In an optional implementation of the second aspect, the ancient and famous tree health index is obtained by calculating according to the first pixel color feature value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters, including: The health index of ancient and famous trees is obtained by the following formula: in, Indicates the health index of ancient and famous trees, Indicates the number of ancient and famous trees collected in advance, Indicates the intensity of sunlight in different areas. represents the first pixel color feature value, Represents the grayscale value of image pixels, Indicates the pre-stored meteorological conditions affecting the parameters, represents the leaf area index, The total number of image regions, Indicates the first region index value, Indicates the third region index value, Indicates the number of image pixels.

[0017] In an optional implementation of the second aspect, the pre-stored meteorological condition influencing parameters are obtained by adding the meteorological condition parameters to the growth environment parameters of the ancient and famous trees.

[0018] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for collecting health data of ancient and famous trees.

[0019] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for collecting health data of ancient and famous trees.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for collecting health data of ancient and famous trees, comprising acquiring image data; wherein the image data comprises the number of image pixels, the number of bark image pixels and the number of leaf image pixels; performing feature extraction on the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value and a third pixel color feature value; performing calculation based on the image data, the first image color feature value and the first pixel color feature value to obtain sunlight intensity in different areas; performing calculation based on the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value to obtain a leaf area index; performing calculation based on the first pixel color feature value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters to obtain a health index of ancient and famous trees; performing analysis based on the health index of ancient and famous trees to obtain the health status of ancient and famous trees.

[0021] The present invention is executed by a computer, firstly obtaining image data including the number of image pixels, the number of bark and leaf image pixels, and obtaining a plurality of image color characteristic values ​​and pixel color characteristic values ​​through feature extraction, and then respectively calculating the sunlight intensity and leaf area index of different regions based on these data, and calculating the health index of ancient trees and famous trees by combining the meteorological condition influencing parameters obtained by adding the meteorological condition parameters and the growth environment parameters of ancient trees and famous trees stored in advance, and then analyzing the health status of ancient trees and famous trees. The corresponding acquisition system includes modules such as image acquisition, extraction, parameter analysis, data calculation, data output, and evaluation, as well as corresponding electronic equipment and computer-readable storage media. Through the present invention, the influence of the growth environment and meteorological conditions on the health assessment of ancient trees and famous trees can be comprehensively considered, so that comprehensive data can be obtained and the accuracy of the assessment can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the health data collection process of ancient and famous trees provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of the ancient and famous tree health data collection system provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Reference Figure 1 The first embodiment of the present invention provides a method and system for collecting health data of ancient and famous trees, comprising the following steps: S1, acquiring image data; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels; S2, extracting features from the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value, and a third pixel color feature value; S3, calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas; S4, calculating according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value to obtain a leaf area index; S5, calculating according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters to obtain the ancient and famous tree health index; S6, analyzing the ancient and famous trees according to their health index to obtain the health status of the ancient and famous trees.

[0025] In step S1, image data is acquired; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels.

[0026] For example, image data is captured by an image sensor inside a digital camera. When the shutter is pressed, the lens focuses light onto the image sensor. The image sensor is composed of many tiny photosensitive elements (pixels), which generate electrical signals based on the intensity and color of the received light. Then, the analog-to-digital converter in the camera converts these electrical signals into digital signals, which are processed by the processor inside the camera, such as adjusting color balance, sharpening, etc., and the processed digital image data is stored in the camera's memory card.

[0027] It should be noted that before the above-mentioned S1 step, the image data is obtained; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels, and specifically also includes: using laser radar, high-definition camera, thermal imager and other acquisition equipment to obtain health assessment data of ancient and famous trees, environmental data required for image acquisition, meteorological data and marking data for dividing bark and leaf areas, and storing the data in a database; obtaining picture data based on the database.

[0028] In step S2, feature extraction is performed on the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value, and a third pixel color feature value.

[0029] In one implementable manner, the specific process of obtaining the first pixel color characteristic value, the second pixel color characteristic value, and the third pixel color characteristic value includes: obtaining an image pixel area; converting and reading the image pixel area using an image processing library (such as OpenCV) to obtain the first pixel color characteristic value, the second pixel color characteristic value, and the third pixel color characteristic value.

[0030] In one possible implementation, the first image color feature value, the second image color feature value, and the third image color feature value are calculated using the following formula: in, represents the color feature value of the first image, represents the color feature value of the second image, represents the color feature value of the third image, Indicates the number of image pixels, Represents the number of pixels in the bark image, Indicates the number of pixels in the leaf image, represents the first pixel color feature value, represents the second pixel color feature value, represents the third pixel color feature value, Indicates the second region index value.

[0031] In step S3, calculation is performed based on the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas.

[0032] In the above step S3, calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain the sunlight intensity in different areas specifically includes the following steps: S31, performing calculation according to the first image color feature value and the first pixel color feature value to obtain a first pixel color feature mean value and an image pixel grayscale value.

[0033] In one achievable manner, the method for obtaining the first pixel color feature mean specifically includes: obtaining each first image color feature value in the picture, accumulating each first image color feature value in the picture and then dividing by the number to obtain the first pixel color feature mean.

[0034] In one implementable manner, the method for obtaining the grayscale value of an image pixel specifically includes: obtaining a first red channel pixel value, a first green channel pixel value, and a first blue channel pixel value of a pixel point; substituting the first red channel pixel value, the first green channel pixel value, and the first blue channel pixel value into the formula: Calculate the gray value of the image pixel. Represents the grayscale value of image pixels, represents the first red channel pixel value, represents the first green channel pixel value, It should be noted that the second red channel pixel value, the second green channel pixel value and the second blue channel pixel value are directly obtained by photographing or scanning with a digital camera, a video camera, a scanner or the like.

[0035] It is worth noting that the formula used in the embodiment of the present invention is to convert a color image into a grayscale image by applying it to each pixel in the image to obtain the grayscale value of each pixel. It is suitable for situations where grayscale information needs to be obtained from a color image for subsequent analysis. For example, when only the image brightness information needs to be concerned without considering the color information, the pixel grayscale value can be calculated to analyze the texture, structure and other characteristics of the bark and leaves.

[0036] S32, performing calculation according to the image pixel grayscale value to obtain an average image pixel grayscale value.

[0037] It should be noted that the average image pixel grayscale value is calculated by the following formula: in, represents the average image pixel gray value, Indicates the number of pixels in the image. Indicates the first region index value, Represents the grayscale value of image pixels.

[0038] S33, performing calculation according to the average image pixel grayscale value to obtain pixel color variance.

[0039] It should be noted that the pixel color variance is calculated by the following formula: in, represents the pixel color variance, Indicates The image area The grayscale value of the image pixel Indicates The average grayscale value of image pixels in image regions, Indicates The number of pixels in an image area.

[0040] It is worth noting that the formula used in the embodiment of the present invention is based on statistics, and variance is a statistic used to measure the degree of dispersion of a set of data. In image processing, the calculation of color variance can describe the uniformity or degree of variation of color distribution in an image.

[0041] S34, performing calculation according to the pixel color variance and the average image pixel grayscale value to obtain a first reflection index.

[0042] It should be noted that the first reflection index is calculated by the following formula: in, represents the first reflection index, represents the average image pixel gray value, Represents pixel color variance.

[0043] It is worth noting that the formula used in the embodiment of the present invention is based on the reflection characteristics of the object surface to light. In the context of image processing, the reflection of light on the object surface can be reflected by the grayscale value of the pixel. The higher the average image pixel grayscale value, the stronger the reflection of light in the area.

[0044] S35, calculating according to the number of image pixels, the first pixel color feature value, the image pixel gray value, the average image pixel gray value, the first pixel color feature mean value and the first reflection index to obtain the light intensity of different areas.

[0045] It should be noted that the light intensity in different areas is calculated by the following formula: in, represents the first reflection index, represents the average image pixel gray value, represents the pixel color variance, Indicates the intensity of sunlight in different areas. Indicates the number of image pixels, Indicates the first region index value, represents the first pixel color feature value, represents the mean value of the first pixel color feature, Represents the grayscale value of image pixels It is worth noting that the formula used in the embodiment of the present invention is based on the indirect association between environmental parameters and meteorological conditions. For example, light intensity will be affected by meteorological conditions (such as cloud cover affecting light intensity, and then affecting other parameters) and regional characteristics (such as color characteristic values ​​and grayscale values, etc., which change due to changes in light under the influence of meteorological conditions). Under different meteorological conditions (such as sunny days and cloudy days), parameters such as color characteristic values ​​and grayscale values ​​will change, resulting in different light intensities, and light intensity will be used in subsequent calculations of leaf area index of bark and leaves, etc., and then meteorological conditions indirectly affect the entire data calculation related to the health assessment of ancient trees and famous trees by affecting light intensity, and ultimately affect the calculation of meteorological conditions affecting parameters and the assessment of the health status of ancient trees and famous trees. For example, on cloudy days, the light intensity decreases, which will cause changes in the calculation results of the reflectance index of bark and leaves, affecting the calculation of leaf area index, and combined with the parameters affected by meteorological conditions, it will ultimately affect the judgment of the health status of ancient trees and famous trees.

[0046] In step S4, a leaf area index is obtained by calculation based on the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value.

[0047] In the above step S4, the step of calculating according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value to obtain the leaf area index specifically comprises the following steps: S41, calculating according to the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value to obtain the bark image pixel grayscale value and the leaf image pixel grayscale value.

[0048] In one implementable manner, the method for obtaining the grayscale value of the bark image pixel specifically includes: obtaining the second red channel pixel value, the second green channel pixel value, and the second blue channel pixel value of the pixel point; substituting the second red channel pixel value, the second green channel pixel value, and the second blue channel pixel value into the formula: The grayscale value of the bark image pixel is calculated. Represents the grayscale value of the bark image pixel, Represents the second red channel pixel value, Represents the second green channel pixel value, Represents the second blue channel pixel value.

[0049] In one implementable manner, the method for obtaining the grayscale value of the leaf image pixel specifically includes: obtaining the third red channel pixel value, the third green channel pixel value and the third blue channel pixel value of the pixel point; substituting the third red channel pixel value, the third green channel pixel value and the third blue channel pixel value into the formula: The grayscale value of the bark image pixel is calculated. Represents the grayscale value of the leaf image pixel, Represents the third red channel pixel value, Represents the third green channel pixel value, Represents the third blue channel pixel value.

[0050] It is worth noting that the second red channel pixel value, the second green channel pixel value, the second blue channel pixel value, the third red channel pixel value, the third green channel pixel value and the third blue channel pixel value are directly obtained by shooting or scanning using a digital camera, a video camera, a scanner or other device.

[0051] S42, calculating according to the grayscale value of the bark image pixel, the grayscale value of the leaf image pixel, the number of the bark image pixel and the number of the leaf image pixel to obtain a second reflection index and a third reflection index.

[0052] It should be noted that the second reflection index and the third reflection index are calculated by the following formula: in, represents the second reflection index, represents the third reflection index, Represents the number of pixels in the bark image, Indicates the number of pixels in the leaf image, Indicates the second region index value, Represents the grayscale value of the bark image pixel, Represents the grayscale value of the leaf image pixel.

[0053] It is worth noting that the formula used in the embodiment of the present invention is to sum and average the grayscale values ​​of pixels in the area to obtain a reflectance index that can reflect the average reflectivity of the bark and leaves in the area to light. The grayscale value is directly related to the reflectivity of light. The higher the average grayscale value, the stronger the reflectivity.

[0054] S43, calculating according to the second reflectance index, the third reflectance index, the number of image pixels, the second image color characteristic value, and the third image color characteristic value to obtain a leaf area index.

[0055] It should be noted that the leaf area index is calculated by the following formula: in, Indicates the number of pixels in the image. represents the color feature value of the second image, represents the second reflection index, represents the third reflection index, represents the color feature value of the third image, Indicates the first region index value, Represents leaf area index.

[0056] It is worth noting that the formula used in the embodiment of the present invention comprehensively considers the color characteristic values ​​and reflectance index of the bark and leaves, and can reflect regional characteristics. The leaf area index is one of the important indicators for evaluating the growth status and health of plants. A larger leaf area index indicates that the plant has more photosynthesis area, which is conducive to the growth and development of the plant. In the health assessment of ancient trees and famous trees, by calculating the leaf area index, the growth vitality and health status of ancient trees and famous trees can be understood.

[0057] S5, calculating according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters to obtain the health index of ancient and famous trees.

[0058] It should be noted that the health index of ancient and famous trees is calculated using the following formula: in, Indicates the health index of ancient and famous trees, Indicates the number of ancient and famous trees collected in advance, Indicates the intensity of sunlight in different areas. represents the first pixel color feature value, Represents the grayscale value of image pixels, Indicates the pre-stored meteorological conditions affecting the parameters, represents the leaf area index, The total number of image regions, Indicates the third region index value, Indicates the number of image pixels.

[0059] Exemplarily, the pre-stored meteorological condition influencing parameters refer to those obtained by adding meteorological condition parameters to growth environment parameters of ancient and famous trees.

[0060] It is worth noting that the meteorological condition parameters are parameters obtained by evaluating the specific conditions of the area using meteorological monitoring equipment (such as meteorological stations, sensors, etc.) based on the soil conditions for the growth of ancient trees, surrounding vegetation conditions and other environmental factors directly related to the growth of ancient trees; the growth environment parameters of ancient trees are parameters obtained by evaluating the meteorological factors such as light, temperature, wind speed, humidity, etc. using environmental monitoring equipment (such as soil nutrient rapid testers, light intensity meters, meteorological stations, etc.).

[0061] It is worth noting that the formula used in the embodiment of the present invention is based on a variety of information of ancient and famous trees, including color feature values, grayscale values, light intensity in image data, structural feature data of bark and leaves such as the number of pixels and reflectance index, and meteorological conditions. These factors are interrelated and reflect the health status of ancient and famous trees from different angles.

[0062] S6, analyzing the ancient and famous trees according to their health index to obtain the health status of the ancient and famous trees.

[0063] Illustratively, the health index of ancient trees and famous trees represents the final health assessment value of ancient trees and famous trees, and its size directly reflects the health level of ancient trees and famous trees. The higher the value, the better the health status, otherwise there may be health problems.

[0064] In a specific embodiment, a threshold range is set. When the health index of ancient trees and famous trees is within the normal range threshold, only regular monitoring and maintenance are required; when the health index of ancient trees and famous trees is within the intervention threshold range, further inspection of the ancient trees and famous trees is required; when the health index of ancient trees and famous trees is higher than the highest range threshold, it is determined that the ancient trees and famous trees are in a healthy state; when the health index of ancient trees and famous trees is lower than the lowest range threshold, it can be determined that the ancient trees and famous trees have health problems.

[0065] Illustratively, the highest range threshold is a health index greater than or equal to 90 points (out of 100); the normal range threshold is a health index between 70 and 89 points, which requires only regular monitoring and maintenance; the intervention threshold range is a health index between 30 and 69 points, which requires monitoring and preliminary intervention measures; the lowest range threshold is a health index below 30 points, indicating that there are significant health problems with ancient trees and famous trees and immediate protective measures are needed.

[0066] The following describes the working process of the present invention using a common scenario as an example. Figure 2 , which is Figure 1 Schematic diagram of the working scenario of the method.

[0067] The specific implementation process of the present invention is as follows: the image data including the number of image pixels, the number of bark image pixels and the number of leaf image pixels are obtained by the image acquisition module, and the image data is subjected to feature extraction according to the image extraction module to obtain the first image color feature value, the second image color feature value, the third image color feature value, the first pixel color feature value, the second pixel color feature value and the third pixel color feature value. The parameter analysis module calculates the first pixel color feature mean, the image pixel gray value, the average image pixel gray value, the pixel color variance, the first reflection index and the light intensity in different regions according to the image data, the first image color feature value and the first pixel color feature value. The data calculation module calculates the bark image pixel gray value, the leaf image pixel gray value, the second reflection index, the third reflection index and the leaf area index according to the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value. The data output module then calculates the health index of ancient and famous trees according to the first pixel color feature value, the sunlight intensity in different regions, the leaf area index and the pre-stored meteorological condition influencing parameters. Finally, the evaluation module evaluates the ancient and famous trees according to their health indexes, thereby obtaining the health status of the ancient and famous trees.

[0068] In summary, the present invention discloses a method for collecting health data of ancient and famous trees, comprising acquiring image data; wherein the image data comprises the number of image pixels, the number of bark image pixels and the number of leaf image pixels; performing feature extraction on the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value and a third pixel color feature value; performing calculation based on the image data, the first image color feature value and the first pixel color feature value to obtain sunlight intensity in different areas; performing calculation based on the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value to obtain a leaf area index; performing calculation based on the first pixel color feature value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters to obtain a health index of ancient and famous trees; performing analysis based on the health index of ancient and famous trees to obtain the health status of ancient and famous trees.

[0069] The present invention first obtains image data including the number of image pixels, the number of bark and leaf image pixels, obtains a variety of image color feature values ​​and pixel color feature values ​​through feature extraction, and then calculates the sunlight intensity and leaf area index of different regions respectively based on these data, and calculates the health index of ancient trees and famous trees by combining the meteorological condition influencing parameters obtained by adding the meteorological condition parameters and the growth environment parameters of ancient trees and famous trees stored in advance, and then analyzes the health status of ancient trees and famous trees. The corresponding acquisition system includes modules such as image acquisition, extraction, parameter analysis, data calculation, data output, and evaluation, as well as corresponding electronic equipment and computer-readable storage media. Through the present invention, the influence of the growth environment and meteorological conditions on the health assessment of ancient trees and famous trees can be comprehensively considered, so that comprehensive data can be obtained.

[0070] Reference Figure 2 The second embodiment of the present invention provides a health data collection system for ancient and famous trees, including: The image acquisition module 101 is used to acquire image data; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels; An image extraction module 102 is used to extract features from the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value, and a third pixel color feature value; A parameter analysis module 103, configured to calculate according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas; A data calculation module 104, configured to calculate according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value to obtain a leaf area index; The data output module 105 is used to calculate the health index of ancient and famous trees according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and the pre-stored meteorological condition influencing parameters; The evaluation module 106 is used to evaluate the ancient and famous trees according to the health index of the ancient and famous trees to obtain the health status of the ancient and famous trees.

[0071] It should be noted that the ancient and famous tree health data collection system provided in an embodiment of the present invention is used to execute all the process steps of the ancient and famous tree health data collection method in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0072] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the method for collecting health data of ancient and famous trees are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the image acquisition module.

[0073] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0074] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0075] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0076] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0077] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0078] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0079] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for collecting health data of ancient and famous trees, characterized in that: Executed by a computer, including: Acquire image data; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels; Extracting features from the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value, and a third pixel color feature value; Calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas; Calculate according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value to obtain a leaf area index; Calculating according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters to obtain the ancient and famous tree health index; The health status of the ancient trees and famous trees is obtained by analyzing the health index of the ancient trees and famous trees.

2. The method for collecting health data of ancient and famous trees according to claim 1, characterized in that: The calculating according to the image data, the first image color characteristic value and the first pixel color characteristic value to obtain sunlight intensity in different areas includes: Calculate according to the first image color feature value and the first pixel color feature value to obtain The first pixel color feature mean and the image pixel grayscale value; Calculating according to the image pixel grayscale value to obtain an average image pixel grayscale value; Calculating according to the average image pixel grayscale value to obtain pixel color variance; Calculating according to the pixel color variance and the average image pixel grayscale value to obtain a first reflection index; The illumination intensity of different areas is obtained by calculation based on the number of image pixels, the first pixel color feature value, the image pixel gray value, the average image pixel gray value, the first pixel color feature mean value and the first reflection index.

3. The method for collecting health data of ancient and famous trees according to claim 2 is characterized in that: The calculating according to the number of image pixels, the first pixel color feature value, the image pixel gray value, the first pixel color feature mean value and the first reflection index to obtain the illumination intensity of different areas includes: The first reflection index and the light intensity in different areas are obtained by the following formula: in, represents the first reflection index, represents the average image pixel gray value, represents the pixel color variance, Indicates the intensity of sunlight in different areas. Indicates the number of image pixels, Indicates the first region index value, represents the first pixel color feature value, represents the mean value of the first pixel color feature, Represents the grayscale value of image pixels.

4. The method for collecting health data of ancient and famous trees according to claim 1, characterized in that: The step of calculating according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value, and the third pixel color characteristic value to obtain the leaf area index comprises: Calculate according to the second image color feature value, the third image color feature value, the second pixel color feature value and the third pixel color feature value to obtain the bark image pixel grayscale value and the leaf image pixel grayscale value; Calculate according to the grayscale value of the bark image pixel, the grayscale value of the leaf image pixel, the number of the bark image pixel and the number of the leaf image pixel to obtain a second reflection index and a third reflection index; The leaf area index is obtained by calculating according to the second reflectance index, the third reflectance index, the number of image pixels, the second image color characteristic value and the third image color characteristic value.

5. The method for collecting health data of ancient and famous trees according to claim 4 is characterized in that: The step of calculating according to the second reflectance index, the third reflectance index, the number of image pixels, the second image color feature value, and the third image color feature value to obtain the leaf area index comprises: The second reflectance index, third reflectance index and leaf area index are obtained by the following formula: in, Indicates the number of image pixels, represents the color feature value of the second image, The third image color feature value, represents the second reflection index, represents the third reflection index, Represents the number of pixels in the bark image, Indicates the number of pixels in the leaf image, Indicates the first region index value, Indicates the second region index value, Represents the grayscale value of the bark image pixel, Represents the grayscale value of the leaf image pixel, Represents leaf area index.

6. The method for collecting health data of ancient and famous trees according to claim 1, characterized in that: The ancient and famous tree health index is obtained by calculating according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and the pre-stored meteorological condition influencing parameters, including: The health index of ancient and famous trees is obtained by the following formula: in, Indicates the health index of ancient and famous trees, Indicates the number of ancient and famous trees collected in advance, Indicates the intensity of sunlight in different areas. represents the first pixel color feature value, Represents the grayscale value of image pixels, Indicates the pre-stored meteorological conditions affecting the parameters, represents the leaf area index, The total number of image regions, Indicates the first region index value, Indicates the third region index value, Indicates the number of image pixels.

7. The method for collecting health data of ancient and famous trees according to claim 1, characterized in that: The pre-stored meteorological condition impact parameters are obtained by adding the meteorological condition parameters to the growth environment parameters of the ancient and famous trees.

8. A health data collection system for ancient and famous trees, characterized in that: include: An image acquisition module, used to acquire image data; wherein the image data includes the number of image pixels, the number of bark image pixels and the number of leaf image pixels; An image extraction module is used to extract features from the image data to obtain a first image color feature value, a second image color feature value, a third image color feature value, a first pixel color feature value, a second pixel color feature value, and a third pixel color feature value; a parameter analysis module, configured to calculate, based on the image data, the first image color characteristic value and the first pixel color characteristic value, to obtain sunlight intensity in different areas; a data calculation module, configured to calculate according to the second image color characteristic value, the third image color characteristic value, the second pixel color characteristic value and the third pixel color characteristic value to obtain a leaf area index; A data output module, used to calculate the health index of ancient and famous trees according to the first pixel color characteristic value, the sunlight intensity in different areas, the leaf area index and pre-stored meteorological condition influencing parameters; The evaluation module is used to evaluate the ancient and famous trees according to their health index to obtain the health status of the ancient and famous trees.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for collecting health data of ancient and famous trees as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for collecting health data of ancient and famous trees as described in any one of claims 1 to 7.

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