Gastrodia elata disease monitoring method and system based on image recognition

Abstract

The invention provides a gastrodia elata disease monitoring method and system based on image recognition, and relates to the technical field of gastrodia elata disease monitoring, four-way image data of gastrodia elata is shot, the image data is processed, the processed image data is compared with color features of mold colonies, and gastrodia elata colony prediction indexes used for reflecting colony abnormal degree indexes of gastrodia elata are generated; meanwhile, model data of the gastrodia elata are collected, a 3D model is generated, the growth appearance of the gastrodia elata is analyzed, a gastrodia elata distortion prediction index used for reflecting the distortion degree of gastrodia elata stems is generated, and gastrodia elata disease indexes used for reflecting the corresponding gastrodia elata disease bearing degree are generated by combining the gastrodia elata distortion prediction index and gastrodia elata colony prediction index analysis; therefore, the gastrodia elata disease grade can be judged, and a large amount of manpower is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of Gastrodia elata disease monitoring, and specifically to a Gastrodia elata disease monitoring method and system based on image recognition. Background Technique

[0002] Gastrodia elata is an important traditional Chinese medicine with extensive medicinal value, mainly used to treat symptoms such as headache, dizziness, and insomnia. However, Gastrodia elata is vulnerable to various diseases during its growth process, seriously affecting its yield and quality. Common Gastrodia elata diseases include white rot, black rot, and rust. White rot is caused by fungi, mainly manifested as the roots and stems of Gastrodia elata rotting, resulting in the withering and even death of the plants. Black rot is also caused by fungi, mainly attacking the stems and leaves of Gastrodia elata, causing black lesions and affecting photosynthesis. Rust is caused by rust fungi, mainly invading the plants through stomata, forming rust-colored spots, leading to leaf shedding, and in severe cases, the death of the whole plant. Gastrodia elata is also vulnerable to insect pests, resulting in deformed growth of its stems.

[0003] Generally, when planting Gastrodia elata, it is necessary to manually check whether Gastrodia elata is invaded by mold, and manually check whether the shape of Gastrodia elata is distorted and whether it is invaded by insect pests. Manual visual inspection not only has a slow detection speed but also easily misses diseased plants.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a Gastrodia elata disease monitoring method and system based on image recognition to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A Gastrodia elata disease monitoring method based on image recognition, the specific steps include: S1. Collect four-direction image data of the Gastrodia elata stems of each Gastrodia elata plant in the planting area, remove non-target Gastrodia elata pixel points through OpenCV, perform blackening processing, and then perform correlation analysis to generate hue, saturation, and lightness data corresponding to each Gastrodia elata plant; S2. Set the hue range threshold, saturation range threshold, and lightness range threshold, and compare them with the hue, saturation, and lightness data corresponding to each Gastrodia elata stem respectively to generate a mask, perform correlation analysis on the mask to generate the number of mold pixel points, and perform correlation analysis on the number of mold pixel points to generate a Gastrodia elata colony prediction index corresponding to each Gastrodia elata plant; S3. Establish a 3D model of Gastrodia elata within the planting area, place the 3D model in a three-dimensional coordinate system, perform equidistant horizontal segmentation on the 3D model of Gastrodia elata, collect the central coordinates and cross-sectional area values of each Gastrodia elata at the segmentation plane where the 3D model is located, process the central coordinates and cross-sectional area values to generate the mean central offset and variance of central offset, and generate the mean area and variance of area offset for each Gastrodia elata; S4. Conduct a correlation analysis on the mean central offset, variance of central offset, mean area, and variance of area offset of each Gastrodia elata to generate a distortion prediction index for each Gastrodia elata, conduct a correlation analysis on the distortion prediction index of Gastrodia elata and the colony prediction index of Gastrodia elata to generate a disease index of Gastrodia elata and conduct a pest and disease analysis to output the disease grade of Gastrodia elata.

[0007] Furthermore, select Gastrodia elata with an exposed stem length greater than 10 cm for numbering. The number of Gastrodia elata is G plants. Use i as the index of Gastrodia elata, and the value range of i is a positive integer from 1 to G.

[0008] Furthermore, the pixel points of the image data are 1920*1080, and the RGB channel values of the black pixel points are (0, 0, 0). The image data is , where j is used to index the image perspective. When j = 1, it is used to represent the front view image; when j = 2, j = 3, it is used to represent the side view image; when j = 4, it is used to represent the rear view image; R, G, and B are used to represent the red value, green value, and blue value respectively, and the value range is 0 - 255; use M as the index of the pixel row of the image data and N as the index of the pixel column of the image data; conduct a correlation analysis on the image data to generate hue , saturation , and lightness , and the formula is: ; where is the two-parameter arctangent function, is the maximum value among R, G, and B of the corresponding pixel point, is the minimum value among R, G, and B of the corresponding pixel point, hue is used to reflect the color type of the pixel point, saturation is used to reflect the purity of the pixel point, lightness is used to reflect the brightness of the color of the pixel point. When , .

[0009] Furthermore, the hue range threshold is , the saturation range threshold is , the lightness range threshold is , create a mask , based on the formula: ; Mask It is used to reflect whether the value of the pixel point is within the hue range threshold, saturation range threshold, and lightness range threshold. If so, it is recorded as 1; otherwise, it is recorded as 0 for counting. Perform correlation analysis on the mask to generate the number of mold pixel points , based on the formula: ; Number of mold pixel points It is used to reflect the predicted number of Gastrodia elata colony pixel points in the photographed image of the i-th Gastrodia elata. Perform correlation analysis on the number of mold pixel points to generate the Gastrodia elata colony prediction index , based on the formula: ; Gastrodia elata colony prediction index It is used to reflect the colony abnormality degree index of the i-th Gastrodia elata.

[0010] Furthermore, in S4, a three-dimensional coordinate system includes the X, Y, and Z axes, where the Z axis is perpendicular to the planting ground, and the planting ground is used as the XOY plane. Multiple equally spaced planes parallel to the XOY plane are used as dividing planes, and the distance between the dividing planes is 1 cm. Use the dividing plane to perform segmentation processing on the 3D model of Gastrodia elata to obtain the central coordinates and the cross-sectional area value of the 3D model of each Gastrodia elata at the dividing plane where it is located. Number the dividing planes sequentially from bottom to top, and use the superscript h to index the dividing plane numbers, with the value range being positive integers between, perform correlation analysis on the central coordinates to generate the mean central offset and the variance of central offset , perform correlation analysis on the cross-sectional area value to generate the mean area and the variance of area offset , based on the formula: ; Among them, the mean central offset is used to reflect the degree of skewness of the stem of the i-th Gastrodia elata, and the variance of central offset is used to reflect the local bending degree index of the stem of the i-th Gastrodia elata. The mean area is used to reflect the average thickness uniformity of the stem of the i-th Gastrodia elata, and the variance of area offset is used to reflect the degree of deformity of the local thickness change of the stem of the i-th Gastrodia elata.

[0011] Furthermore, for the mean central offset , central offset variance , area mean and area offset variance to perform correlation analysis and generate Gastrodia elata distortion prediction index , and the formula is: ; Gastrodia elata distortion prediction index is used to reflect the distortion degree of Gastrodia elata stems, and is the maximum value of the central offset mean in G Gastrodia elata plants.

[0012] Furthermore, perform correlation analysis on the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index to generate Gastrodia elata disease index , and the formula is: ; Gastrodia elata disease index is used to reflect the degree of disease of the corresponding Gastrodia elata.

[0013] Furthermore, compare the Gastrodia elata disease index with the disease threshold and output the Gastrodia elata disease level. When , output the corresponding Gastrodia elata disease level as level two, with a deeper degree of disease infection, and targeted observation and determination of the Gastrodia elata status are required; when , output the corresponding Gastrodia elata disease level as level one, and the Gastrodia elata grows normally, and no targeted observation is required.

[0014] The present invention also provides a Gastrodia elata disease monitoring system based on image recognition for executing the Gastrodia elata disease monitoring method based on image recognition, including: A numbering module for numbering G Gastrodia elata plants in the planting area; An image acquisition module for taking four-way photos of each Gastrodia elata plant, collecting image data of Gastrodia elata stems, removing non-target Gastrodia elata pixel points and performing blackening processing through OpenCV, performing correlation analysis on the image data, and generating the hue, saturation, and brightness of the image data pixel points; A Gastrodia elata colony prediction module for setting the hue range threshold, saturation range threshold, and brightness range threshold. The hue range threshold, saturation range threshold, and brightness range threshold include the hue range threshold, saturation range threshold, and brightness range threshold. Compare the hue with the hue range threshold, the saturation with the saturation range threshold, and the brightness with the brightness range threshold to generate a mask, perform correlation analysis on the mask to generate the number of mold pixel points, and perform correlation analysis on the number of mold pixel points to generate the Gastrodia elata colony prediction index; A model generation module for establishing a numbered Gastrodia elata 3D model within a planting area, placing the 3D model in a three-dimensional coordinate system, performing equidistant horizontal segmentation on the Gastrodia elata 3D model, collecting the central coordinates and cross-sectional area values of each segmented surface where the Gastrodia elata 3D model is located, processing the central coordinates and cross-sectional area values to generate a mean central offset and a variance of central offset, and generating a mean area and a variance of area offset; A model analysis module for performing a correlation analysis on the mean central offset, the variance of central offset, the mean area, and the variance of area offset to generate a Gastrodia elata distortion prediction index; A comprehensive analysis module for performing a correlation analysis on the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index to generate a Gastrodia elata disease index, comparing the Gastrodia elata disease index with a disease threshold, and outputting a Gastrodia elata disease grade.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention captures four-way image data of Gastrodia elata, processes the image data, compares it with the color characteristics of mold colonies, generates a Gastrodia elata colony prediction index for reflecting the degree of colony abnormality of Gastrodia elata. At the same time, it also collects model data of Gastrodia elata to generate a 3D model, analyzes the growth shape of Gastrodia elata, generates a Gastrodia elata distortion prediction index for reflecting the degree of distortion of the Gastrodia elata stem, and combines the analysis of the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index to generate a Gastrodia elata disease index for reflecting the degree of disease of the corresponding Gastrodia elata, thereby helping to judge the Gastrodia elata disease grade and saving a large amount of manpower. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is a schematic diagram of the overall system flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second" and similar words used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0019] Embodiment: Please refer to Figure 1 , the present invention provides a technical solution: A Gastrodia elata disease monitoring method based on image recognition, the specific steps include: Step 1: Collect four-directional image data of the Gastrodia elata stems of each Gastrodia elata plant in the planting area, remove non-target Gastrodia elata pixel points through OpenCV and perform blackening processing, and then perform correlation analysis to generate hue, saturation and lightness data corresponding to each Gastrodia elata plant; among them, the four-directional image data includes front-view images, rear-view images and side-view image data on both sides.

[0020] In this embodiment, the Gastrodia elata plants monitored by image recognition are plants approaching maturity. At this stage, the exposed stems of Gastrodia elata are on the soil surface, and the morphological characteristics of the exposed stems can be monitored to judge the disease level of Gastrodia elata. The Gastrodia elata plants have exposed stems with a length greater than 10 cm. The qualified Gastrodia elata plants are numbered. The number of qualified Gastrodia elata plants is G plants. In this embodiment, i is used as the index for Gastrodia elata, and the value range of i is a positive integer from 1 to G.

[0021] In order to shoot and input image data for all angles of Gastrodia elata, each Gastrodia elata plant is photographed in four directions to collect image data of the Gastrodia elata stems. Non-target Gastrodia elata pixel points are removed through the OpenCV software and blackening processing is performed. The RGB channel values of the black pixel points are (0, 0, 0), which is convenient for subsequent calculations. Correlation analysis is performed on the image data to generate the hue, saturation and lightness of the pixel points of the image data; The pixel points of the pixel image data taken by the drone are 1920*1080, and the image data is , where j is used to index the image perspective. When j = 1, it represents the front-facing image; when j = 2 or j = 3, it represents the side-facing image; when j = 4, it represents the rear-facing image. R, G, and B are used to represent the red value, green value, and blue value respectively, with a value range of 0 - 255. M is used as the index of the pixel row of the image data, and N is used as the index of the pixel column of the image data. The maximum value of M is 1920, and the maximum value of N is 1080. In order to analyze the bacterial community characteristics in the image data, the image data is subjected to correlation analysis to generate hue , saturation , and lightness . The formulas used are: ; are the red, green, and blue channel values of the pixel at the M*N position in the image data of the j-th image perspective of the i-th Gastrodia elata respectively. Among them, is the two-parameter arctangent function, is the maximum value among the R, G, and B of the corresponding pixel, is the minimum value among the R, G, and B of the corresponding pixel. Hue is used to reflect the color type of the pixel, saturation is used to reflect the purity of the pixel, and lightness is used to reflect the brightness of the color of the pixel. When , . Through comprehensive analysis of the color type, purity, and brightness of the pixel, the bacterial community image in the image data is extracted. In the above formula, when the red component of the corresponding pixel is the largest, Z takes the value of 0; when the green component of the corresponding pixel is the largest, Z takes the value of 120; when the blue component of the corresponding pixel is the largest, Z takes the value of 240. When , hue .

[0022] Step 2: Set the hue range threshold, saturation range threshold, and lightness range threshold, and compare them with the hue, saturation, and lightness data corresponding to each Gastrodia elata stem respectively to generate a mask. Then perform correlation analysis on the mask to generate the number of mold pixel points, and perform correlation analysis on the number of mold pixel points to generate the Gastrodia elata colony prediction index corresponding to each plant; The hue range threshold, saturation range threshold, and lightness range threshold are measured through experiments. First, select samples with different degrees of Gastrodia elata infection (such as no mold, mild, moderate, and severe infections). Observe the samples under natural light or using uniform white light illumination to ensure the true representation of colors. Observe the color of the mold, especially its surface hue, saturation, and lightness. Record the color characteristics of the mold in each sample. Use a color wheel to identify the color of the mold and record it as an angular range, which is set as the hue range threshold range. Estimate the vividness of the mold color and classify it from light to dark (in the range of 1 to 255, 50 is the lower saturation, and 255 is the high saturation). This value range is the saturation range threshold. Observe the brightness of the mold and record it as a value between 50 and 255. This value is the lightness range threshold range. .

[0023] Create a mask. The formula is as follows: ; The mask is used to reflect whether the value of a pixel point is within the hue range threshold, saturation range threshold, and lightness range threshold. If so, record it as 1, otherwise record it as 0 for counting. Perform a correlation analysis on the mask to generate the number of mold pixel points. The formula is as follows: ; The number of mold pixel points is used to reflect the predicted number of Gastrodia elata colony pixel points in the photographed image of the i-th Gastrodia elata. Perform a correlation analysis on the number of mold pixel points to generate the Gastrodia elata colony prediction index. The formula is as follows: ; The number of mold pixel points The more, the greater the value of the Gastrodia elata colony prediction index. The Gastrodia elata colony prediction index is used to reflect the degree of mold infection of the i-th Gastrodia elata. The larger the value, the higher the degree of mold infection of the Gastrodia elata stem.

[0024] Step 3: Establish a 3D model of Gastrodia elata in the planting area, place the 3D model in a three-dimensional coordinate system, perform an equidistant horizontal segmentation process on the Gastrodia elata 3D model, collect the central coordinates and cross-sectional area values of each Gastrodia elata on the segmentation plane where the 3D model is located, process the central coordinates and cross-sectional area values to generate the mean central offset and variance of the central offset, and generate the mean area and variance of the area offset of each Gastrodia elata. Scan the data point cloud of Gastrodia elata in the planting area. Select Pix4Dmapper as the data processing software and generate a high-density point cloud using image stitching and lidar data. Set the "point cloud density" provided by the software to "high" to ensure that the number of point clouds per square meter reaches more than 1000. Generate a 3D mesh from the point cloud and select the "Delaunay triangulation" algorithm to ensure the smoothness and accuracy of the mesh.

[0025] Use a three-dimensional coordinate system including the X, Y, and Z axes, where the Z axis is perpendicular to the planting ground, and the planting ground is used as the XOY plane. Use multiple equally spaced planes parallel to the XOY plane as the dividing planes, with a spacing of 1 cm between the dividing planes. Use the dividing planes to perform segmentation processing on the 3D model of Gastrodia elata to obtain the central coordinates of each 3D model of Gastrodia elata on the dividing plane and the cross-sectional area value , number the dividing planes sequentially from bottom to top, and use the superscript h to index the dividing plane numbers, with the value range being positive integers between. Since the heights of different Gastrodia elata are different, the number of cutting planes is also different. The value used to reflect the number of cutting planes. Perform a correlation analysis on the central coordinates to generate the mean central offset and the variance of central offset . Perform a correlation analysis on the cross-sectional area values to generate the mean area and the variance of area offset . The formulas are as follows: ; where and are the abscissa and ordinate of the center point of the intersection surface of the lowest cutting plane and the 3D model of Gastrodia elata. The mean central offset reflects the degree of skewness of the stem of the i-th Gastrodia elata by calculating the offset degree of the center point. The variance of central offset is used to reflect the local bending degree index of the stem of the i-th Gastrodia elata. The larger the value, the higher the bending degree. The mean area is used to reflect the average thickness uniformity degree of the stem of the i-th Gastrodia elata. The larger the value, the thicker it is. The variance of area offset is used to reflect the degree of deformity of the local thickness change of the stem of the i-th Gastrodia elata. The larger the value, the higher the bending degree and the degree of deformity; where, set to take 9 coordinates on the edge of the cutting plane of Gastrodia elata. Since it is to calculate the cross-sectional area of Gastrodia elata on the cutting plane, the Z-axis data can be ignored, and only the X-axis and Y-axis coordinates are used, which are respectively , , , , …… , calculate the cross-sectional area Area through the following formula: ; Calculate the center point of the cut surface of Gastrodia elata through the following formula : ; , , , , ……, are the randomly selected 9-point coordinates evenly on the edge of the cut surface of Gastrodia elata. Arrange them separately, and they can be arranged as , , , …, and , , , …, , and use , , , to index the above 9-point coordinates. Among them, the value of k is a positive integer from 1 to 8, and the subscripts k and k + 1 are both used to number and index the 9-point coordinates. For example, when k = 1, , , , respectively represent , , , , when k = 2, , , , respectively represent , , , . The center point of the cut surface of Gastrodia elata is the approximate center point of the outline of the cut part of Gastrodia elata after each cut surface is cut. Analyze multiple center points to evaluate the bending degree of Gastrodia elata.

[0026] Step 4. Conduct a correlation analysis on the center offset mean, center offset variance, area mean, and area offset variance of each Gastrodia elata plant to generate a distortion prediction index for each Gastrodia elata plant. Conduct a correlation analysis on the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index to generate a Gastrodia elata disease index and conduct a pest and disease analysis to output the Gastrodia elata disease level.

[0027] For the center offset mean , the center offset variance , the area mean and the area offset variance Perform a correlation analysis to generate a Gastrodia elata distortion prediction index , and the formula is as follows: ; The Gastrodia elata distortion prediction index is used to reflect the distortion degree of the Gastrodia elata stem. is the maximum value of the mean central offset within G strains of Gastrodia elata. For Gastrodia elata, its stem is generally straight upward. When infected by pests, it will cause abnormal growth of its stem. Therefore, the degree of pest damage to Gastrodia elata can be analyzed through its appearance. The 0.5 in is used to reduce the influence of variance on the result. A larger variance of central offset means a large growth difference among plants, which may lead to distortion. is used to normalize the offset and more intuitively reflect its influence on distortion. When the mean central offset is small, it indicates that the plant growth is closer to the healthy state. On the contrary, it may imply a distortion risk. Therefore, it is directly incorporated into the formula to reflect its direct influence. The logarithmic form of the area offset variance plays a role in smoothing the influence. The larger the value of the area offset variance , the greater the deviation of the area size of the Gastrodia elata cutting contour, and the higher the distortion degree. Since the actual change in the area size of the Gastrodia elata stem cutting contour is small, a logarithmic function is used to limit the contribution index of the area offset variance . Among them, the mean central offset , the variance of central offset , the area offset variance and the Gastrodia elata distortion prediction index are all in a positive correlation. The larger the value, the larger the Gastrodia elata distortion prediction index . The area mean is used as a basic parameter to adjust the numerical ratio.

[0028] Perform a correlation analysis on the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index to generate a Gastrodia elata disease index , and the formula is as follows: ; The Gastrodia elata disease index is used to reflect the degree of disease of the corresponding Gastrodia elata, that is, the degree of influence by mold colonies and pests. The larger the value, the higher the degree of disease influence.

[0029] The Gastrodia elata disease index is a comprehensive indicator that reflects the overall degree of disease infection in Gastrodia elata plants. This index combines the morphological distortion of Gastrodia elata and the depth of mold colony infection to comprehensively measure the health status of the plants. The larger the value of the Gastrodia elata disease index, the more severely the plants are affected by the disease. At higher levels of the Gastrodia elata disease index, obvious distortions may exist in the stems of Gastrodia elata, or the molds may reproduce more densely on the surface or inside of Gastrodia elata. This situation usually indicates that the health and normal growth of the plants have been greatly disturbed.

[0030] The distortion prediction index represents the physical distortion of Gastrodia elata plants, such as bending or uneven growth. This kind of distortion is usually one of the manifestations of disease infestation. Therefore, as the degree of distortion increases, the disease index also increases. This monotonicity reflects the impact of the physical abnormality of the plants on their overall health. The colony prediction index represents the degree of mold infection in Gastrodia elata. The presence and proliferation of molds directly affect the health of the plants and are one of the important factors affecting the disease. Through the exponential function, the higher the degree of mold infection, the faster the index increases, which reflects the disease risk brought by mold infection.

[0031] Adopt reflects the sensitivity to mold infection. As increases, the exponential part shows rapid growth, ensuring that even a slight change in mold infection can significantly affect the disease index. In biological monitoring, this sensitivity helps in early identification and intervention. By multiplying the distortion and colony indices, it shows that both jointly affect the disease index. The multiplication combination not only ensures that both have an impact but also amplifies the effect of their combined action because the actual disease impact is usually the result of the superposition of multiple factors.

[0032] In the form of, ensure that in the absence of mold infection, even if is 0, the exponential part is still greater than 1, so that the distortion index part can still reflect the most basic disease risk, avoiding the situation where the index is zero when there is no colony infection, and ensuring the stability and continuity of the disease index in various situations. The coefficient 20 is used to amplify the impact of colony infection. From the perspective of exponential growth, if has a slight increase, then will cause the exponential term to expand rapidly, increasing the sensitivity to the disease severity, especially in the case where mold infection is relatively common, and being able to highlight the potential high risk of the disease. The disease threshold is 25.4. By selecting multiple slightly diseased Gastrodia elata plants, calculating their Gastrodia elata disease indices, taking the average value and then reducing the numerical size, the judgment standard is thus improved and used as the disease threshold , set to 25.4, and output the Gastrodia elata disease grade. When , for the Gastrodia elata disease index Compared with the disease threshold Make a comparison, and output that the corresponding Gastrodia elata disease level is secondary, the degree of disease infection is relatively deep, and it is necessary to observe specifically and determine the status of Gastrodia elata; when it is the case, output that the corresponding Gastrodia elata disease level is primary, the growth of Gastrodia elata is normal, and no specific observation is required. Referring to Figure 2 , the present invention also provides a Gastrodia elata disease monitoring system based on image recognition, which is used to execute the Gastrodia elata disease monitoring method based on image recognition, including: A numbering module for numbering G Gastrodia elata plants in the planting area; An image acquisition module for taking four-way shots of each Gastrodia elata plant, collecting image data of the Gastrodia elata stem, removing non-target Gastrodia elata pixel points through OpenCV and performing blackening processing, performing correlation analysis on the image data, and generating the hue, saturation, and lightness of the pixel points of the image data; A Gastrodia elata colony prediction module for setting a hue range threshold, a saturation range threshold, and a lightness range threshold. The hue range threshold, saturation range threshold, and lightness range threshold include the hue range threshold, saturation range threshold, and lightness range threshold. Compare the hue with the hue range threshold, the saturation with the saturation range threshold, and the lightness with the lightness range threshold, generate a mask, perform correlation analysis on the mask, generate the number of mold pixel points, and perform correlation analysis on the number of mold pixel points to generate a Gastrodia elata colony prediction index; A model generation module for establishing a 3D model of the numbered Gastrodia elata in the planting area, placing the 3D model in a three-dimensional coordinate system, performing equidistant horizontal segmentation processing on the Gastrodia elata 3D model, collecting the central coordinates and cross-sectional area values of the segmentation planes where each Gastrodia elata 3D model is located, processing the central coordinates and cross-sectional area values, generating a central offset mean and a central offset variance, and generating an area mean and an area offset variance; A model analysis module for performing correlation analysis on the central offset mean, central offset variance, area mean, and area offset variance to generate a Gastrodia elata distortion prediction index; A comprehensive analysis module for performing correlation analysis on the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index to generate a Gastrodia elata disease index, comparing the Gastrodia elata disease index with the disease threshold, and outputting the Gastrodia elata disease level.

[0033] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0034] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0035] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0036] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered within the protection scope of the present application.

Claims

1. A method for monitoring gastrodia elata diseases based on image recognition, characterized in that: The specific steps include: S1, collecting four-way image data of the Gastrodia elata stem of each Gastrodia elata plant in the planting area, removing non-target Gastrodia elata pixels and performing blackening processing through OpenCV, and then performing correlation analysis to generate hue, saturation and brightness data corresponding to each Gastrodia elata plant; S2, setting a hue range threshold, a saturation range threshold and a lightness range threshold, and comparing them with the hue, saturation and lightness data corresponding to each Gastrodia elata stem, generating a mask, and performing a correlation analysis on the mask, generating the number of mold pixels, performing a correlation analysis on the number of mold pixels, and generating a Gastrodia elata colony prediction index corresponding to each Gastrodia elata strain; S3, establishing a 3D model of Gastrodia elata in the planting area, and placing the 3D model in a three-dimensional coordinate system, performing equidistant horizontal segmentation processing on the Gastrodia elata 3D model, collecting the center coordinates and cross-sectional area values ​​of each Gastrodia elata on the segmentation surface where the 3D model is located, processing the center coordinates and cross-sectional area values, generating a center offset mean and a center offset variance, and generating an area mean and an area offset variance for each Gastrodia elata; S4. Perform correlation analysis on the center offset mean, center offset variance, area mean and area offset variance of each Gastrodia elata plant to generate a distortion prediction index for each Gastrodia elata plant. Perform correlation analysis on the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index to generate a Gastrodia elata disease index and perform pest and disease analysis to output the Gastrodia elata disease grade.

2. The method for monitoring gastrodia elata diseases based on image recognition according to claim 1, characterized in that: Gastrodia elata with exposed stems longer than 10 cm were selected for numbering, where the number of Gastrodia elata was G plants, and i was used as the index of Gastrodia elata, and the value range of i was a positive integer from 1 to G.

3. The method for monitoring gastrodia elata diseases based on image recognition according to claim 2, characterized in that: The pixel points of the image data are 1920*1080, the RGB channel value of the black pixel point is (0,0,0), and the image data is , where j is used to index the image viewing angle. When j=1, it is used to indicate a front view image; when j=2 or j=3, it is used to indicate a side view image; when j=4, it is used to indicate a rear view image; R, G, and B are used to indicate red, green, and blue values, respectively, with a value range of 0-255; M is used as the index of the pixel row of the image data, and N is used as the index of the pixel column of the image data; Perform correlation analysis to generate hue , Saturation , Brightness , the formula based on is: ； in is the two-argument inverse tangent function, is the maximum value of R, G, and B of the corresponding pixel. is the minimum value of R, G, and B of the corresponding pixel, hue Used to reflect the color type and saturation of the pixel Used to reflect the purity and brightness of pixels Used to reflect the brightness of the pixel color. hour, .

4. The method for monitoring gastrodia elata diseases based on image recognition according to claim 3, characterized in that: The hue range threshold range is , saturation range threshold is , the brightness range threshold is , create a mask , the formula based on is: ; Mask It is used to reflect whether the value of the pixel point is within the range of the hue range threshold, saturation range threshold and brightness range threshold. If yes, it is recorded as 1, otherwise it is recorded as 0 for counting; Perform correlation analysis on the mask to generate the number of mold pixels , the formula based on is: ； Number of mold pixels Used to reflect the predicted number of Gastrodia elata colony pixels in the photograph of the i-th Gastrodia elata plant; Correlation analysis was performed on the number of mold pixels to generate the Gastrodia elata colony prediction index , the formula based on is: ; Gastrodia elata colony prediction index Used to reflect the colony abnormality index of the i-th strain of Gastrodia elata.

5. The method for monitoring gastrodia elata diseases based on image recognition according to claim 2, characterized in that: In S4, a three-dimensional coordinate system includes X, Y, and Z axes, wherein the Z axis is perpendicular to the planting ground, the planting ground is used as the XOY plane, and multiple equidistant planes parallel to the XOY plane are used as segmentation planes, and the spacing between the segmentation planes is 1 cm. The 3D model of Gastrodia elata is segmented using the segmentation plane to obtain the center coordinates of the segmentation plane where the 3D model of each Gastrodia elata is located. and cross-sectional area , number the split faces from bottom to top, and use the superscript h to index the split face number. The value range is A positive integer between Perform correlation analysis to generate center-shifted means and the center shift variance , for the cross-sectional area value Perform correlation analysis to generate area means and area offset variance , the formula based on is: ; Among them, the center shift mean Used to reflect the degree of skewness of the i-th Gastrodia elata stem, the center shift variance Used to reflect the local bending index of the i-th Gastrodia elata stem, the area mean Used to reflect the average thickness uniformity of the i-th Gastrodia elata stem, area deviation variance Used to reflect the degree of deformity of the local thickness changes of the i-th Gastrodia elata stem.

6. The method for monitoring gastrodia elata diseases based on image recognition according to claim 5, characterized in that: Center shift mean , center shift variance , area mean and area offset variance Conduct correlation analysis to generate the Gastrodia elata distortion prediction index , the formula based on is: ； Gastrodia elata distortion prediction index Used to reflect the degree of deformation of Gastrodia elata stems. is the mean of the center shift within G strain Gastrodia elata The maximum value of .

7. The method for monitoring gastrodia elata diseases based on image recognition according to claim 4 or 6, characterized in that: Gastrodia elata distortion prediction index and Gastrodia elata colony prediction index Conduct correlation analysis to generate the Gastrodia elata disease index , the formula based on is: ； Gastrodia elata disease index Used to reflect the degree of disease on the corresponding Gastrodia elata.

8. The method for monitoring gastrodia elata diseases based on image recognition according to claim 7, characterized in that: Gastrodia elata disease index Disease threshold Compare and output the Gastrodia elata disease grade. When , the output corresponds to the level of Gastrodia elata disease as Level 2, which means that the disease infection is serious and it is necessary to observe and determine the status of Gastrodia elata in a targeted manner; when When the output corresponds to the level of Gastrodia elata disease, it is level one, Gastrodia elata is growing normally, and no targeted observation is needed.

9. A gastrodia elata disease monitoring system based on image recognition, used to execute the gastrodia elata disease monitoring method based on image recognition according to claim 1, characterized in that: include: A numbering module is used to number the G Gastrodia elata plants in the planting area; The image acquisition module is used to take four-directional photos of each Gastrodia elata plant, collect image data of the Gastrodia elata stem, remove non-target Gastrodia elata pixels and perform blackening processing through OpenCV, perform correlation analysis on the image data, and generate the hue, saturation, and brightness of the image data pixels; A Gastrodia elata colony prediction module is used to set a hue range threshold, a saturation range threshold and a brightness range threshold, wherein the hue range threshold, the saturation range threshold and the brightness range threshold include a hue range threshold, a saturation range threshold and a brightness range threshold, compare the hue with the hue range threshold, compare the saturation with the saturation range threshold, compare the brightness with the brightness range threshold, generate a mask, perform a correlation analysis on the mask, generate the number of mold pixels, perform a correlation analysis on the number of mold pixels, and generate a Gastrodia elata colony prediction index; A model generation module is used to establish a numbered 3D model of Gastrodia elata in the planting area, place the 3D model in a three-dimensional coordinate system, perform equidistant horizontal segmentation processing on the 3D model of Gastrodia elata, collect the center coordinates and cross-sectional area values ​​of the segmentation surface where each 3D model of Gastrodia elata is located, process the center coordinates and cross-sectional area values, generate a center offset mean and a center offset variance, and generate an area mean and an area offset variance; A model analysis module is used to perform correlation analysis on the center offset mean, center offset variance, area mean and area offset variance to generate a Gastrodia elata distortion prediction index; The comprehensive analysis module is used to perform correlation analysis on the Gastrodia elata distortion prediction index and the Gastrodia elata colony prediction index, generate the Gastrodia elata disease index, compare the Gastrodia elata disease index with the disease threshold, and output the Gastrodia elata disease grade.

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