TW201419171A - Method of leaf recognition - Google Patents

Abstract

A method of leaf recognition for identifying the species of a leaf is disclosed. The method comprises: an image accessing step, a pre-processing step, a features extracting step and a comparison step. The image accessing step accesses a input image containing a input leaf. The pre-processing step processes the input image with gray scaling and thresholding operations to generate a binary image, and calculates the centroid of the binary image to isolate the input leaf. The features extracting step extracts features of the input leaf, where the features includes at least one clear feature and at least one fuzzy feature. The comparison step uses the clear features to classify the leaves; then locates all the species of a database with the same clear feature classification of the input leaf; further compares the difference between the fuzzy features of the input leaf and the fuzzy features of the above mentioned species. Accordingly, the species that the input leaf belongs to can be recognized.

Description

Leaf identification method

The invention relates to a leaf identification method, in particular to a leaf identification method for identifying a plant species to which a leaf belongs by image analysis processing technology.

There are many species in the plant world, and there are an estimated 350,000 species of plants around the world. Therefore, many species with similar characteristics are difficult for plant experts to identify differences, and for the general public, the types of plants that can be distinguished are often countable.

Among the various organs of the plant, the leaves are important organs for identifying plants. Since the leaves are closer to the two-dimensional plane image than the flowers and other organs, and have a wealth of information on various geometric features, the shape, color, and shape of the leaves through the leaves. Characteristics such as morphology and growth order can distinguish most plant species. Therefore, when it is desired to identify the plant species to which a leaf belongs, the leaves are usually the preferred indicator of judgment. However, the conventional method of leaf identification is to observe, measure and record the characteristics of a leaf by manpower, and then compare the data on the plant map for plant species discrimination. However, the process of observing the leaves is often time-consuming and laborious. In addition, the number of plant species recorded on the plant illustrations is innumerable, leading to the inefficiency of the conventional leaf identification method. In addition, the method of manually measuring the characteristics of the leaves and comparing the data with the naked eye is easy to cause the judgment result to be wrong due to human error.

For the above reasons, it is necessary to further provide an improved leaf identification method, in order to be able to identify leaves using image analysis processing technology and save labor. Identify the manpower, material and time spent on leaves, and achieve the ability to quickly and accurately identify the plant species to which a leaf belongs.

The object of the present invention is to improve the above-mentioned disadvantages, and to provide a leaf identification method, which can analyze the leaf to be tested by a computer system to identify the plant species to which the leaf to be tested belongs, and has the effect of reducing the cost of identifying the leaves.

Another object of the present invention is to provide a leaf identification method for identifying a plant species to which a leaf belongs according to an image recognition method, which has the effect of improving the accuracy of leaf recognition by eliminating the influence of human error.

The leaf identification method of the present invention identifies a plant species to which the leaf to be tested belongs by using a computer system and a leaf image database of the computer system, comprising: an image reading step, the computer system reading in a image to be tested The image to be tested includes the leaf to be tested; a pre-processing step is to perform grayscale processing and binarization processing on the image to be tested, generate a binarized graph, and calculate the quality of the binarized graph. a heart position to separate the leaf to be tested in the image to be tested; a feature extraction step of extracting a feature value of the leaf to be tested, the feature value comprising at least one explicit feature and at least one statistical feature, the at least one clear Characterized by a leaf feature having a definite value, the at least one statistical feature is a leaf feature having a range of values; and a data matching step to screen out the image database of the leaf having the same clear characteristics as the leaf to be tested Plant species, and comparing the statistical characteristics of each plant species with the statistical characteristics of the leaf to be tested to determine the tree to be tested The plant species to which the leaf belongs.

The leaf identification method of the present invention, wherein the algorithm used for the comparison operation of the data matching step comprises a least square method, and the minimum flat method operation formula is as shown in the following formula (5): Where ε is the least square error of the plant species to be compared with the plant species in the leaf image database, i is the number of the statistical feature, n is the number of the statistical features, and d _i is taken from the leaf to be tested The i-th statistical feature, ε _{i is} the least square error of the i-th statistical feature of the leaf to be tested compared to the plant species, and max _{i is} the plant species in the leaf image database, the recorded i The maximum value of the statistical features, min _{i is} the minimum value of the i-th statistical feature recorded by the plant species in the leaf image database.

The leaf identification method of the present invention, wherein the at least one explicit feature comprises a number of points of upper and lower edges, the number of points of the upper and lower edges being the intersection of a horizontal line and the upper and lower edges of the blade of the leaf to be tested.

The leaf identification method of the present invention, wherein the at least one statistical feature comprises a compactness, a circular ratio, a rectangular ratio, a convex envelope area ratio, an angle, a length to length axis ratio, a tip length, a petiole length, and an upper and lower half area ratio.

The method for identifying a leaf of the present invention, wherein the grayscale processing takes the saturation of the image to be tested as a grayscale value of the image to be tested.

The leaf identification method of the present invention, wherein the gray scale processing is taken The saturation of the image is measured, and an edge detection operation is performed to calculate an image gradient value of each pixel of the image to be tested, and the saturation is added as the grayscale value of the image to be tested.

The leaf identification method of the present invention, wherein the pre-processing step further comprises a noise filtering operation, wherein the noise filtering operation performs expansion and erosion operations on the binarized graphics to fill holes and eliminate noise. The centroid position of the binarized graph is then calculated.

The leaf identification method of the present invention, wherein the binarization processing performs binarization operation using an OTSU automatic threshold method.

The above and other objects, features and advantages of the present invention will become more <RTIgt; (pixels) is the smallest unit of an image used to represent the resolution of the image. For example, if the resolution of the image is 1024×768, it means that the image is common (1024× 768 = 786432) pixels are understood by those of ordinary skill in the art to which the present invention pertains.

The "color level" as used throughout the present invention refers to the degree of shading of the color component or brightness exhibited by the pixel, for example, the red (R), green (G), and blue (color) images. B) The range of the color gradation of the component may be 0 to 255, or the gradation range of the luminance of the gray-level image may range from 0 to 255, which is generally in the technical field of the present invention. Knowledge people can understand.

The "leaf" as used throughout the present invention refers to a vegetative organ of a plant, and includes three parts: a leaf, a petiole, and a stipules. The part of the leaf near the petiole is called the leaf base, along the main vein (primary The portion of the vein) away from the petiole is referred to as the tip of the leaf, as will be understood by those of ordinary skill in the art to which the present invention pertains.

Please refer to FIG. 1 , which is a system architecture diagram of a preferred embodiment of the leaf identification method of the present invention, connected to a computer system 2 by a source 1 to be tested (for example, a conventional computer host, a file server, or a cloud server) As an execution architecture. The image source 1 to be tested may be a camera, a camera, a scanner, or a storage device, and may store only one image to be tested A. The image to be tested A may be a color or black and white image, and the image to be tested is A. It contains a leaf image and a petiole image of the leaf B to be tested. The computer system 2 has a leaf image database 21, and the leaf image database 21 includes leaf image data of a plurality of plant species, and the plurality of plant species covers all plant species to which the leaf B to be tested may belong. The computer system 2 is coupled to the image source 1 to be tested to receive the image A to be tested, and according to the operation flow disclosed in the preferred embodiment of the leaf identification method of the present invention, the image to be tested can be identified. Contains the plant species to which the leaf B to be tested belongs. In this embodiment, the image to be tested A is described by using an RGB color image as an implementation aspect, but not limited thereto, and the like, and can be applied to leaf recognition of black and white images or other kinds of color images. It is understood by those of ordinary skill in the art to which the present invention pertains, and is not described herein.

Please refer to FIG. 2, which is a flow chart of the operation of the preferred embodiment of the leaf identification method of the present invention. Wherein the leaf identification method comprises an image reading Step S1, a pre-processing step S2, a feature extraction step S3, and a data matching step S4 are respectively described as follows.

The image reading step S1 first reads, by the computer system 2, a to-be-tested image A from the image source 1 to be tested, and the image to be tested A includes a leaf image and a petiole image of the leaf B to be tested. The leaf end of the leaf B to be tested faces the image A to be tested, and the leaf base of the leaf B to be tested faces the image A to be tested. In the embodiment, the image to be tested A is an RGB image, but the invention is not limited thereto. After the image reading step S1 is completed, the pre-processing step S2 is started.

Generally, the source of the image to be tested that is read in is usually a picture taken or scanned by the camera. The content of the image to be tested A is often mixed with other external environmental factors or noise components in addition to the leaf B to be tested. For example, the uneven brightness distribution, the shadow of the light incident angle, the partial defect of the blade or the rupture of the petiole are not conducive to the identification of the leaf. Therefore, the pre-processing step S2 must be performed for the image A to be tested, which includes a gray scale processing. Sub-step S21, a binarization sub-step S22 and an object search sub-step S24, and preferably comprise a noise filtering sub-step S23, which are described in detail below.

Since the color of the leaf changes due to the color of the external light source, the image to be tested A needs to be subjected to gray scale processing. In the gray scale processing substep S21, the saturation of the image to be tested A is taken. S is used as the grayscale value to generate a grayscale image. Compared with the traditional gradation value of the RGB image, the color gradation values of the red, green and blue gamuts are respectively multiplied by the weight gradation conversion mode, which can effectively avoid the shadow being mistakenly judged as the blade. The situation of the outline. The manner of calculating the saturation S of the image to be tested A is as follows: (1): Where (x, y) is the pixel coordinate value, and R(x, y), G(x, y), and B(x, y) represent the gradation values of the red, green, and blue color gamuts, respectively.

In addition to directly generating the grayscale image with the saturation S as the grayscale value, the grayscale processing substep S21 preferably includes an edge detection operation, and the main principle is to calculate the image gradient of each pixel in the image to be tested A (gradient) The value ▽ I , the calculation method of the image gradient value ▽ I is as shown in the following formula (2): The image gradient value ▽ I is then added to the saturation S generated by the gray-scale processing sub-step S21 to generate the gray-scale image. The gray-scale image containing the image gradient value ▽ I has a clearer outline than the gray-scale image with the saturation S as the gray-scale value.

Since the leaf identification method of the present invention only uses the shape characteristics of the leaves to identify the plant species to which the leaves belong, the lines, veins, and color depths on the leaves are invalid feature information, so the gray scale image needs to be binarized. The binarization sub-step S22 selects a threshold value, and compares the gray scale value of each pixel of the gray scale image with the threshold value, and the pixel with the gray scale value greater than the threshold value in the gray scale image is converted into 1 (white), pixels with grayscale values less than this threshold will be converted to 0 (black) to produce a binarized graph. The method for selecting the threshold may be a bimodal method, an iterative method or an OTSU automatic threshold method, and the OTSU automatic threshold method is preferably used in the embodiment.

The noise filtering sub-step S23 filters the binary image Processing, the main principle is to use dilation erosion (erosion) two basic morphological operations, fill the hole or eliminate noise, eliminate the noise inside and outside the leaf B to be tested in the binarized graph. However, the noise filtering sub-step S23 can be selectively performed, for example, when a to-be-tested image A is sufficiently clear that the binarized pattern generated thereby does not include a noise point that may affect the operation result of the subsequent step, may be omitted. This step.

Since the image to be tested A may contain other objects in addition to the leaf B to be tested, it is necessary to perform an object search process on the binarized image of the image to be tested A to find the leaf B to be tested. The search sub-step S24 first calculates a centroid position, which is calculated as shown in the following equation (3): Where N is the number of pixels with a gradation value of 1, Q ( x , y ) is the centroid position, p _x ( i ) is the x coordinate value of the pixel with the ith gradation value of 1, p _y ( i ) is the y coordinate value of the pixel whose i-th gradation value is 1. Next, the pattern closest to the centroid position in the binarized pattern is determined as the leaf B to be tested, and the leaf B to be tested is separated for subsequent recognition of the leaves.

The feature extraction step S3 captures a plurality of feature values C in the leaf B to be tested, and the plurality of feature values C are various features of the leaf shape, and include at least one explicit feature C1 and at least one statistical feature C2. Wherein, the explicit feature C1 refers to a leaf feature having a definite value, such as the number of endpoints of the leaf edge or the leaf base, the edge of the blade being convex or concave, and the like; the statistical feature C2 refers to the feature of the leaf having a range of values. For example, blade area, leaf length, and the like. Since the leaf B to be tested contains the image of the blade and the image of the petiole, The feature extraction step S3 is to distinguish the blade and the petiole of the leaf B to be tested by a petiole separation operation before starting to capture the plurality of feature values C. It is known that the leaf end of the leaf B to be tested is directed above the image to be tested A, and the leaf base is directed below the image to be tested A, according to which the petal separation operation system takes a leaf handle width from the lower end of the leaf B to be tested. And upwardly comparing the width of each horizontal position of the leaf B to be tested until a level is found, the width of the leaf B to be tested is greater than four times the width of the petiole, and the leaf B to be tested is determined to be at the level The following part is the petiole, and the part above the level is the blade.

In this embodiment, the explicit feature C1 is the number of points on the lower edge of the leaf B to be tested, but the invention is not limited thereto, and the explicit feature C1 may also be other features such as the number of upper and lower ends of a leaf, or Can contain multiple features at the same time. Referring to FIG. 4, the upper and lower edge points are obtained by finding the upper and lower end points of the leaves of the leaves B and B' to be tested, and respectively drawing a horizontal line as an upper boundary L _U and The boundary L _{D is} further scanned downward from the upper boundary L _U by a horizontal detection line T _U , and scanned upward from the lower boundary L _D by another horizontal detection line T _D , wherein the scanning distance is preferably smaller than the upper end Half the distance between the point and the lower end point. Recording the number of intersections between the horizontal detection lines T _U and T _D and the leaf B to be tested during the scanning process, and counting the number of intersections with the most occurrences as the number of edge points and lower edge points of the leaf B to be tested. The number of the upper edge points and the lower edge points is the number of the upper and lower edge points. For example, as shown in the leaf B to be tested, when the horizontal detection line T _U is scanned downward from the upper boundary L _U , the number of intersections with the leaf B to be tested is the highest, and the horizontal detection line When T _D is scanned upward from the lower boundary L _D , the number of intersections with the leaf B to be tested is the most frequently occurring, so the leaf B to be tested has the number of points on the upper and lower edges (4, 2); The leaf B' is measured to have the number of points on the upper and lower edges (2, 2). The leaf image database 21 classifies the plant species to which the leaves belong according to the explicit feature C1. Therefore, in the embodiment, the leaf image database 21 can have the number of points on the upper and lower edges (2, 2), (4, 2). ), (6, 2), (2, 4), etc.

In this embodiment, the at least one statistical feature C2 includes a compactness, a circularity ratio, a rectangularity ratio, and a convex hull ratio of the leaf B to be tested. ), angle (angle), length to short axis ratio, tip length, petiole length and upper and lower half area ratio, etc., a total of 11 statistical characteristics C2, but not limited to this.

The compactness is the ratio of the square of the blade circumference of the leaf B to be tested to the leaf area, and the calculation formula is as shown in the following formula (4): Wherein, CP is compactness, P _{B is} the blade circumference of the leaf _B to be tested, and A B is the leaf area of the leaf _B to be tested, which is equivalent to the sum of the number of pixels of the leaf B to be tested.

The circular ratio is the area ratio of the blade of the leaf B to be tested to a circle, and the circumference of the circle is the same as the blade circumference of the leaf B to be tested, and the calculation formula is as shown in the following formula (5): Wherein, CR is a circular ratio, P _{B is} the blade circumference of the leaf _B to be tested, and A B is the leaf area of the leaf _B to be tested.

The rectangular ratio is the ratio of the blade area of the leaf B to be tested to a minimum rectangular area, wherein the minimum rectangular area is generated by rotating the blade of the leaf to be tested in units of 1°, and each rotation is 1 ° Calculate the rectangular footprint of the blade of the leaf B to be tested during the rotation, and finally take the minimum value to obtain the minimum rectangular area. The calculation formula of the rectangle ratio is as shown in the following formula (6): Wherein RR is a rectangular ratio, A _{B is} the blade area of the leaf _B to be tested, and A _{rect is} the minimum rectangular area.

The convex package area ratio is a ratio of the blade area of the leaf B to be tested to a convex package area, wherein the convex type package is a closed area covering the blade of the leaf B to be tested, and the leaf to be tested B is The connection of any two points in the blade will not exceed the range of the convex package. The convex package can be operated by the conventional convex package algorithm to calculate the blade of the leaf B to be tested. The conventional convex package algorithm mainly includes an incremental algorithm (Incremental coven hull algorithm) and a Jarvis step method. (Jarvis march), Graham scan, Monotone chain, Divide and conquer, or Quick hull, etc., in this embodiment, the fast packet method is preferably used. The calculation formula of the convex package area ratio is as shown in the following formula (7): Wherein, CHA is a convex package area ratio, A B is the blade area of the leaf _B to be tested, and A _{conv is} the convex package area.

Referring to FIG. 5, the end point o and the lower end point p of the blade of the leaf B to be tested are known, and the length H of the leaf to be tested of the leaf B to be tested is measured, and the upper end o extends vertically downward. At a height of a custom length h, a horizontal line is drawn and the edge of the leaf B to be tested has two intersections m, n, and the angle between the two intersections m and n respectively connected to the upper end point o is the angle θ And at a height at which the lower end point p extends vertically upwards by a custom length h, a horizontal line is drawn with two intersections q and r of the edge of the leaf B to be tested, and the two intersections q and r are respectively connected to the lower end p The angle formed is the angle θ'. In this embodiment, the custom length h is preferably one quarter and one sixth of the length H of the leaf to be tested, so four angle feature values will be generated.

Please refer to the leaf B to be tested shown in FIG. 4. If the angle is to be smashed, since there are two end points on the leaf B to be tested, the computer system 2 uses an optional point as a reference point. In addition, when the reference point extends vertically downwards at a height of a custom length h, when drawing a horizontal line, there may be 4 intersections with the edge of the leaf B to be tested, and the angle feature value cannot be calculated according to the foregoing method, so the computer The system 2 calculates a total length of the horizontal line intersecting the leaf B to be tested as a bottom edge, and takes an end point and the bottom edge to form a standard triangle, and the standard triangle may be a right triangle or an isosceles triangle, and the like The end point is spaced from the bottom edge by the custom length h, and the standard triangle has the angle of the end point as the apex, and can be used as the angle feature value to be extracted.

The long axis ratio refers to the ratio of the long axis r _L to the short axis r _S of the leaf B to be tested. Wherein, the long axis r _L refers to the longest distance between the centroid position and the edge of the leaf B to be tested, and the short axis r _S refers to the shortest distance between the centroid position and the edge of the leaf B to be tested. The centroid position of the leaf B to be tested is calculated in the same manner as described in the object search sub-step S24 of the pre-processing step S2.

The calculation method of the length of the leaf end is similar to the separation operation of the petiole, and takes a leaf end width from the upper end of the leaf B to be tested, and compares the width of each horizontal position of the leaf B to be tested downward until a level is found. The height of the leaf B to be tested is greater than four times the width of the leaf end, and the portion above the horizontal height is regarded as the leaf end of the leaf B to be tested, so the upper end of the leaf B to be tested is The vertical distance of the horizontal height is the length of the tip end.

Please refer to the leaf B to be tested shown in Fig. 4. If you want to lengthen the leaf end, since there are two end points on the leaf B to be tested, the computer system 2 will select any point as a reference point. The leaf end of the reference point takes a leaf end width. In addition, when the width of each horizontal position of the leaf B to be tested is downward, a horizontal line should be drawn at each horizontal position, and the total length of the horizontal line intersecting the leaf B to be tested is calculated as the leaf B to be tested. The width of each horizontal position is compared to the width of the tip end.

The length of the petiole is calculated by adding the number of pixels of the left half of the petiole of the leaf B to be tested to the number of pixels of the right half of the contour, and averaging even if the petiole of the leaf B to be tested is curved. The length of the petiole can also be calculated correctly.

The calculation method of the upper and lower half area ratios is to determine the length of the leaf end, and the determined leaf end of the leaf B to be tested is removed. Then calculating the length of the leaf of the leaf B to be tested after removing the leaf end, and dividing the leaf of the leaf B to be tested into an upper half and a lower half according to the length, the ratio of the area of the upper half to the lower half That is, the upper and lower half area ratio.

As described above, after the computer system 2 captures the plurality of feature values C of the leaf B to be tested, the data comparison step S4 is performed, according to the plurality of special The levy value C is compared with the leaf image data of the plurality of plant species of the leaf image database 21 to identify the plant species to which the leaf B to be tested belongs.

The leaf image data of the leaf image database 21 contains the characteristic value C data of each plant species, that is, the explicit feature C1 and the statistical feature C2 are also included. Wherein, since the statistical feature C2 refers to a leaf feature having a range of values, the statistical feature C2 has a maximum value and a minimum value. The data comparison step S4 is firstly based on the explicit feature C1 of the leaf B to be tested, and in the present embodiment, the number of the upper and lower edge points is selected, and the leaf image database 21 is selected to have the same upper and lower edges as the leaf B to be tested. Points are therefore attributed to plant species of the same classification. Thereby, the plant species having different numbers of the upper and lower edge points from the leaf B to be tested can be ignored, and the subsequent steps are avoided for redundant comparison operations of the plant species that cannot be tested for the leaf B to be tested.

Then, the data comparison step S4 is operated by a least square method, and the statistical feature C2 of the leaf B to be tested is associated with each plant species of the same classification as the leaf image database 21 and the leaf B to be tested. The statistical feature C2 performs a one-by-one comparison operation, and the minimum flat method operation formula is as shown in the following formula (8): Where ε is the least square error of the leaf species B to be compared with a plant species in the leaf image database 21, i is the number of the statistical feature C2, so in the present embodiment, there are i=1, 2, .. ., 11 of 11 statistical features C2, D _i is the i-th measured from the statistical feature leaves fetched C2 B, D _i [epsilon] _i is the least squares as compared to the i-th statistical characteristics of the plant species of C2 The error, max _{i is} the maximum value of the i-th statistical feature C2 recorded by the plant species in the leaf image database 21, and min _{i is} the plant species in the leaf image database 21, the recorded i The minimum value of the statistical feature C2. Through the above formula (8), each plant species in the leaf image database 21 and the leaf B to be tested belong to the same classification, and the least square error ε generated in the leaf B to be tested is calculated. The plant species with the lowest ε value can be determined as the plant species to which the leaf B to be tested belongs.

In addition, since the numerical range of each statistical feature C2 is not the same, the magnitude of the least square error ε _{i of} each statistical feature C2 is also different, resulting in the influence of the least square error ε on each statistical feature C2 in the above formula (5). It may also be different. Therefore, the data comparison step S4 preferably includes a normalization operation before performing the least squares operation, and the maximum value of the statistical feature C2 of each plant species included in the leaf image database 21 is The minimum value is respectively removed from all plant species of the leaf image database 21, the maximum value of the statistical feature C2, and the statistical feature C2 extracted from the leaf B to be tested is also removed, and all plants of the leaf image database 21 are also removed. The maximum value of this statistical characteristic C2 in the species. Accordingly, it is possible to avoid the influence of the different statistical characteristics C2 on the least square error ε .

In summary, the leaf identification method of the present invention begins with the image reading step S1, in which the computer system 2 reads an image to be tested A, and performs image processing on the image to be tested A through the pre-processing step S2. Analytical processing to The image A to be tested is converted into a binary image, and the leaf B to be tested is taken out, and the feature extraction step B is taken for the feature extraction step S2, and then the data comparison step S6 is performed. The feature value C is compared with the leaf image data of the plurality of plant species of the leaf image database 21 to identify the plant species to which the leaf B to be tested belongs. Accordingly, the leaf identification method of the present invention achieves the purpose of identifying leaves through image analysis processing techniques.

Therefore, the preferred embodiment of the leaf identification method of the present invention only needs to cooperate with a computer system 2 to identify the leaves by the image A to be tested provided by the image source 1 to be tested, and the efficiency is compared with the traditional artificial measurement of the leaf features. The visual comparison of the image data is greatly improved, and the labor, material resources and time spent manually identifying the leaves can be saved to reduce the cost of leaf identification.

Furthermore, the preferred embodiment of the leaf identification method of the present invention uses an image recognition method to identify a plant species to which the leaf B to be tested belongs, thereby avoiding errors caused by human measurement and judgment, and improving the accuracy of leaf recognition.

In the preferred embodiment of the leaf identification method of the present invention, the plant species to which the leaves belong to the image A can be quickly and efficiently measured by analyzing and processing an image, thereby improving the efficiency and accuracy of leaf identification, and further Achieve the effects of "reducing the cost of identifying leaves" and "increasing the accuracy of leaf identification".

While the invention has been described in connection with the preferred embodiments described above, it is not intended to limit the scope of the invention. The technical scope of the invention is protected, and therefore the scope of the invention is defined by the scope of the appended claims.

〔this invention〕

1‧‧‧Source of image to be tested

2‧‧‧ computer system

3‧‧‧Foliage Image Database

A‧‧‧ image to be tested

B‧‧‧ leaves to be tested

B’‧‧‧ leaves to be tested

C‧‧‧ eigenvalue

C1‧‧‧clear features

C2‧‧‧ statistical characteristics

S1‧‧‧Image reading step

S2‧‧‧ pre-processing steps

S21‧‧‧ Grayscale processing substeps

S22‧‧‧ Binarization substep

S23‧‧‧Hard filter substeps

S24‧‧‧Object Search Substep

S3‧‧‧Character extraction steps

S4‧‧‧ data comparison steps

L _U ‧‧‧ upper border

L _D ‧‧‧ lower border

T _U ‧‧‧ horizontal test line

T _D ‧‧‧ horizontal test line

H‧‧‧Lower length

h‧‧‧Custom length

Θ‧‧‧ angle

’’‧‧‧ angle

o‧‧‧Upper endpoint

P‧‧‧ lower end

m, n‧‧‧ intersection

q, r‧‧‧ intersection

S‧‧‧Saturation

▽ I ‧‧‧ image gradient values

Figure 1 is a system architecture diagram of a preferred embodiment of the leaf identification method of the present invention

Figure 2 is a flow chart showing the operation of the preferred embodiment of the leaf identification method of the present invention

Figure 3: Internal flow chart of the pre-processing steps of the preferred embodiment of the leaf identification method of the present invention

Figure 4: Schematic diagram of the calculation of the number of points on the lower edge above the preferred embodiment of the leaf identification method of the present invention

Figure 5 is a schematic diagram showing the angle calculation of the preferred embodiment of the leaf identification method of the present invention

S1‧‧‧Image reading step

S2‧‧‧ pre-processing steps

S3‧‧‧Character extraction steps

S4‧‧‧ data comparison steps

Claims (8)

A leaf identification method for identifying a plant species to which a leaf to be tested belongs by using a computer system and a leaf image database of the computer system, comprising: an image reading step, the computer system reading in a image to be tested, The image to be tested includes the to-be-tested leaf; a pre-processing step performs grayscale processing and binarization processing on the image to be tested, generates a binarized graph, and calculates a centroid of the binarized graph Positioning to separate the leaves to be tested in the image to be tested; a feature extraction step of extracting feature values of the leaf to be tested, the feature value comprising at least one explicit feature and at least one statistical feature, the at least one explicit feature For a leaf feature having a definite value, the at least one statistical feature is a leaf feature having a numerical range; and a data matching step to screen out a plant having the same clear characteristics as the leaf to be tested in the leaf image database Species, and comparing the statistical characteristics of each plant species with the statistical characteristics of the leaves to be tested to determine the plant species to which the leaves to be tested belong. The leaf identification method according to claim 1, wherein the algorithm used in the comparison operation includes a least square method, and the minimum flat method operation formula is as follows: Where ε is the least square error of the plant species to be compared with the plant species in the leaf image database, i is the number of the statistical feature, n is the number of the statistical features, and d _i is taken from the leaf to be tested The i-th statistical feature, ε _{i is} the least square error of the i-th statistical feature of the leaf to be tested compared to the plant species, and max _{i is} the plant species in the leaf image database, the recorded i The maximum value of the statistical features, min _{i is} the minimum value of the i-th statistical feature recorded by the plant species in the leaf image database. The method for identifying leaves according to claim 1 or 2, wherein the at least one explicit feature comprises a number of upper and lower edge points, wherein the upper and lower edge points are a horizontal line and the upper and lower edges of the blade of the leaf to be tested. The number of intersections. The leaf identification method according to claim 3, wherein the at least one statistical feature comprises a compactness, a circular ratio, a rectangular ratio, a convex package area ratio, an angle, a long axis ratio, a leaf end length, and a petiole. The length is compared with the upper and lower half areas. The method for identifying leaves according to claim 1 or 2, wherein the grayscale processing takes the saturation of the image to be tested as a grayscale value of the image to be tested. The method for identifying leaves according to claim 1 or 2, wherein the grayscale processing takes the saturation of the image to be tested, and performs an edge detection operation to calculate an image gradient of each pixel of the image to be tested. The value is added to the saturation as the grayscale value of the image to be tested. The method for identifying leaves according to claim 1 or 2, wherein the pre-processing step further comprises a noise filtering operation, wherein the noise filtering operation performs expansion and erosion on the binarized image. The operation fills the hole and eliminates the noise, and then calculates the centroid position of the binarized figure. The leaf identification method according to claim 1 or 2, wherein the binarization processing is performed by an OTSU automatic threshold method.