Schizothorax fish identification method, device, equipment, medium and product
Through otolith image processing, the Fourier and wavelet coefficients of the otolith profile are extracted and standardized, and the rapid identification of Tarimite cleft fish, wide-mouth fish and thick-lip-mouth fish are achieved, and the identification difficulties in the prior art are solved. and the problem of resource decline provides efficient fish resource assessment support.
Patent Information
- Application Number
- CN202411161061.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-21
- Publication Date
- 2024-12-10
AI Technical Summary
The prior art is difficult to accurately distinguish Tarim cleft fish, wide-mouth cleft fish and thick-lipped cleft fish. Especially in the case of fish resources decline and individual miniaturization, traditional morphological analysis has variability and cross-characteristic influences, and molecular biology Learning methods is time-consuming, financial and work-intensive, and requires high preservation of specimens.
By obtaining fish otolith images, the elliptical Fourier coefficients and discrete wavelet coefficients of the otolith profile are extracted, and the otolith morphological characteristics are compared to achieve rapid and efficient identification of fish.
Overcome the variability of traditional methods and the high cost of molecular biology, and achieve rapid and accurate identification of fish of genus genus genus, supporting resource evaluation and protection.
Smart Images
Figure CN119107495A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of aquatic wildlife protection, and in particular to a method, device, equipment, medium and product for identifying fish of the genus Schizothorax. Background Art
[0002] Schizothorax biddulphi, Schizothorax eurystomus and Schizothorax curvifrons belong to the Cypriniformes, Cyprinidae, Schizothorinae and Schizothorax genus. The scales of the forequarters of the wide-mouthed Schizothorax are arranged irregularly, the mouth is horizontal and straight, and the lower jaw has a sharp horny edge, while the scales of the forequarters of the Tarim Schizothorax and the thick-lipped Schizothorax are arranged neatly. The mouth of the Tarim Schizothorax is horseshoe-shaped, the snout is sharp, and the lower lip is narrow. The lower lip of the thick-lipped Schizothorax is fatter and the two lobes are discontinuous. Studies have shown that these three fish have similar morphology and are still difficult to distinguish accurately. If the identification and composition of mixed species in the habitat cannot be well solved, it will be difficult to correctly and accurately evaluate the current stock and dynamics of resources.
[0003] Among traditional methods, fish morphology has always been an important basis for population or group identification. Based on some traditional morphological and framework measurements of fish (such as weight, total length and body length, etc.), as well as some external features of fish (such as the number of fins), predecessors have established a series of fish identification methods. Traditional methods have been very fast and effective in previous surveys and are classic methods for fish population identification. In particular, they are irreplaceable by other methods in terms of the convenience of data acquisition and the protection of fish bodies. However, since fish morphology is closely related to its physiological condition and development stage, there is a great deal of variability.
[0004] There is overlap in some countable traits of the appearance of the three species of Schizothorax, which may have little impact on the correct evaluation of the species when the resources of the genus Schizothorax were abundant in the past, so that traditional morphological analysis can objectively reflect the species composition to a large extent. However, due to the sharp decline in the resources of the genus Schizothorax, the miniaturization and low age of individuals are also very prominent, so it is particularly important to more accurately distinguish and identify the three species of Schizothorax. Compared with traditional methods, molecular biology can accurately analyze the species composition of fish from a genetic and biochemical perspective, but it consumes more time, money and workload, and has certain requirements for the preservation of specimens.
[0005] Due to the above situation, there is an urgent need to find a convenient and efficient method for identifying fish of the genus Schizothorax. Summary of the invention
[0006] The purpose of the present application is to provide a method, device, equipment, medium and product for identifying fish of the genus Schizothorax, which can quickly and efficiently identify fish of the genus Schizothorax.
[0007] To achieve the above objectives, this application provides the following solutions:
[0008] In a first aspect, the present application provides a method for identifying fish of the genus Schizothorax, comprising: obtaining otolith images of multiple fish of the genus Schizothorax in different waters; extracting otolith contours from each otolith image; determining elliptical Fourier coefficients and discrete wavelet coefficients of each otolith contour, and standardizing the elliptical Fourier coefficients and discrete wavelet coefficients of each otolith contour; reconstructing the otolith contour of each fish of the genus Schizothorax according to the standardized elliptical Fourier coefficients and the standardized discrete wavelet coefficients; comparing the otolith morphological characteristics of multiple fish of the genus Schizothorax based on the reconstructed otolith contour of each fish of the genus Schizothorax, and obtaining the otolith morphological characteristics of each fish of the genus Schizothorax that distinguishes it from other fish of the genus Schizothorax; identifying fish of the genus Schizothorax based on the otolith morphological characteristics of each fish of the genus Schizothorax that distinguishes it from other fish of the genus Schizothorax.
[0009] In a second aspect, the present application provides a device for identifying fish of the genus Schizothorax, comprising: an image acquisition module, a contour extraction module, a standardization module, a screening module, a reconstruction module, a comparison module and an identification module. The image acquisition module is used to acquire otolith images of various genus Schizothorax fish in different waters. The contour extraction module is used to extract otolith contours from each otolith image. The standardization module is used to determine the elliptical Fourier coefficients and discrete wavelet coefficients of each otolith contour, and standardize the elliptical Fourier coefficients and discrete wavelet coefficients of each otolith contour. The reconstruction module is used to reconstruct the otolith contour of each genus Schizothorax fish according to the standardized elliptical Fourier coefficients and the standardized discrete wavelet coefficients. The comparison module is used to compare the otolith morphological characteristics of various genus Schizothorax fish according to the otolith contour reconstructed by each genus Schizothorax fish, and obtain the otolith morphological characteristics of each genus Schizothorax fish that distinguishes it from other genus Schizothorax fish. The identification module is used to identify the genus Schizothorax fish according to the otolith morphological characteristics of each genus Schizothorax fish that distinguishes it from other genus Schizothorax fish.
[0010] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for identifying fish of the genus Schizothorax.
[0011] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for identifying fish of the genus Schizothorax.
[0012] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for identifying fish of the genus Schizothorax.
[0013] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0014] The present application provides a method, device, equipment, medium and product for identifying fish of the genus Schizothorax. The otolith profile of each fish of the genus Schizothorax is reconstructed by elliptical Fourier coefficients and discrete wavelet coefficients. The otolith morphological characteristics of multiple fish of the genus Schizothorax are compared based on the reconstructed otolith profile of each fish of the genus Schizothorax, so that the otolith morphological characteristics of each fish of the genus Schizothorax that distinguish it from other fish of the genus Schizothorax can be obtained, and the fish of the genus Schizothorax can be identified based on this, overcoming the defects of molecular biology in that it consumes a lot of time, money and workload, and has certain requirements for the preservation of specimens, and achieving rapid and efficient identification of fish of the genus Schizothorax. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0016] Figure 1 This is a diagram of an application environment of a method for identifying fish of the genus Schizothorax in one embodiment of the present application;
[0017] Figure 2 A schematic diagram of a flow chart of a method for identifying fish of the genus Schizothorax provided in one embodiment of the present application;
[0018] Figure 3 A schematic diagram of average otolith shapes of three species of Schizothorax fish provided in another embodiment of the present application;
[0019] Figure 4 A schematic diagram of otolith morphological characteristics provided in another embodiment of the present application;
[0020] Figure 5 A schematic diagram of the relationship between quality and wavelet series provided in another embodiment of the present application;
[0021] Figure 6 A schematic diagram of the relationship between mass and Fourier harmonics provided in another embodiment of the present application;
[0022] Figure 7 A relationship diagram provided for another embodiment of the present application;
[0023] Figure 8A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0025] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0026] The method for identifying fish of the genus Schizothorax provided in the embodiments of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send otolith images of multiple Schizothorax fishes in different waters to the server 104. After the server 104 receives the otolith images of the multiple Schizothorax fishes in different waters, for the otolith images of the multiple Schizothorax fishes in different waters, the server 104 extracts the otolith contour from each otolith image; determines the elliptical Fourier coefficients and discrete wavelet coefficients of each otolith contour, and standardizes the elliptical Fourier coefficients and discrete wavelet coefficients of each otolith contour; reconstructs the otolith contour of each Schizothorax fishes according to the standardized elliptical Fourier coefficients and the standardized discrete wavelet coefficients; compares the otolith morphological characteristics of multiple Schizothorax fishes according to the reconstructed otolith contour of each Schizothorax fishes, and obtains the otolith morphological characteristics of each Schizothorax fishes that distinguishes it from other Schizothorax fishes; and identifies the Schizothorax fishes according to the otolith morphological characteristics of each Schizothorax fishes that distinguishes it from other Schizothorax fishes. The server 104 can feed back the obtained otolith morphological features of each Schizothorax fish that distinguish it from other Schizothorax fish to the terminal 102. In addition, in some embodiments, the method for identifying Schizothorax fish can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly identify the Schizothorax fish based on the otolith images of multiple Schizothorax fish in different waters, or the server 104 can obtain the otolith images of multiple Schizothorax fish in different waters from the data storage system, and identify the Schizothorax fish based on the otolith images of multiple Schizothorax fish in different waters.
[0027] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.
[0028] In an exemplary embodiment, Figure 2 As shown, a method for identifying fish of the genus Schizothorax is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 206. Among them:
[0029] Step 201: Obtain otolith images of various Schizothorax fish species in different waters.
[0030] Step 202: extracting otolith contours from each otolith image.
[0031] Step 203: Determine the elliptic Fourier coefficients and discrete wavelet coefficients of each otolith contour, and standardize the elliptic Fourier coefficients and discrete wavelet coefficients of each otolith contour.
[0032] Step 204: reconstruct the otolith profile of each Schizothorax fish species according to the normalized elliptic Fourier coefficients and the normalized discrete wavelet coefficients.
[0033] Step 205: comparing the otolith morphological characteristics of multiple Schizothorax fishes based on the otolith profile reconstructed for each Schizothorax fishes, and obtaining the otolith morphological characteristics of each Schizothorax fishes that are distinguishable from other Schizothorax fishes.
[0034] Step 206: Identify the Schizothorax fishes based on the otolith morphological characteristics of each Schizothorax fishes that distinguish it from other Schizothorax fishes.
[0035] Fourier transform and shape index are two commonly used methods for otolith morphology analysis, and wavelet transform is a commonly used signal processing method, which can not only analyze the fine features of otoliths, but also locate characteristic areas at the same time, but it has not been widely used in related research. In addition, traditional otolith morphology analysis focuses on the overall outline of otoliths, while there are fewer studies on the morphology of some characteristic structures on otoliths. In view of this, the above-mentioned steps 201 to 206 are implemented, and the present application analyzes the otolith morphological characteristics of various Schizothorax fishes in combination with wavelet transform, so as to provide a fast and efficient discrimination method for understanding three Schizothorax fishes, thereby providing possible technical support for accurately evaluating the resource situation and protection of three Schizothorax fishes.
[0036] In another exemplary embodiment of the present application, the above step 201 of obtaining the otolith image can be replaced by the following steps 301 to 303:
[0037] Step 301: Collect fish body samples of various Schizothorax fish species from different waters.
[0038] Step 302: Extract micro-otoliths from each fish sample.
[0039] Step 303: Position all micro-otoliths with the sulcus downward and rostral to the left, and capture otolith images of each micro-otolith at the same magnification using a stereo microscope with a sensor camera.
[0040] Taking three species of Schizothorax, namely, Tarim Schizothorax, Widemouth Schizothorax and Thicklip Schizothorax, as examples, the detailed process of otolith image acquisition is explained: three species of Schizothorax samples were collected from different waters; micro-otoliths were removed after being brought back to the laboratory, and otolith images were captured using a stereo microscope (SMZ 1270i) with a sensor camera. With the otolith groove facing downward and the beak facing left, the magnification was calibrated horizontally with the NIS Element software, so that all photos were in the same direction and magnification, and the microscope was adjusted so that the otoliths were as clear as possible against a black background. When the settings were ready, the images measured by calibration were used as size references, and then images were taken and stored to ensure good focus and high-resolution images in JPEG format.
[0041] In another exemplary embodiment of the present application, the characteristic parameters of otolith morphology include otolith contour variables, which are mathematical summary analysis of the two-dimensional projection of otoliths, such as Fourier coefficients obtained by fast Fourier transform and elliptical Fourier analysis. Then the elliptical Fourier coefficients and discrete wavelet coefficients in the above step 203 can be determined by the following process:
[0042] All otoliths are rotated along the longest horizontal axis of the otolith, and the areas are set to be all equal (area = 1). Polar coordinates are then collected by drawing a polar axis (radial) horizontally to the right from the otolith centroid (i.e., the average of the x- and y-coordinates of the contour), which corresponds to the 0° angle of the otolith contour. Starting from the 0° angle, radial lines are collected or drawn at equidistant angular intervals in a counterclockwise direction until an angle of 360° is reached, thereby dividing the entire circumference into several equal parts, such as Figure 3 shown.
[0043] Discrete wavelet coefficients were obtained using the functions wd and wr in the wavethresh package. For Fourier, the normalized elliptic Fourier technique was performed using the iefourier and efourier functions, both of which normalized the otoliths for size and rotation and collected the coefficients. The 10 wavelet levels gave 64 wavelet coefficients and the 12 Fourier harmonics gave 45 normalized elliptic Fourier coefficients.
[0044] To visually assess the variation in otolith shape among populations, the average otolith shape of each Schizothorax population was plotted using wavelet and Fourier coefficients, e.g. Figure 3 shown.
[0045] In another exemplary embodiment of the present application, in order to reflect the differences of the fish of the genus Schizothorax from the geometric shape and size of the otoliths, the geometric parameters of the otoliths can be further obtained, and a certain aspect of the shape of the otoliths can be characterized by performing a series of mathematical transformations on the geometric parameters of the otoliths, which is called a shape index. After the above step 202, the method can also include the following steps 401 to 406. Among them:
[0046] Step 401: Determine the morphometric parameters of each otolith contour; the morphometric parameters include length, width, perimeter and area.
[0047] Step 402: Based on the perimeter and area, use formula F f =4πA / P 2 , calculate the shape factor; where F f is the shape factor, A is the area, and P is the perimeter.
[0048] Step 403: Based on the length and area, use the formula R o =4A / πL 2 , calculate the roundness; where R o is the roundness and L is the length.
[0049] Step 404: Based on the perimeter and area, use the formula C = P 2 / A, calculate the circularity; where C is the circularity.
[0050] Step 405: Based on the length, width and area, use the formula R e =A / LW, calculate the rectangle approach rate; where R e is the rectangle approach rate, and W is the width.
[0051] Step 406: Calculate the ellipticity based on the length and width using the formula E=(LW) / (L+W); where E is the ellipticity.
[0052] The length and width mentioned above correspond to the maximum Feret diameter and the minimum Feret diameter, that is, the longest and shortest lengths of the otolith (and the auditory groove) measured by the caliper; while the circumference and area correspond to the image circumference and area in the general sense. Figure 4 shown.
[0053] In another exemplary embodiment of the present application, the process of obtaining geometric parameters, elliptic Fourier coefficients and discrete wavelet coefficients from an otolith image may be as follows: the otolith image is read into R language. Through the ShapeR package running on the R language, the contour is extracted from the digital image of the otolith, the contour is smoothed, and the pixel noise is eliminated to analyze the shape of the otolith. After all contours are captured, different parameters can be obtained after analysis, including: geometric parameters, elliptic Fourier coefficients and discrete wavelet coefficients, etc.
[0054] In another exemplary embodiment of the present application, in order to eliminate or reduce the influence of the allometric relationships of the length of the fish on the otolith shape analysis, it is necessary to adjust the shape coefficient (wavelet coefficient and Fourier coefficient). Allometric growth refers to the phenomenon of disproportionate growth rate between different parts of an organism. The ShapeR package in the R language can be used to standardize the elliptic Fourier coefficient and the discrete wavelet coefficient of each otolith profile in the above-mentioned step 203. The working principle of the ShapeR package in the R language includes:
[0055] Coefficient evaluation:
[0056] Each wavelet coefficient and Fourier coefficient were evaluated using the stdCoefs function in the ShapeR package in R. These coefficients are key parameters describing the shape of otoliths.
[0057] Interaction detection:
[0058] Check which coefficients have a significant interaction between species and length (i.e., P value < 0.05). This means that the values of these coefficients are not only affected by species, but also significantly affected by fish length.
[0059] Interaction coefficients are automatically omitted:
[0060] For those coefficients showing significant interactions, they were automatically omitted from the analysis without any adjustment, because these coefficients may distort the true interpretation of population differences or changes in shape traits.
[0061] Bonferroni adjustment:
[0062] To reduce the increase in Type I errors (i.e., false positives) due to multiple testing of different coefficients, the Bonferroni adjustment can be used. This adjustment ensures that the false positive rate of the overall test remains within an acceptable range by increasing the significance level of each test (i.e., reducing the threshold for the P value).
[0063] Apply the Bonferroni adjustment:
[0064] After applying the Bonferroni adjustment, the three wavelet coefficients that were originally omitted because of their significant interactions with length are now included. This is because the Bonferroni adjustment relaxes the significance criteria for each individual test, allowing more coefficients to be included in the analysis.
[0065] Results Impact:
[0066] Although these three wavelet coefficients are of particular interest because of their interactions with length, their inclusion did not significantly change the results of the overall analysis, suggesting that the overall impact of these coefficients on the shape analysis is limited after accounting for the allometric relationship between population and length.
[0067] In general, this section shows how to account for allometric relationships when processing shape data, how to detect interactions between coefficients, and how to optimize the results through statistical adjustments (Bonferroni adjustments). Ultimately, this adjustment method helps to more accurately explain the relationship between shape variation and fish growth and population differences.
[0068] Standardization can make the measurements from individuals of different body lengths comparable, eliminating the bias caused by differences in body length between individuals. When used for statistical analysis and modeling, it can improve the accuracy and reliability of the analysis results.
[0069] In another exemplary embodiment of the present application, after reconstructing the otolith outline in step 204, the difference between the reconstructed otolith outline and the original otolith outline is used to evaluate the quality of wavelet and Fourier reconstruction. This can be achieved by the estimate.outline.reconstruction function in the ShapeR package of the R language, which estimates the reconstruction quality of the reconstructed otolith outline and returns a list est.list containing the evaluation results. Then, using the outline.reconstruction.plot function and the est.list list, a graph is drawn showing the reconstruction quality changing with the number of Wavelet / Fourier coefficients used, such as Figure 5 and Figure 6 As shown. Figure 5 and Figure 6 As can be seen in Figure 2, the reconstruction quality improves as the number of wavelet / Fourier coefficients used increases.
[0070] In another exemplary embodiment of the present application, after reconstructing the otolith contour in step 204, in order to study which areas and coefficients on the otolith contour contribute most to the change in shape, Fourier or wavelet transform is used to plot the relationship between the average shape coefficient of all otolith contours and their standard deviation and the contour angle through the plotCI command in the gplots package, and the proportional change of the total change (intra-group correlation ICC) between populations is calculated along the otolith contour, such as Figure 7 After the above step 204, the method may further include the following steps 501 to 505. Among them:
[0071] Step 501: along the otolith contour, respectively calculate the distribution degree values of all standardized elliptic Fourier coefficients and all standardized discrete wavelet coefficients at each contour angle of the otolith contour; the distribution degree value is the mean value and / or the standard deviation.
[0072] Step 502: along the otolith contour, calculate the intraclass correlation coefficient of all otolith contours at each contour angle of the otolith contour.
[0073] Step 503: Draw a relationship diagram by taking the contour angle of the otolith contour as the horizontal axis, and the distribution degree value and the intraclass correlation coefficient as the two vertical axes.
[0074] Step 504: Filter the angle regions whose intra-group correlation coefficients are greater than the coefficient threshold from the relationship graph, and determine them as regions of interest for differences in otolith contour shapes.
[0075] Step 505: Filter the standardized elliptic Fourier coefficient or the standardized discrete wavelet coefficient with the largest distribution value from the region of interest as the parameter of interest of the otolith contour shape difference.
[0076] The intraclass correlation coefficient (ICC) is a statistic used to quantify the degree of variation between different groups (in this case, different regions on the otolith profile or different otolith samples). By analyzing the ICC, we can further understand the distribution of variation on the profile. Figure 7 , it is also possible to identify which parts of the otolith contour vary most significantly in shape coefficient.
[0077] In another exemplary embodiment of the present application, in order to identify different Schizothorax fish and evaluate the changes in otolith shape among Schizothorax fish populations, the specific implementation method of the above-mentioned step 205 is: based on the reconstructed otolith contour of each Schizothorax fish, a variance analysis permutation test is used to compare the standardized discrete wavelet coefficients of the Schizothorax fish, and the standardized discrete wavelet coefficients of each Schizothorax fish are obtained to distinguish it from other Schizothorax fish, and the standardized discrete wavelet coefficients are used as the otolith morphological characteristics of each Schizothorax fish to distinguish it from other Schizothorax fish.
[0078] In another exemplary embodiment of the present application, the above step 205 can also use the "vegan" package to evaluate the variance analysis (quasi-ANOVA) permutation test of the constrained correspondence analysis, convert the standardized wavelet coefficients into principal coordinates and perform canonical analysis (CAP) to compare the standardized wavelet coefficients of different populations.
[0079] Based on the same inventive concept, the embodiment of the present application also provides a device for identifying fish of the genus Schizothorax for implementing the above-mentioned method for identifying fish of the genus Schizothorax. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiment of the device for identifying fish of the genus Schizothorax provided below can refer to the limitations of the method for identifying fish of the genus Schizothorax above, and will not be repeated here.
[0080] In an exemplary embodiment, a device for identifying fish of the genus Schizothorax is provided, comprising: an image acquisition module, a contour extraction module, a standardization module, a screening module, a reconstruction module, a comparison module and an identification module.
[0081] An image acquisition module is used to acquire otolith images of various schizothorax fishes in different waters. A contour extraction module is used to extract otolith contours from each otolith image. A standardization module is used to determine the elliptic Fourier coefficients and discrete wavelet coefficients of each otolith contour, and to standardize the elliptic Fourier coefficients and discrete wavelet coefficients of each otolith contour. A screening module is used to calculate the significant coefficients of the standardized elliptic Fourier coefficients and the standardized discrete wavelet coefficients with the body length of the schizothorax fishes, respectively, and to screen the standardized elliptic Fourier coefficients and the standardized discrete wavelet coefficients whose significant coefficients are greater than or equal to a preset threshold. A reconstruction module is used to reconstruct the otolith contour of each schizothorax fishes according to the standardized elliptic Fourier coefficients and the standardized discrete wavelet coefficients. A comparison module is used to compare the otolith morphological characteristics of various schizothorax fishes according to the otolith contours reconstructed by each schizothorax fishes, and to obtain the otolith morphological characteristics of each schizothorax fishes that are different from other schizothorax fishes. The identification module is used to identify the fish of the genus Schizothorax based on the otolith morphological characteristics of each fish of the genus Schizothorax that distinguishes it from other fish of the genus Schizothorax.
[0082] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the otolith morphological characteristics of each Schizothorax fish that distinguishes it from other Schizothorax fish. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for identifying Schizothorax fish is implemented.
[0083] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0084] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0085] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0086] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.
[0087] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0088] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.
[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0090] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for identifying fish of the genus Schizothorax, characterized in that: The method for identifying fish of the genus Schizothorax comprises: Obtain otolith images of various Schizothorax species from different water bodies; Extract otolith contours from each otolith image; Determine the elliptic Fourier coefficient and discrete wavelet coefficient of each otolith contour, and standardize the elliptic Fourier coefficient and discrete wavelet coefficient of each otolith contour; The otolith profiles of each Schizothorax species were reconstructed based on the normalized elliptic Fourier coefficients and normalized discrete wavelet coefficients; Based on the reconstructed otolith profiles of each genus Schizothorax, the otolith morphological characteristics of various genus Schizothorax were compared to obtain the otolith morphological characteristics of each genus Schizothorax that distinguish it from other genus Schizothorax. Identify Schizothorax species based on the otolith morphology of each species that distinguishes it from other Schizothorax species.
2. The method for identifying fish of the genus Schizothorax according to claim 1, characterized in that: Obtain otolith images of various Schizothorax species from different waters, including: Collect fish samples of various species of Schizothorax from different waters; Micro-otoliths were extracted from each fish sample; All micro-otoliths were positioned with the sulcus downward and rostral to the left, and otolith images of each micro-otolith were captured at the same magnification using a stereo microscope with a sensor camera.
3. The method for identifying fish of the genus Schizothorax according to claim 1, characterized in that: Extract otolith outlines from each otolith image, followed by: Determining morphometric parameters of each otolith outline; the morphometric parameters include length, width, perimeter and area; According to the perimeter and area, use the formula F f =4πA / P 2 , calculate the shape factor; where F f is the shape factor, A is the area, and P is the perimeter; According to the length and area, use the formula R o =4A / πL 2 , calculate the roundness; where R o is the roundness, L is the length; According to the perimeter and area, use the formula C = P 2 / A, calculate the circularity; where C is the circularity; According to the length, width and area, use the formula R e =A / LW, calculate the rectangle approach rate; where R e is the rectangle approach rate, W is the width; Based on the length and width, the ellipticity is calculated using the formula E=(LW) / (L+W); where E is the ellipticity.
4. The method for identifying fish of the genus Schizothorax according to claim 1, characterized in that: The otolith profiles of each Schizothorax species were reconstructed from the normalized elliptic Fourier coefficients and the normalized discrete wavelet coefficients, followed by: Along the otolith contour, respectively calculating the distribution degree values of all standardized elliptic Fourier coefficients and all standardized discrete wavelet coefficients at each contour angle of the otolith contour; the distribution degree value is the mean value and / or the standard deviation; Along the otolith contour, the intraclass correlation coefficient of all otolith contours at each contour angle of the otolith contour was calculated; The contour angle of the otolith contour is used as the horizontal axis, and the distribution degree value and the intraclass correlation coefficient are used as the two vertical axes to draw a relationship diagram; Filtering the angle region where the intraclass correlation coefficient is greater than the coefficient threshold from the relationship diagram to determine it as the region of interest for the difference in otolith contour shape; The standardized elliptic Fourier coefficient or the standardized discrete wavelet coefficient with the largest distribution value is selected from the region of interest as the parameter of interest of the difference in the shape of the otolith contour.
5. The method for identifying fish of the genus Schizothorax according to claim 1, characterized in that: Based on the reconstructed otolith profiles of each genus Schizothorax, the otolith morphological characteristics of various genus Schizothorax were compared to obtain the otolith morphological characteristics of each genus Schizothorax that are different from other genus Schizothorax, including: Based on the reconstructed otolith profiles of each Schizothorax species, the standardized discrete wavelet coefficients of the Schizothorax species were compared using the variance analysis permutation test to obtain the standardized discrete wavelet coefficients of each Schizothorax species that distinguished it from other Schizothorax species, and used them as the otolith morphological characteristics that distinguished each Schizothorax species from other Schizothorax species.
6. A device for identifying fish of the genus Schizothorax, characterized in that: The device for identifying fish of the genus Schizothorax comprises: An image acquisition module, used for acquiring otolith images of various Schizothorax fishes in different waters; A contour extraction module, used for extracting otolith contours from each otolith image; A standardization module, used for determining the elliptic Fourier coefficient and the discrete wavelet coefficient of each otolith contour, and standardizing the elliptic Fourier coefficient and the discrete wavelet coefficient of each otolith contour; a reconstruction module for reconstructing the otolith profile of each Schizothorax fish species based on normalized elliptic Fourier coefficients and normalized discrete wavelet coefficients; A comparison module is used to compare the otolith morphological characteristics of multiple Schizothorax fishes according to the otolith profile reconstructed by each Schizothorax fishes, so as to obtain the otolith morphological characteristics of each Schizothorax fishes that are different from other Schizothorax fishes; The identification module is used to identify the fish of the genus Schizothorax based on the otolith morphological characteristics of each fish of the genus Schizothorax that distinguishes it from other fish of the genus Schizothorax.
7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for identifying fish of the genus Schizothorax according to any one of claims 1 to 5.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying fish of the genus Schizothorax described in any one of claims 1 to 5 is implemented.
9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for identifying fish of the genus Schizothorax described in any one of claims 1 to 5 is implemented.