Flounder fish growth discrimination system and method

Abstract

The invention relates to the technical field of aquaculture. The invention discloses a flounder fish growth discrimination system and method, and the method comprises the steps: employing a high-definition camera to carry out the multi-angle shooting of a cultured fish body, obtaining a non-shielding, non-overlapping and high-resolution cultured fish body image, building a YOLOv8 segmentation model, building a recognition module, enabling the recognition module to obtain a measurable parameter and a frame parameter based on a generated mask of the fish body, and carrying out the recognition of the measurable parameter and the frame parameter. After data correction is completed, variety analysis and identification are completed, variety information is input into the YOLOv8 model, and the growth condition of cultured fish bodies is judged according to the final growth conditions of the body thickness, the body length and the body width. According to the method, cultured fish body image segmentation is completed by establishing the YOLOv8 segmentation model, and after measurable parameters and frame parameters are obtained, automatic generation and discrimination are completed according to body thickness derivation results of different varieties, so that the efficiency and precision of culture management are improved, and the method is suitable for large-scale popularization and application. Powerful data support is provided for scientific feeding, feed optimization, growth regulation and control, disease early warning and the like in the breeding process.

Description

Technical Field

[0001] The invention relates to the technical field of aquaculture, and in particular to a system and method for distinguishing the growth of flounder. Background Art

[0002] With the advancement of science and technology, the aquaculture industry has gradually transitioned from traditional aquaculture methods to diversified large-scale and intensive aquaculture models such as ponds, factories, cages, fences, and work boats. These large-scale aquaculture integrates modern technologies such as civil engineering, mechanical electronics, instrumentation, chemistry, bioengineering, automatic control, and socioeconomics. The water quality, water flow, water temperature, feeding, sewage discharge, disease prevention, water treatment, and recycling of the entire aquaculture production process are semi-automatically or fully automatically monitored, so that it can maintain suitable physiological and ecological conditions from beginning to end under high-density breeding conditions without causing internal and external environmental pollution, thereby achieving the health and rapid growth of the aquaculture objects and improving the unit water output and quality. With the gradual development of factory aquaculture technology, it can solve many problems existing in traditional aquaculture.

[0003] Among them, in the process of fish farming, it is impossible to accurately feed the fish because the survival rate of the fish cannot be determined. However, in order to improve the efficiency of farming, farmers often feed excessive bait, which causes waste of bait and causes diseases such as fish enteritis. At the same time, excessive bait is deposited to the bottom of the water, and a large amount of harmful substances are produced through fermentation, which leads to deterioration of water quality and causes other fish diseases. The determination of growth performance can not only achieve accurate feeding and nutritional management, but also determine the health status of fish through growth performance, which is of great significance for disease warning. In addition, through in-depth analysis of farming data, it can also help farmers optimize production processes, improve product quality, and thus enhance market competitiveness. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a system and method for determining the growth of flatfish, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a system and method for distinguishing the growth of flatfish, comprising the following specific steps:

[0006] S1: Use a high-definition camera to shoot farmed fish at multiple angles to obtain multiple groups of unobstructed, non-overlapping, high-resolution farmed fish images, perform motion judgment on farmed fish images in adjacent frames, shoot the farmed fish without motion in the next cycle, and judge the farmed fish without obvious motion for five consecutive shooting cycles as dead fish;

[0007] S2: Establish a YOLOv8 segmentation model, the YOLOv8 segmentation model completes segmentation based on the farmed fish image, generates a mask of the fish body, and the YOLOv8 segmentation model completes the calculation of the body length d and body width p of the farmed fish body based on the mask;

[0008] S3: Establishing an identification module, the identification module obtains measurable parameters and framework parameters based on the mask of the generated fish body, and completes species analysis and identification through DataRobot software after completing data correction;

[0009] S4: Input the species information into the YOLOv8 model. The YOLOv8 segmentation model derives the body thickness h according to the body width p, body length d, and species, and determines the growth of the farmed fish according to the final growth of the body thickness h, body length d, and body width p.

[0010] A further improvement of the technical solution of the present invention is that: the YOLOv8 segmentation model completes mask detection based on contour detection technology, obtains the head and coordinate points of the farmed fish, and calculates the body length of the fish based on the distance formula;

[0011]

[0012] In the formula, x head ,y head and x tail ,y tail They are the coordinates of the head and tail of the farmed fish obtained based on the contour detection technology;

[0013] The YOLOv8 segmentation model completes mask detection based on contour detection technology, identifies the left and right edge coordinate points of the widest part of the fish body, and calculates the vertical distance between the left and right edges as the body width p.

[0014] A further improvement of the technical solution of the present invention is that: the recognition module selects the recognition feature coordinate points based on the coordinates obtained by the contour detection technology, and completes the variety analysis and recognition in combination with the measurable parameters and the framework parameters;

[0015] The identification feature coordinate points include the coordinate point of the front end of the snout of the farmed fish, the coordinate point of the first dorsal fin, the coordinate point of the starting point of the pelvic fin, the coordinate point of the second dorsal fin, the coordinate point of the starting point of the anal fin, the coordinate point of the starting point of the fifth upper adipose fin, the coordinate point of the starting point of the fifth lower adipose fin, and the coordinate point of the last tail vertebra.

[0016] A further improvement of the technical solution of the present invention is that the measurable parameters in the identification module include snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d.

[0017] A further improvement of the technical solution of the present invention is that the framework parameters include the length of the line between the front end coordinate point of the snout and the starting point coordinate point of the first dorsal fin, the line between the front end coordinate point of the snout and the starting point coordinate point of the pelvic fin, the line between the first dorsal fin and the starting point coordinate point of the pelvic fin, the line between the first dorsal fin and the starting point coordinate point of the second dorsal fin, the line between the starting point coordinate point of the pelvic fin and the starting point coordinate point of the anal fin, the line between the starting point coordinate point of the second dorsal fin and the starting point coordinate point of the fifth upper adipose fin, the line between the starting point coordinate point of the anal fin and the starting point coordinate point of the fifth lower adipose fin, the line between the starting point of the fifth upper adipose fin and the coordinate point of the last tail vertebra, and the line between the starting point coordinate point of the fifth lower adipose fin and the coordinate point of the last tail vertebra.

[0018] In the above, the recognition module obtains the measurable parameters and the frame parameters based on the mask of the generated fish body, and completes the species analysis and recognition through the DataRobot software after completing the data correction, wherein the data correction specifically includes the correction formula;

[0019] e=exp(lnY-b(lnX-lnX L ));

[0020] Where e is the correction value, Y is the original measured value of the measurable parameters snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d, X is the length of the line connecting the coordinate point of the front end of the snout and the coordinate point of the starting point of the first dorsal fin, and X is the length of the line connecting the coordinate point of the front end of the snout and the coordinate point of the starting point of the first dorsal fin. L is the average value of the lengths of each line, b is the slope of lnY to lnX, and the corrected parameters of snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d are compared with the parameters of snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d of various species of flatfish through software to achieve species comparison and discrimination.

[0021] A further improvement of the technical solution of the present invention is that the body thickness h in S4 is derived from an empirical formula, wherein the empirical formula is:

[0022] Body thickness h = k1 × body length d + k2 × body width p + k3;

[0023] Wherein, body length d and body width p are calculated based on the distance between coordinate points obtained by contour detection technology, k1 and k2 are variety variable coefficients derived from a large number of manual measurements, and k3 is the error compensation coefficient.

[0024] Taking turbot as an example, the empirical formula is body thickness h = 0.055 × body length d + 0.019 × body width p + 0.686.

[0025] A further improvement of the technical solution of the present invention is that a flatfish growth identification system is also provided, comprising a model training module, an image acquisition module, a variety identification module, and an intelligent analysis module;

[0026] The model training module trains the YOLOv8 model based on the prepared training set and makes adjustments based on the results of cross-validation;

[0027] The image acquisition module shoots the fish body at multiple angles based on a high-definition camera;

[0028] The variety identification module performs variety identification based on measurable parameters and framework parameters;

[0029] The intelligent analysis module determines the growth status based on body width, body length, and body thickness.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: by establishing a YOLOv8 segmentation model, the image segmentation of farmed fish is completed, and a mask of the fish body is generated. The YOLOv8 segmentation model completes high-precision measurement of the length and width of the farmed fish body based on the mask, with small and stable errors. After obtaining measurable parameters and framework parameters, data correction is completed to ensure accuracy while realizing variety analysis. Automatic generation and discrimination are completed according to the derivation results of body thickness of different varieties, which can better reflect the actual growth condition of the fish body and solve the problem of cumbersome measurement and identification processes in traditional farming. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure is a flow chart of the method for determining the growth of flatfish;

[0032] Figure 2 It is a schematic diagram of the construction of fish body framework parameters in the method of fish growth identification of flatfish;

[0033] Figure 3 It is a flow chart of the growth identification system of flatfish;

[0034] Figure 4 This is a table of differences between the automated measurement results and the manual measurement results in Example 3. DETAILED DESCRIPTION

[0035] Various exemplary embodiments, features and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0036] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0037] In addition, in order to better illustrate the present application, numerous specific details are provided in the specific examples below. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In some examples, methods, means, and elements well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present application.

[0038] Embodiment 1, the present invention provides a flatfish growth identification system and method, comprising the following specific steps:

[0039] S1: Use a high-definition camera to shoot farmed fish at multiple angles to obtain multiple groups of unobstructed, non-overlapping, high-resolution farmed fish images, perform motion judgment on farmed fish images in adjacent frames, shoot the farmed fish without motion in the next cycle, and judge the farmed fish without obvious motion for five consecutive shooting cycles as dead fish;

[0040] S2: Establish a YOLOv8 segmentation model, the YOLOv8 segmentation model completes segmentation based on the farmed fish image, generates a mask of the fish body, and the YOLOv8 segmentation model completes the calculation of the body length d and body width p of the farmed fish body based on the mask;

[0041] S3: Establishing an identification module, the identification module obtains measurable parameters and framework parameters based on the mask of the generated fish body, and completes species analysis and identification through DataRobot software after completing data correction;

[0042] S4: Input the species information into the YOLOv8 model. The YOLOv8 segmentation model derives the body thickness h according to the body width p, body length d, and species, and determines the growth of the farmed fish according to the final growth of the body thickness h, body length d, and body width p.

[0043] The YOLOv8 segmentation model extracts image features to complete the farmed fish image segmentation, and then uses a median filtering algorithm to eliminate the farmed fish image background and interference, and retains the outline of the farmed fish to obtain a fish mask.

[0044] The YOLOv8 segmentation model completes mask detection based on contour detection technology, obtains the head and coordinate points of the farmed fish, and calculates the body length of the fish based on the distance formula;

[0045]

[0046] In the formula, x head ,y head and x tail ,y tail They are the coordinates of the head and tail of the farmed fish obtained based on the contour detection technology;

[0047] The YOLOv8 segmentation model completes mask detection based on contour detection technology, identifies the left and right edge coordinate points of the widest part of the fish body, and calculates the vertical distance between the left and right edges as the body width p.

[0048] The recognition module selects the recognition feature coordinate points based on the coordinates obtained by the contour detection technology, and completes the variety analysis and recognition in combination with the measurable parameters and the framework parameters;

[0049] The identification feature coordinate points include the coordinate point of the front end of the snout of the farmed fish, the coordinate point of the first dorsal fin, the coordinate point of the starting point of the pelvic fin, the coordinate point of the second dorsal fin, the coordinate point of the starting point of the anal fin, the coordinate point of the starting point of the fifth upper adipose fin, the coordinate point of the starting point of the fifth lower adipose fin, and the coordinate point of the last tail vertebra.

[0050] The measurable parameters in the identification module include snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d.

[0051] The framework parameters include the length of the line between the front end coordinate point of the snout and the starting point coordinate point of the first dorsal fin, the line between the front end coordinate point of the snout and the starting point coordinate point of the pelvic fin, the line between the first dorsal fin and the starting point coordinate point of the pelvic fin, the line between the first dorsal fin and the starting point coordinate point of the second dorsal fin, the line between the starting point coordinate point of the pelvic fin and the starting point coordinate point of the anal fin, the line between the starting point coordinate point of the second dorsal fin and the starting point coordinate point of the fifth upper adipose fin, the line between the starting point coordinate point of the anal fin and the starting point coordinate point of the fifth lower adipose fin, the line between the starting point of the fifth upper adipose fin and the last tail vertebrae, and the line between the starting point coordinate point of the fifth lower adipose fin and the last tail vertebrae. Figure 2 As shown;

[0052] In the above, the recognition module obtains the measurable parameters and the frame parameters based on the mask of the generated fish body, and completes the species analysis and recognition through the DataRobot software after completing the data correction, wherein the data correction specifically includes the correction formula;

[0053] e=exp(lnY-b(lnX-lnX L ));

[0054] Where e is the correction value, Y is the original measured value of the measurable parameters snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d, X is the length of the line connecting the coordinate point of the front end of the snout and the coordinate point of the starting point of the first dorsal fin, and X is the length of the line connecting the coordinate point of the front end of the snout and the coordinate point of the starting point of the first dorsal fin. L is the average value of the lengths of each line, b is the slope of lnY to lnX, and the corrected parameters of snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d are compared with the parameters of snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d of various species of flatfish through software to achieve species comparison and discrimination.

[0055] The body thickness h in S4 is derived from an empirical formula, wherein the empirical formula is:

[0056] Body thickness h = k1 × body length d + k2 × body width p + k3;

[0057] Wherein, body length d and body width p are calculated based on the distance between coordinate points obtained by contour detection technology, k1 and k2 are variety variable coefficients derived from a large number of manual measurements, and k3 is the error compensation coefficient.

[0058] Taking turbot as an example, the empirical formula is body thickness h = 0.055 × body length d + 0.019 × body width p + 0.686.

[0059] Embodiment 2: The present invention also provides a flatfish growth identification system applicable to the above method, comprising a model training module, an image acquisition module, a variety identification module, and an intelligent analysis module;

[0060] The model training module trains the YOLOv8 model based on the prepared training set and makes adjustments based on the results of cross-validation;

[0061] The image acquisition module shoots the fish body at multiple angles based on a high-definition camera;

[0062] The variety identification module performs variety identification based on measurable parameters and framework parameters;

[0063] The intelligent analysis module determines the growth status based on body width, body length, and body thickness.

[0064] Example 3: Based on the above disclosed system and method, turbot is generated and identified. The object of the example is flounder from an experimental farm, covering samples at different growth stages. According to the actual breeding conditions, the body length of the fish in the example fluctuates between 10 and 20 cm, and the body width data varies between 8 and 15 cm. The sample can better represent flounder of different body shapes and growth stages, and the results of the example can also more comprehensively reflect the applicability and performance of the model.

[0065] Shooting equipment: Use a high-definition camera with a resolution of 1920x1080 to capture high-resolution images of farmed fish. Shoot from multiple angles to ensure a clear outline of the fish in the image and avoid occlusion and overlap.

[0066] Computing equipment: The computing tasks required for the implementation example are performed on a high-performance computing workstation equipped with an NVID IA RTX 3080 GPU accelerator card to support real-time segmentation and detection of the YOLOv8 model. The workstation is also equipped with an Intel i5 processor and 64GB of memory to ensure computing stability when processing large batches of samples;

[0067] Manual measurement tools: To verify the accuracy of the automated measurements, manual measurements were performed using a traditional measuring ruler and electronic scale. The length, width, and thickness of each fish were measured manually and used as a comparison standard for the YOLOv8 model measurements.

[0068] Operating system: Ubuntu 22.04 LTS, a stable operating environment that supports the running of the YOLOv8 model and its related tools; YOLOv8 version: YOLOv8v1.0, this embodiment uses this version of the model to perform fish segmentation and detection tasks; Python version: Python 3.10 is the main programming language of this embodiment, integrating the YOLOv8 model and related data processing tools; Deep learning framework: PyTorch 1.11 is the main framework for model training and reasoning, which can efficiently support the YOLOv8 model to run on a high-performance GPU; auxiliary tools include OpenCV, NumPy, and Matplotlib, etc. These tools are mainly used for image processing, data calculation, and visualization of results.

[0069] In this embodiment, data processing is a key link in model evaluation and is directly related to the accuracy and stability of the YOLOv8 model measurement results. The processing process includes data sorting, error calculation, and error source analysis. First, the body length, body width, and body thickness data measured by the YOLOv8 model are sorted and compared with the manually measured data. The manually measured data is used as the "true value" to evaluate the accuracy of the model prediction results.

[0070] Then, the manual measurement results were compared with the model prediction data, and the errors of body length, body width and body thickness were calculated one by one. The error is defined as the difference between the model prediction value and the actual measurement value, and the calculation formula is as follows:

[0071] Error = |Model measurement value - manual measurement value |

[0072] By calculating the error of each sample, we can accurately evaluate the performance of the model in various samples. At the same time, data processing will also analyze the distribution of errors, find samples with large errors, and explore possible sources of errors, such as image quality, fish posture, model generalization ability, etc. This analysis process helps to clarify the advantages and disadvantages of model performance and provide a basis for subsequent optimization;

[0073] The specific steps are as follows:

[0074] Data preparation: First, use a high-definition camera to shoot the fish from multiple angles to ensure that the complete outline and different postures of the fish are captured. At the same time, the length, width, and thickness of the fish are obtained by manual measurement and recorded as the real measurement values. These manually annotated measurement data will be used to train and test the YOLOv8 model.

[0075] YOLOv8 model training: Use the prepared training set to train the YOLOv8 model. During the training process, the model gradually improves its segmentation and measurement accuracy by learning the edges, contours, and features of the fish body. During training, the model's hyperparameters (such as learning rate, training rounds, etc.) will be adjusted according to the results of cross-validation to ensure the optimal performance of the model.

[0076] Model prediction and measurement: After the model training is completed, it is verified using the test set. In this process, the YOLOv8 model automatically identifies the fish body contour in the test image and calculates the body length and width of the fish. Based on these measurement results, the snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width, and body length parameters of various species of flounder are compared through software to achieve species comparison and discrimination, and the body thickness of the fish species is derived using empirical formulas to generate complete prediction data.

[0077] Results analysis: Finally, the length, width and thickness predicted by the model were compared in detail with the manually measured values, the errors were calculated and the overall performance of the model was analyzed. The stability and generalization ability of the model were evaluated through the distribution and trend of the errors, and its performance on fish of different sizes was summarized to determine whether the model needed to be further optimized or adjusted.

[0078] The results are attached Figure 4 As shown, in this embodiment, the body length and width of the flatfish body fluctuate within the range of 10 to 20 cm. The following are the measurement results of the body length, body width and body thickness of some samples, including the results measured by the YOLOv8 segmentation model and the real data measured manually, and the error between the body thickness derivation result and the real value is shown. The average error is: Overall, the average error of the model in body thickness prediction is 0.52 cm. This shows that there is a small deviation between most of the measurement results and the actual measurement values, and the deviation is within an acceptable range.

[0079] Maximum error: The maximum error occurs on fish No. 2, with an error of 0.9 cm. The length and width of this sample are relatively small, and the model may have certain prediction biases on small fish. Due to the small size of the fish, the details captured in the image are relatively reduced, which may lead to amplified errors in segmentation and measurement.

[0080] Minimum error: The minimum error is 0.2 cm, which occurs on the fish No. 1. This shows that the model's prediction is more accurate and stable on medium-sized fish, and the derived body thickness value is highly consistent with the manual measurement value.

[0081] Error range: The overall error range is between 0.2cm and 0.9cm, and all errors are controlled within 1cm, which shows that the measurement results of the YOLOv8 model have a certain degree of reliability. The error of most samples is around 0.5cm, which meets the accuracy requirements of automated measurement.

[0082] The experimental fish used in this embodiment are all turbot, and the accuracy rate in species identification is 100%. The body thickness derivation formula used in this test is: body thickness = 0.055 × body length + 0.019 × body width + 0.686

[0083] The formula is based on a linear regression model and is obtained by fitting historical data. It aims to deduce the body thickness of fish from the measurements of body length and width.

[0084] According to the current data analysis results, the overall performance of the model in the test is acceptable. For medium fish size, the prediction error is small and the performance is good; however, for extremely small or extremely large fish, the error is large, and further optimization of the model may be needed to improve its applicability. The error is controlled within 1 cm, indicating that the model has a certain degree of accuracy and stability.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for determining the growth of flatfish, characterized in that: The specific steps include: S1: Use a high-definition camera to shoot farmed fish at multiple angles to obtain multiple groups of unobstructed, non-overlapping, high-resolution farmed fish images, perform motion judgment on farmed fish images in adjacent frames, shoot the farmed fish without motion in the next cycle, and judge the farmed fish without obvious motion for five consecutive shooting cycles as dead fish; S2: Establish a YOLOv8 segmentation model, the YOLOv8 segmentation model completes segmentation based on the farmed fish image, generates a mask of the fish body, and the YOLOv8 segmentation model completes the calculation of the body length d and body width p of the farmed fish body based on the mask; S3: Establishing an identification module, the identification module obtains measurable parameters and framework parameters based on the mask of the generated fish body, and completes species analysis and identification through DataRobot software after completing data correction; S4: Input the species information into the YOLOv8 model. The YOLOv8 segmentation model derives the body thickness h according to the body width p, body length d, and species, and determines the growth of the farmed fish according to the final body thickness h, body length d, and body width p.

2. A method for determining the growth of flatfish according to claim 1, characterized in that: The YOLOv8 segmentation model extracts image features to complete the farmed fish image segmentation, and then uses a median filtering algorithm to eliminate the farmed fish image background and interference, and retains the outline of the farmed fish to obtain a fish mask.

3. A method for determining the growth of flatfish according to claim 1, characterized in that: The YOLOv8 segmentation model completes mask detection based on contour detection technology, obtains the head and coordinate points of the farmed fish, and calculates the body length of the fish based on the distance formula; In the formula, x head ,y head and x tail ,y tail They are the coordinates of the head and tail of the farmed fish obtained based on the contour detection technology; The YOLOv8 segmentation model completes mask detection based on contour detection technology, identifies the left and right edge coordinate points of the widest part of the fish body, and calculates the vertical distance between the left and right edges as the body width p.

4. The method for determining the growth of flatfish according to claim 1, wherein: The recognition module selects the recognition feature coordinate points based on the coordinates obtained by the contour detection technology, and completes the variety analysis and recognition in combination with the measurable parameters and the framework parameters; The identification feature coordinate points include the coordinate point of the front end of the snout of the farmed fish, the coordinate point of the first dorsal fin, the coordinate point of the starting point of the pelvic fin, the coordinate point of the second dorsal fin, the coordinate point of the starting point of the anal fin, the coordinate point of the starting point of the fifth upper adipose fin, the coordinate point of the starting point of the fifth lower adipose fin, and the coordinate point of the last tail vertebra.

5. The method for determining the growth of flatfish according to claim 1, characterized in that: The measurable parameters in the identification module include snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d.

6. The method for determining the growth of flatfish according to claim 1, characterized in that: The framework parameters include the length of the line connecting the front end coordinate point of the snout and the starting point coordinate point of the first dorsal fin, the line connecting the front end coordinate point of the snout and the starting point coordinate point of the pelvic fin, the line connecting the first dorsal fin and the starting point coordinate point of the pelvic fin, the line connecting the first dorsal fin and the starting point coordinate point of the second dorsal fin, the line connecting the starting point coordinate point of the pelvic fin and the starting point coordinate point of the anal fin, the line connecting the starting point coordinate point of the second dorsal fin and the starting point coordinate point of the fifth upper adipose fin, the line connecting the starting point coordinate point of the anal fin and the starting point coordinate point of the fifth lower adipose fin, the line connecting the starting point of the fifth upper adipose fin and the coordinate point of the last tail vertebra, and the line connecting the starting point coordinate point of the fifth lower adipose fin and the coordinate point of the last tail vertebra. The correction formula used for data correction in S3 is: e=exp(lnY-b(lnX-lnX L )); Where e is the correction value, Y is the original measured value of the measurable parameters snout length, eye diameter, eye distance, tail peduncle length, tail peduncle height, body width p, and body length d, X is the length of the line between the coordinate points, and X is the length of the line between the coordinate points. L is the average value of the lengths of each line, b is the slope of lnY to lnX, and the corrected parameters of snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d are compared with the parameters of snout length, eye diameter, eye distance, caudal peduncle length, caudal peduncle height, body width p, and body length d of various species of flatfish through software to achieve species comparison and discrimination.

7. The method for determining the growth of flatfish according to claim 1, characterized in that: The body thickness h in S4 is derived from an empirical formula, wherein the empirical formula is: Body thickness h = k1 × body length d + k2 × body width p + k3; Wherein, body length d and body width p are calculated based on the distance between coordinate points obtained by contour detection technology, k1 and k2 are variety variable coefficients derived from a large number of manual measurements, and k3 is the error compensation coefficient.

8. A flatfish growth identification system, applied to any flatfish growth identification method according to any one of claims 1 to 7, characterized in that: It includes model training module, image acquisition module, variety identification module and intelligent analysis module; The model training module trains the YOLOv8 model based on the prepared training set and makes adjustments based on the results of cross-validation; The image acquisition module shoots the fish body at multiple angles based on a high-definition camera; The variety identification module performs variety identification based on measurable parameters and framework parameters; The intelligent analysis module determines the growth status based on body width, body length, and body thickness.

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