Fish disease detection method and device, computer equipment and storage medium

Abstract

The invention discloses a fish disease detection method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, obtaining and analyzing an image of a farm, performing target identification to determine the distribution position of fish bodies, then segmenting the image to obtain a plurality of independent fish body images, inputting the fish body images into a fish disease detection model, identifying fish bodies carrying diseases, and then determining a fish disease body image. And then, acquiring an image of the farm again, and carrying out image matching so as to determine the specific position of the diseased fish. And the system continuously monitors the position to obtain the motion path of the fish sick body, and outputs the final fish sick body image and the motion path thereof to the client. The invention also relates to the technical field of block chains, and the fish body image is stored on a block chain network. According to the method, automatic fish disease monitoring and tracking are realized by using image recognition, target detection and a deep learning model, and the efficiency and accuracy of fish disease prevention and control are improved.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology, and specifically relates to a fish disease detection method, device, computer equipment and storage medium. Background Art

[0002] Aquaculture is one of the fastest growing industries in the world. Artificial intelligence technology has been maturely studied and applied in many aquaculture sub-fields such as water quality monitoring, intelligent feeding, and remote temperature control, but it has not yet achieved a breakthrough in the quality and speed of industry practice in the sub-field of fish disease detection. Summer is the season when fish reproduce most vigorously in aquaculture, but due to high temperatures and heavy rainfall, fish pathogens will also multiply in large numbers, leading to various farmed fish diseases, especially some viral fish diseases, which are often highly contagious. Once they are accidentally prevalent, they will cause a large number of fish deaths, which will have a huge impact on the aquaculture industry.

[0003] Farmed fish disease detection is extremely important for the underwriting and claims of aquaculture insurance. However, the current popular method of fish disease detection in the industry is mainly through manual processing such as naked eye observation of fry activity and feeding status, body color and body shape. However, the number of fry in fish ponds is huge, and manual naked eye observation is time-consuming and labor-intensive, and it is difficult to ensure that fish diseases are discovered in the first place, which often leads to inaccurate risk assessment when aquaculture insurance is insured, and disputes arise due to insufficient evidence or delays in claims, affecting the efficiency of insurance services and the protection of farmers' rights and interests. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a fish disease detection method, device, computer equipment and storage medium to solve the technical problems that the existing farmed fish disease detection adopts a manual processing method of observing the condition of fry by naked eye, which is inefficient, time-consuming and labor-intensive, and difficult to ensure that fish diseases are discovered in the first time, resulting in inaccurate risk assessment when insuring and claiming aquaculture insurance, as well as disputes caused by insufficient evidence or delays, affecting the efficiency of insurance services and the protection of the rights and interests of farmers.

[0005] In order to solve the above technical problems, the present application embodiment provides a fish disease detection method, which adopts the following technical solution:

[0006] A method for detecting fish diseases, comprising:

[0007] Obtaining a detection instruction, obtaining a breeding site image according to the detection instruction, obtaining a first breeding site image, and performing target recognition on the first breeding site image to obtain a first target recognition result;

[0008] Performing image segmentation on the first breeding site image according to the first target recognition result to obtain a plurality of fish body images;

[0009] Import several fish images into the pre-trained fish disease detection model to identify fish carrying fish diseases;

[0010] Among a plurality of fish body images, fish body images carrying fish disease bodies are identified to obtain fish disease body images;

[0011] Acquire the breeding site image again to obtain a second breeding site image, and perform image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image;

[0012] The position of the diseased fish in the image of the second breeding site is monitored to obtain a movement path of the diseased fish, and the image of the diseased fish and the movement path of the diseased fish are output to the client.

[0013] Furthermore, a plurality of cameras are arranged around the breeding site, each camera is responsible for photographing a part of the breeding site, obtaining a detection instruction, obtaining an image of the breeding site according to the detection instruction, and obtaining the first breeding site image, specifically comprising:

[0014] Receive detection instructions, and instruct several cameras to shoot the breeding grounds according to the detection instructions;

[0015] Acquire images of the breeding ground taken by several cameras to obtain several regional images of the breeding ground;

[0016] The images of several breeding grounds are fused to obtain a first breeding ground image.

[0017] Furthermore, each camera is connected to an edge server, and each edge server is connected to a cloud server. After the step of obtaining the images of the breeding ground taken by several cameras and obtaining several images of the breeding ground area, the method further includes:

[0018] For each breeding site area image, image feature extraction is performed on the breeding site area image to obtain area image features;

[0019] Performing image block processing on the regional image of the breeding site according to regional image features to obtain a number of image sub-blocks;

[0020] Transmitting several image sub-blocks from the edge server to the cloud server in sequence;

[0021] On the cloud server, image reconstruction is performed based on the image features of the image sub-blocks to obtain an image of the breeding site area.

[0022] Furthermore, before the step of importing a plurality of fish body images into the pre-trained fish disease detection model to identify fish bodies carrying fish diseases, the method further includes:

[0023] Acquire historical fish body images, wherein the historical fish body images include historical diseased fish body images and historical normal fish body images;

[0024] The historical diseased fish images are used as positive sample data, and the historical normal fish images are used as negative sample data to construct a data sample set;

[0025] The data sample set is imported into a preset image classification model, so that the image classification model learns the image features in the data sample set to obtain a pre-trained fish disease detection model.

[0026] Furthermore, the data sample set includes a training data set and a verification data set. The data sample set is imported into a preset image classification model so that the image classification model learns the image features in the data sample set to obtain a pre-trained fish disease detection model. The steps specifically include:

[0027] Extracting image features of sample images in the training data set to obtain training sample image features;

[0028] In the image classification model, the training sample image features and the preset classification labels are associated with each other to obtain an initial fish classification model;

[0029] The initial fish classification model was verified using the verification data set, and the initial fish classification model was iteratively trained based on the model verification results until the model was fitted to obtain a fish disease detection model.

[0030] Further, the step of acquiring the breeding site image again to obtain a second breeding site image, and performing image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image specifically includes:

[0031] Acquire the breeding site image again to obtain a second breeding site image, and perform target recognition on the second breeding site image to obtain a second target recognition result;

[0032] Performing image segmentation on the second breeding site image according to the second target recognition result to obtain a plurality of fish body images to be detected;

[0033] Extracting image features of diseased fish images to obtain a first fish feature, and extracting image features of a plurality of fish images to be detected to obtain a second fish feature;

[0034] Calculating the similarity between the first fish body feature and the second fish body feature;

[0035] When the similarity between the first fish body feature and the second fish body feature is greater than or equal to a preset similarity threshold, a fish body matching the diseased fish body in the second breeding site image is determined to obtain a target fish body;

[0036] A position coordinate system of the breeding site is constructed, and the position of the target fish body is determined in the position coordinate system to obtain the position of the diseased fish body in the second breeding site image.

[0037] Further, the step of monitoring the position of the diseased fish in the image of the second breeding site, obtaining the movement path of the diseased fish, and outputting the image of the diseased fish and the movement path of the diseased fish to the client specifically includes:

[0038] Determine the position of the diseased fish in the second breeding site image as the first position;

[0039] Acquire images of the breeding site according to a preset time period to obtain a plurality of third breeding site images;

[0040] Determine the position of the target fish body in the position coordinate system, and obtain the position of the diseased fish body in the images of the plurality of third breeding sites;

[0041] Determine the position of the diseased fish in the third breeding site image as a second position;

[0042] Connect the first position and the second position to obtain the movement path of the fish disease body;

[0043] The diseased fish image and the diseased fish movement path are output to the client.

[0044] In order to solve the above technical problems, the present application also provides a fish disease detection device, which adopts the following technical solution:

[0045] A fish disease detection device, comprising:

[0046] A first image acquisition module is used to acquire a detection instruction, acquire an image of a breeding site according to the detection instruction, obtain a first breeding site image, and perform target recognition on the first breeding site image to obtain a first target recognition result;

[0047] An image segmentation module, used to perform image segmentation on the first breeding site image according to the first target recognition result to obtain a plurality of fish body images;

[0048] The fish disease detection module is used to import a number of fish images into a pre-trained fish disease detection model to identify fish carrying fish diseases;

[0049] The fish disease body image matching module is used to identify the fish body image carrying the fish disease body among a plurality of fish body images to obtain the fish disease body image;

[0050] A second image acquisition module is used to acquire the breeding site image again to obtain a second breeding site image, and to perform image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image;

[0051] The diseased fish monitoring module is used to monitor the position of the diseased fish in the image of the second breeding site, obtain the movement path of the diseased fish, and output the diseased fish image and the movement path of the diseased fish to the client.

[0052] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0053] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of any of the above-mentioned fish disease detection methods when executing the computer-readable instructions.

[0054] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0055] A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of any one of the above-mentioned fish disease detection methods.

[0056] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0057] The present application discloses a fish disease detection method, device, computer equipment and storage medium, which belongs to the field of artificial intelligence technology. The present application realizes automatic identification and tracking of the disease conditions of farmed fish through image recognition, target detection and deep learning models, effectively solves the inefficiency, time-consuming and labor-intensive problems of traditional manual observation of fry, realizes real-time and accurate monitoring of fish diseases, ensures early detection of fish diseases, and through automatic monitoring of the position of sick fish and tracking of movement paths, the system can provide accurate evidence of fish diseases, improves the accuracy of risk assessment in aquaculture insurance, avoids claims disputes caused by delayed discovery or insufficient evidence, thereby improving the efficiency of insurance services and further protecting the rights and interests of farmers. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0059] Figure 1 An exemplary system architecture diagram is shown in which the present application can be applied;

[0060] Figure 2 A flow chart showing an embodiment of a fish disease detection method according to the present application is shown;

[0061] Figure 3 A structural schematic diagram of an embodiment of a fish disease detection device according to the present application is shown;

[0062] Figure 4 A schematic structural diagram of an embodiment of a computer device according to the present application is shown. DETAILED DESCRIPTION

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0064] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0066] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0067] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0068] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0069] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0070] It should be noted that the fish disease detection method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the fish disease detection device is generally arranged in the server / terminal device.

[0071] It should be understood that Figure 1 The numbers of terminal devices, networks and servers in the embodiment are merely illustrative. The above system may have any number of terminal devices, networks and servers according to implementation requirements.

[0072] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for detecting fish diseases according to the present application. The method for detecting fish diseases comprises the following steps:

[0073] S201, obtaining a detection instruction, obtaining a breeding site image according to the detection instruction, obtaining a first breeding site image, and performing target recognition on the first breeding site image to obtain a first target recognition result.

[0074] Specifically, the system first receives a detection instruction, which is usually issued by the farm's monitoring personnel or automated control system. When the instruction is issued, the system will call a camera or other imaging device to collect real-time images of the farm in order to analyze and process the image. The acquired image is called the first breeding site image, which contains the overall distribution of fish in the current pool or breeding area. Subsequently, the system performs target recognition on the image, mainly identifying the location and approximate outline of all fish. This step is achieved through computer vision technology, which may include edge detection, contour extraction or other feature recognition methods to accurately distinguish fish from background or other irrelevant objects.

[0075] Target recognition can be achieved based on the YOLO neural network model. YOLO (You Only Look Once) is a real-time target detection model based on a convolutional neural network (CNN). The YOLO model is popular for its efficient target detection speed and high accuracy. It is suitable for real-time application scenarios with limited computing resources, such as video surveillance, autonomous driving, and robot vision. The YOLO model can divide the image of the breeding site into multiple grids and determine whether there are fish bodies in each grid and the type and location information of the fish bodies. Through training, the YOLO model can accurately identify different types of fish bodies, such as carp, crucian carp, grass carp, etc. In addition, by analyzing the behavioral characteristics of fish bodies in the breeding site images, the YOLO model can monitor the lesions and abnormal behaviors of fish bodies, such as abnormal swimming speed, aggregation or dispersion, etc.

[0076] S202: performing image segmentation on the first breeding site image according to the first target recognition result to obtain a plurality of fish body images.

[0077] Specifically, the system uses the results of target recognition to further refine the first breeding site image and perform image segmentation. The purpose of image segmentation is to separate the fish area in the entire image from the background in order to obtain a separate fish image. The segmentation method can use traditional image processing algorithms such as threshold segmentation and region growing, or more advanced deep learning segmentation models such as U-Net, Mask R-CNN, etc. These technologies can accurately identify the edges and contours of the fish body, so that each fish body can be independently extracted into a separate image. These independent fish body images will be used for disease detection in subsequent steps. Therefore, the accuracy of image segmentation directly affects the accuracy of fish disease detection.

[0078] S203, importing a plurality of fish body images into a pre-trained fish disease detection model to identify fish bodies carrying fish diseases.

[0079] Specifically, the system inputs multiple individual fish images extracted from image segmentation into a pre-trained fish disease detection model, which is based on deep learning technology and has been trained with a large amount of labeled data to identify the visual features of specific fish diseases. For example, the model can detect disease features such as abnormal color of fish scales, skin lesions, and parasite attachment. The model can be constructed using structures such as convolutional neural networks (CNNs) to automatically identify sick fish by extracting feature vectors and comparing them with known diseases. In this process, the model not only identifies whether the fish is carrying a disease, but can also further output the probability score of a specific disease, providing a basis for identifying sick fish images.

[0080] S204, identifying fish body images carrying fish diseases among a plurality of fish body images, and obtaining fish disease body images.

[0081] Specifically, after the fish disease detection model identifies fish that may be carrying the disease, the system will extract these diseased fish images separately and generate fish disease images. This step is to screen all the test results to ensure that the system only retains images of confirmed diseased fish and removes images of healthy fish. Each fish disease image includes detailed features of the fish, such as disease type, location, symptoms and other information, for further analysis. Fish disease images are not only easy to save as records, but also can be used to evaluate the severity and change trend of the symptoms of sick fish through further image analysis, providing reliable data support for tracking and intervention.

[0082] S205, acquiring the breeding site image again to obtain a second breeding site image, and performing image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image.

[0083] Specifically, after identifying the image of the diseased fish, the system will obtain the real-time image of the farm again after a period of time, that is, the second farm image, in order to update the actual location of the diseased fish. The system will match the newly acquired second farm image with the previous diseased fish image, and lock the real-time location of the diseased fish by comparing the position features. This matching process relies on algorithms such as image feature point matching and pattern recognition to ensure accurate tracking of the location of the diseased fish in the new image. This image matching method enables the system to track diseased fish in real time in a dynamic water environment, avoiding positioning deviations caused by the movement of the fish and improving the accuracy of monitoring.

[0084] S206, monitoring the position of the diseased fish in the image of the second breeding site, obtaining a movement path of the diseased fish, and outputting the image of the diseased fish and the movement path of the diseased fish to the client.

[0085] Specifically, after the specific location of the sick fish is determined, the system will start real-time tracking and monitoring of the sick fish to obtain its movement path. By continuously capturing the location of the sick fish, the system can depict the swimming trajectory of the sick fish and record its movement pattern. These data can provide information on the behavior of the sick fish, such as whether it is out of the group, whether it swims abnormally, etc., to provide support for disease judgment. The system also sends information such as the image and movement path of the sick fish to the client, allowing managers to view the real-time dynamics of the sick fish on the client device. This data output method enables aquaculture managers to take timely measures, such as isolating sick fish or administering drugs, which improves the efficiency of sick fish treatment and reduces the risk of fish disease spread.

[0086] In the process of aquaculture insurance underwriting and claims settlement, the above-mentioned fish disease detection solution can provide timely and accurate identification of sick fish and data support. For example, before taking out insurance, the insurance company needs to assess the risk status of the farm. Traditionally, it is difficult to accurately obtain the specific situation of sick fish, resulting in a highly subjective assessment. After applying this solution, the system can automatically collect fish images from the farm, detect sick fish in real time through a pre-trained model, and transmit sick fish images and data to the insurance company. In this way, the insurance company can conduct accurate risk assessment based on real-time detection data, determine reasonable insurance premiums based on the type, quantity and distribution of fish diseases, and ensure that the insurance plan is more fair and targeted.

[0087] During the claims process, the solution can also help insurance companies and farmers verify the occurrence and development of fish diseases and avoid disputes caused by insufficient evidence. When a disease breaks out in a fish population, the system can monitor the distribution and movement paths of the diseased fish in real time, and retain specific diseased fish images and movement data as evidence. Insurance company claims personnel can use this data to determine whether the diseased fish have died or suffered losses due to the insured risk, clarify the cause of the disease and the attribution of responsibility, and avoid disputes. In addition, the system can also record the changes and spread of symptoms of diseased fish, providing a basis for determining whether the spread of the disease is a controllable factor, and improving the efficiency and transparency of claims.

[0088] For farmers, the use of this system also effectively protects their rights and interests. By real-time monitoring of sick fish, farmers can obtain disease warning information in a timely manner and take measures to reduce losses. If a large-scale disease occurs, farmers can submit diseased fish detection data directly to the insurance company as strong evidence for claim applications, ensuring a faster and smoother claims process. This digital and automated disease detection method greatly shortens the review time for claims, avoids claims disputes caused by lack of diseased fish monitoring records, and enables farmers to obtain more timely compensation.

[0089] In the above embodiments, the present application realizes automatic identification and tracking of the disease conditions of farmed fish through image recognition, target detection and deep learning models, effectively solving the inefficient, time-consuming and labor-intensive problems of traditional manual observation of fish fry, and realizing real-time and accurate monitoring of fish diseases, ensuring the early detection of fish diseases. Moreover, through automatic monitoring of the position of sick fish and tracking of movement paths, the system can provide accurate evidence of fish diseases, thereby improving the accuracy of risk assessment in aquaculture insurance, avoiding claims disputes caused by delayed discovery or insufficient evidence, thereby improving the efficiency of insurance services and further protecting the rights and interests of farmers.

[0090] Furthermore, a plurality of cameras are arranged around the breeding site, each camera is responsible for photographing a part of the breeding site, obtaining a detection instruction, obtaining an image of the breeding site according to the detection instruction, and obtaining the first breeding site image, specifically comprising:

[0091] Receive detection instructions, and instruct several cameras to shoot the breeding grounds according to the detection instructions;

[0092] Acquire images of the breeding ground taken by several cameras to obtain several regional images of the breeding ground;

[0093] The images of several breeding grounds are fused to obtain a first breeding ground image.

[0094] In this embodiment, the system first receives a detection instruction, and controls several cameras around the breeding site according to the instruction to synchronously shoot different areas. Each camera covers a part of the breeding site to ensure that the areas shot by each camera can be seamlessly connected to achieve full-area monitoring of the breeding site. Subsequently, the system collects the images taken by each camera, that is, the regional images of the breeding site, which correspond to the various areas covered by the camera. Then, the system performs image fusion processing on these regional images, and generates a complete first breeding site image through steps such as splicing, alignment and correction. Image fusion technology ensures that each regional image is consistent in brightness, contrast and geometric alignment to form a unified panoramic picture, which is convenient for target detection and fish disease identification processing. This panoramic image generation method is suitable for large-scale breeding farms and can clearly show the distribution of fish.

[0095] Through the above steps, the system can generate high-quality images covering the entire farm, making the monitoring perspective during diseased fish detection more comprehensive, which helps to improve the accuracy and efficiency of detection.

[0096] Furthermore, each camera is connected to an edge server, and each edge server is connected to a cloud server. After the step of obtaining the images of the breeding ground taken by several cameras and obtaining several images of the breeding ground area, the method further includes:

[0097] For each breeding site area image, image feature extraction is performed on the breeding site area image to obtain area image features;

[0098] Performing image block processing on the regional image of the breeding site according to regional image features to obtain a number of image sub-blocks;

[0099] Transmitting several image sub-blocks from the edge server to the cloud server in sequence;

[0100] On the cloud server, image reconstruction is performed based on the image features of the image sub-blocks to obtain an image of the breeding site area.

[0101] In this embodiment, each camera transmits the captured image of the breeding site area to the edge server connected to it. The edge server pre-processes the received regional image, first extracts the feature information of the image, which can be the key points, edge information, etc. of the image. Then, the system blocks the regional image of the breeding site according to the extracted image features and decomposes it into several image sub-blocks. The block operation can reduce the amount of data transmitted each time, reduce the pressure of network transmission, and enable large-scale images to be transmitted to the cloud server more efficiently. Then, each edge server transmits the processed image sub-blocks to the cloud server one by one. After receiving the image, the cloud server recombines the image features of each image sub-block, reconstructs the image, and restores it to a complete regional image of the breeding site. When reconstructing the image, the cloud server uses the image feature information to ensure the correct splicing and fusion between the image sub-blocks, and ensures that the reconstructed regional image has high quality and coherence.

[0102] Through the above steps, the system can efficiently complete image transmission and reconstruction, significantly reducing bandwidth requirements while ensuring image quality, and improving the real-time and reliability of data processing.

[0103] Furthermore, before the step of importing a plurality of fish body images into the pre-trained fish disease detection model to identify fish bodies carrying fish diseases, the method further includes:

[0104] Acquire historical fish body images, wherein the historical fish body images include historical diseased fish body images and historical normal fish body images;

[0105] The historical diseased fish images are used as positive sample data, and the historical normal fish images are used as negative sample data to construct a data sample set;

[0106] The data sample set is imported into a preset image classification model, so that the image classification model learns the image features in the data sample set to obtain a pre-trained fish disease detection model.

[0107] In this embodiment, in order to improve the accuracy of the fish disease detection model, the system first trains the model through historical data before formal detection. First, the system obtains historical fish images from the database, which include historical fish disease images and normal fish images that have been marked. The system marks the fish disease images as positive samples and the normal fish images as negative samples to construct a set of labeled data sample sets. Then, this data sample set is input into the preset image classification model for training. By learning the feature differences of these positive and negative samples, the image classification model can gradually grasp the typical features of fish disease bodies, such as color, morphology, and lesion location. During the training process, the model optimizes parameters through multiple iterations, so that it has the ability to identify lesions in new images. The model pre-trained in this way becomes a pre-trained fish disease detection model, which can identify and judge the lesion features in future fish images.

[0108] The image classification model is an intelligent model built based on a deep learning algorithm. It can learn image features and classify images into different categories through a large number of labeled samples. In the fish disease detection application, the image classification model is trained to distinguish between "sick fish" and "healthy fish". By comparing positive samples (sick fish images) and negative samples (normal fish images), the typical features of the two types of images are learned, such as color, shape, texture and other unique features of diseased areas. During the training process, the model continuously adjusts its internal parameters to optimize the recognition of fish disease features. Ultimately, the model can automatically extract and analyze features from the input image to determine whether the fish is sick, thereby achieving fast and accurate classification during fish disease detection.

[0109] Through the above steps, the system can obtain a fish disease detection model with good detection accuracy, thereby effectively improving the accuracy of diseased fish identification and ensuring the reliability of the detection results.

[0110] Furthermore, the data sample set includes a training data set and a verification data set. The data sample set is imported into a preset image classification model so that the image classification model learns the image features in the data sample set to obtain a pre-trained fish disease detection model. The steps specifically include:

[0111] Extracting image features of sample images in the training data set to obtain training sample image features;

[0112] In the image classification model, the training sample image features and the preset classification labels are associated with each other to obtain an initial fish classification model;

[0113] The initial fish classification model was verified using the verification data set, and the initial fish classification model was iteratively trained based on the model verification results until the model was fitted to obtain a fish disease detection model.

[0114] In this embodiment, the system first extracts the features of each sample image from the training data set, such as texture, shape, color and other information, to form a training sample image feature set, and assigns corresponding classification labels (such as "sick fish" or "healthy fish") to these samples. Then, the training sample image feature set and the classification label are imported into the preset image classification model, and the association learning is performed to generate a preliminary fish body classification model, that is, the initial fish body classification model. This model will then be verified by the verification data set. The verification data set does not participate in the initial training, so it can more truly reflect the classification ability of the model. The system evaluates the accuracy of the model based on the verification results, and further performs iterative training to improve its ability to recognize fish disease characteristics by adjusting the model parameters. This process continues until the model can achieve an ideal fitting effect on the verification data set, and then the final fish disease detection model is generated. The model has the ability to efficiently and accurately identify sick fish in new images.

[0115] When performing associative learning, the system will match the training sample image feature set (such as feature vectors containing information such as fish color, shape, texture, etc.) with the corresponding classification labels (such as "sick fish" or "healthy fish") one by one and import them into the image classification model. During the learning process, the model gradually identifies the feature differences of different categories of images by calculating the relationship between the input image features and the classification labels. For example, in the early stage of training, the model randomly initializes its parameters. Each time the image features are input into the model, the model will generate a predicted label, and then compare the predicted label with the actual label (classification label) to calculate the error. The error adjusts the model parameters through back propagation, so that the model can gradually reduce misclassification. After multiple rounds of training, the model learns to associate the patterns between fish disease features and labels, allowing it to accurately predict the health status of the fish based on image features. This associative learning process enables the model to eventually identify sick fish or healthy fish based on fish body features.

[0116] Through the above steps, the system obtains a fish disease detection model that has been fully trained and verified, enabling the model to achieve high recognition accuracy in actual detection and ensure the reliability and stability of diseased fish identification results.

[0117] Further, the step of acquiring the breeding site image again to obtain a second breeding site image, and performing image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image specifically includes:

[0118] Acquire the breeding site image again to obtain a second breeding site image, and perform target recognition on the second breeding site image to obtain a second target recognition result;

[0119] Performing image segmentation on the second breeding site image according to the second target recognition result to obtain a plurality of fish body images to be detected;

[0120] Extracting image features of diseased fish images to obtain a first fish feature, and extracting image features of a plurality of fish images to be detected to obtain a second fish feature;

[0121] Calculating the similarity between the first fish body feature and the second fish body feature;

[0122] When the similarity between the first fish body feature and the second fish body feature is greater than or equal to a preset similarity threshold, a fish body matching the diseased fish body in the second breeding site image is determined to obtain a target fish body;

[0123] A position coordinate system of the breeding site is constructed, and the position of the target fish body is determined in the position coordinate system to obtain the position of the diseased fish body in the second breeding site image.

[0124] In this embodiment, the system first obtains the overall image of the farm again through the camera, that is, the second farm image, and performs target recognition on the image to obtain the specific distribution and position of the fish in the farm, forming a second target recognition result. Based on this result, the system performs image segmentation on the second farm image and extracts a number of fish images to be detected. Next, the system performs feature extraction on the initially detected fish disease image, and the extracted features include unique texture, color or shape and other feature values ​​to form the first fish feature; at the same time, the features of the fish images to be detected are extracted one by one to form a second fish feature set. Subsequently, the system calculates the similarity between the first fish feature and each second fish feature to find the most matching fish position. If the similarity reaches or exceeds the set threshold, it is considered that the corresponding fish disease body is found and marked as the target fish body. The system then determines the specific position of the target fish body by constructing a coordinate system of the farm, thereby obtaining the precise position of the fish disease body in the second farm image.

[0125] Through the above steps, the system can efficiently locate and track the position of diseased fish in the farm, making subsequent monitoring and prevention and control measures more targeted and improving the accuracy and efficiency of fish disease detection and management.

[0126] Further, the step of monitoring the position of the diseased fish in the image of the second breeding site, obtaining the movement path of the diseased fish, and outputting the image of the diseased fish and the movement path of the diseased fish to the client specifically includes:

[0127] Determine the position of the diseased fish in the second breeding site image as the first position;

[0128] Acquire images of the breeding site according to a preset time period to obtain a plurality of third breeding site images;

[0129] Determine the position of the target fish body in the position coordinate system, and obtain the position of the diseased fish body in the images of the plurality of third breeding sites;

[0130] Determine the position of the diseased fish in the third breeding site image as a second position;

[0131] Connect the first position and the second position to obtain the movement path of the fish disease body;

[0132] The diseased fish image and the diseased fish movement path are output to the client.

[0133] In this embodiment, the system first marks the initial position of the identified fish disease body in the second breeding site image as the first position. Then, the system continuously acquires new breeding site images according to a preset time period (for example, every few seconds), which are called third breeding site images, and tracks the position of each third breeding site image. Through coordinate matching, the system marks the position of the fish disease body in these images at each time point in the position coordinate system and records its relative coordinates, so as to obtain the position information of the fish disease body at each time point, that is, the second position. The system then connects the positions of the fish disease body at all time points in sequence to generate the movement path of the fish disease body, thereby reflecting the dynamic activity trajectory of the fish disease body in the breeding farm. Through this path information, the system can accurately track the activity range of the sick fish, which helps the breeding managers to grasp the distribution and diffusion trend of the sick fish in the breeding environment. Finally, the system transmits the complete data of the image and movement path of the fish disease body to the client so that the managers can view and take necessary measures in real time.

[0134] Through the above steps, the system can achieve accurate tracking and path monitoring of sick fish, making it easy to grasp the activity status of sick fish in a timely manner, helping to take targeted intervention measures and reduce the risk of fish disease spread.

[0135] In the above embodiment, the present application discloses a fish disease detection method, which belongs to the field of artificial intelligence technology. The present application realizes automatic identification and tracking of the disease conditions of farmed fish through image recognition, target detection and deep learning models, effectively solving the inefficiency, time-consuming and labor-intensive problems of traditional manual observation of fry, and realizes real-time and accurate monitoring of fish diseases, ensuring early detection of fish diseases, and through automatic monitoring of the position of sick fish and tracking of movement paths, the system can provide accurate evidence of fish diseases, improve the accuracy of risk assessment in aquaculture insurance, avoid claims disputes caused by delayed discovery or insufficient evidence, thereby improving the efficiency of insurance services and further protecting the rights and interests of farmers.

[0136] In this embodiment, the fish disease detection method is executed on an electronic device (e.g. Figure 1The server shown in the figure) can receive instructions or obtain data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0137] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned fish image information, the above-mentioned fish image information can also be stored in a node of a blockchain.

[0138] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0139] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0140] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0142] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0143] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a fish disease detection device, which is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0144] like Figure 3 As shown, the fish disease detection device 300 described in this embodiment includes:

[0145] The first image acquisition module 301 is used to acquire a detection instruction, acquire a breeding site image according to the detection instruction, obtain a first breeding site image, and perform target recognition on the first breeding site image to obtain a first target recognition result;

[0146] An image segmentation module 302 is used to segment the first breeding site image according to the first target recognition result to obtain a plurality of fish body images;

[0147] The fish disease detection module 303 is used to import a number of fish body images into a pre-trained fish disease detection model to identify fish bodies carrying fish diseases;

[0148] The fish diseased body image matching module 304 is used to identify the fish body image carrying the fish diseased body among a plurality of fish body images, and obtain the fish diseased body image;

[0149] The second image acquisition module 305 is used to acquire the breeding site image again to obtain a second breeding site image, and perform image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image;

[0150] The fish disease body monitoring module 306 is used to monitor the position of the fish disease body in the second breeding site image, obtain the movement path of the fish disease body, and output the fish disease body image and the fish disease body movement path to the client.

[0151] Furthermore, a number of cameras are arranged around the breeding ground, and each camera is responsible for photographing a part of the breeding ground. The first image acquisition module 301 is specifically used for:

[0152] Receive a detection instruction, and instruct several cameras to shoot the breeding ground according to the detection instruction; obtain the breeding ground images shot by several cameras to obtain several breeding ground area images; perform image fusion on the several breeding ground area images to obtain a first breeding ground image.

[0153] Furthermore, each camera is connected to an edge server, each edge server is connected to a cloud server, and the first image acquisition module 301 is also used for:

[0154] For each breeding site area image, image features are extracted to obtain regional image features; image segmentation is performed on the breeding site area image according to the regional image features to obtain a number of image sub-blocks; the several image sub-blocks are transmitted from the edge server to the cloud server in sequence; on the cloud server, image reconstruction is performed based on the image features of the image sub-blocks to obtain the breeding site area image.

[0155] Furthermore, the fish disease detection device 300 also includes a model training module, which is specifically used for:

[0156] Acquire historical fish body images, wherein the historical fish body images include historical fish disease images and historical normal fish body images; construct a data sample set by taking the historical fish disease images as positive sample data and the historical normal fish body images as negative sample data; import the data sample set into a preset image classification model, so that the image classification model learns the image features in the data sample set, and obtains a pre-trained fish disease detection model.

[0157] Furthermore, the data sample set includes a training data set and a validation data set, and the model training module is further used to:

[0158] The image features of the sample images in the training data set are extracted to obtain the training sample image features; in the image classification model, the training sample image features and the preset classification labels are associated with each other to obtain the initial fish classification model; the initial fish classification model is verified using the verification data set, and the initial fish classification model is iteratively trained based on the model verification results until the model is fitted to obtain the fish disease detection model.

[0159] Furthermore, the second image acquisition module 305 is specifically used for:

[0160] The breeding site image is acquired again to obtain a second breeding site image, and target recognition is performed on the second breeding site image to obtain a second target recognition result; the second breeding site image is segmented according to the second target recognition result to obtain a plurality of fish body images to be detected; image feature extraction is performed on the diseased fish image to obtain a first fish body feature, and image feature extraction is performed on a plurality of fish body images to be detected to obtain a second fish body feature; the similarity between the first fish body feature and the second fish body feature is calculated; when the similarity between the first fish body feature and the second fish body feature is greater than or equal to a preset similarity threshold, the fish body matching the diseased fish body in the second breeding site image is determined to obtain the target fish body; a position coordinate system of the breeding site is constructed, and the position of the target fish body is determined in the position coordinate system to obtain the position of the diseased fish body in the second breeding site image.

[0161] Furthermore, the fish disease monitoring module 306 is specifically used for:

[0162] The position of the diseased fish body in the second breeding ground image is determined as the first position; the breeding ground image is acquired according to a preset time period to obtain a plurality of third breeding ground images; the position of the target fish body is determined in the position coordinate system to obtain the position of the diseased fish body in the plurality of third breeding ground images; the position of the diseased fish body in the third breeding ground image is determined as the second position; the first position and the second position are connected to obtain a movement path of the diseased fish body; and the diseased fish body image and the movement path of the diseased fish body are output to the client.

[0163] In the above embodiment, the present application discloses a fish disease detection device, which belongs to the field of artificial intelligence technology. The present application realizes automatic identification and tracking of the disease conditions of farmed fish through image recognition, target detection and deep learning models, effectively solving the inefficient, time-consuming and labor-intensive problems of traditional manual observation of fry, and realizes real-time and accurate monitoring of fish diseases, ensuring early detection of fish diseases. Moreover, through automatic monitoring of the position of sick fish and tracking of movement paths, the system can provide accurate evidence of fish diseases, improve the accuracy of risk assessment in aquaculture insurance, avoid claims disputes caused by delayed discovery or insufficient evidence, thereby improving the efficiency of insurance services and further protecting the rights and interests of farmers.

[0164] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0165] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 with a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0166] The computer device may be a desktop computer, a notebook computer, a PDA, a cloud server, etc. The computer device may interact with a user via a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0167] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the fish disease detection method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0168] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the fish disease detection method.

[0169] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0170] In the above embodiment, the present application discloses a computer device, which belongs to the field of artificial intelligence technology. The present application realizes automatic identification and tracking of the disease conditions of farmed fish through image recognition, target detection and deep learning models, effectively solves the inefficiency, time-consuming and labor-intensive problems of traditional manual observation of fry, realizes real-time and accurate monitoring of fish diseases, ensures early detection of fish diseases, and through automatic monitoring of the position of sick fish and tracking of movement paths, the system can provide accurate evidence of fish diseases, improves the accuracy of risk assessment in aquaculture insurance, avoids claims disputes caused by delayed discovery or insufficient evidence, thereby improving the efficiency of insurance services and further protecting the rights and interests of farmers.

[0171] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the fish disease detection method as described above.

[0172] In the above embodiments, the present application discloses a computer-readable storage medium, which belongs to the field of artificial intelligence technology. The present application realizes automatic identification and tracking of the disease conditions of farmed fish through image recognition, target detection and deep learning models, effectively solves the inefficiency, time-consuming and labor-intensive problems of traditional manual observation of fry, realizes real-time and accurate monitoring of fish diseases, ensures early detection of fish diseases, and through automatic monitoring of the position of sick fish and tracking of movement paths, the system can provide accurate evidence of fish diseases, improves the accuracy of risk assessment in aquaculture insurance, avoids claims disputes caused by delayed discovery or insufficient evidence, thereby improving the efficiency of insurance services and further protecting the rights and interests of farmers.

[0173] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0174] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0175] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for detecting fish diseases, characterized in that: include: Obtaining a detection instruction, obtaining a breeding site image according to the detection instruction, obtaining a first breeding site image, and performing target recognition on the first breeding site image to obtain a first target recognition result; Performing image segmentation on the first breeding site image according to the first target recognition result to obtain a plurality of fish body images; Importing a plurality of the fish body images into a pre-trained fish disease detection model to identify fish bodies carrying fish diseases; Among the plurality of fish body images, identifying the fish body image carrying the fish disease body, and obtaining the fish disease body image; Acquire the breeding site image again to obtain a second breeding site image, and perform image matching on the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image; The position of the diseased fish body in the second breeding site image is monitored to obtain a movement path of the diseased fish body, and the image of the diseased fish body and the movement path of the diseased fish body are output to a client.

2. The fish disease detection method according to claim 1, characterized in that: A plurality of cameras are arranged around the breeding site, each of which is responsible for photographing a portion of the breeding site. The step of obtaining a detection instruction, obtaining an image of the breeding site according to the detection instruction, and obtaining a first breeding site image specifically includes: receiving a detection instruction, and instructing a plurality of cameras to photograph the breeding ground according to the detection instruction; Acquire a number of breeding site images taken by the cameras to obtain a number of breeding site area images; The first breeding site image is obtained by performing image fusion on a plurality of the breeding site area images.

3. The fish disease detection method according to claim 2, characterized in that: Each of the cameras is connected to an edge server, and each of the edge servers is connected to a cloud server. After the step of acquiring the images of the breeding grounds taken by the cameras and obtaining the images of the breeding grounds, the method further includes: For each breeding site area image, image feature extraction is performed on the breeding site area image to obtain area image features; Performing image block processing on the breeding site area image according to the regional image features to obtain a plurality of image sub-blocks; Transmitting a plurality of the image sub-blocks from the edge server to the cloud server in sequence; On the cloud server, image reconstruction is performed based on the image features of the image sub-blocks to obtain the breeding site area image.

4. The fish disease detection method according to claim 1, characterized in that: Before the step of importing the plurality of fish body images into the pre-trained fish disease detection model to identify fish bodies carrying fish diseases, the method further includes: Acquire historical fish body images, wherein the historical fish body images include historical diseased fish body images and historical normal fish body images; The historical diseased fish images are used as positive sample data, and the historical normal fish images are used as negative sample data to construct a data sample set; The data sample set is imported into a preset image classification model, so that the image classification model learns the image features in the data sample set to obtain the pre-trained fish disease detection model.

5. The fish disease detection method according to claim 4, characterized in that: The data sample set includes a training data set and a verification data set. The step of importing the data sample set into a preset image classification model so that the image classification model learns the image features in the data sample set to obtain the pre-trained fish disease detection model specifically includes: Extracting image features of sample images in the training data set to obtain training sample image features; In the image classification model, the training sample image features and the preset classification labels are associated with each other to obtain an initial fish classification model; The initial fish classification model is verified using the verification data set, and the initial fish classification model is iteratively trained based on the model verification result until the model is fitted to obtain the fish disease detection model.

6. The fish disease detection method according to claim 1, characterized in that: The step of acquiring the breeding site image again to obtain a second breeding site image, and performing image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image specifically includes: Acquire the breeding site image again to obtain a second breeding site image, and perform target recognition on the second breeding site image to obtain a second target recognition result; Performing image segmentation on the second breeding site image according to the second target recognition result to obtain a plurality of fish body images to be detected; Performing image feature extraction on the diseased fish image to obtain a first fish body feature, and performing image feature extraction on a plurality of the to-be-detected fish body images to obtain a second fish body feature; Calculating the similarity between the first fish body feature and the second fish body feature; When the similarity between the first fish body feature and the second fish body feature is greater than or equal to a preset similarity threshold, a fish body matching the diseased fish body in the second breeding site image is determined to obtain a target fish body; A position coordinate system of the breeding site is constructed, and the position of the target fish body is determined in the position coordinate system to obtain the position of the diseased fish body in the second breeding site image.

7. The fish disease detection method according to claim 6, characterized in that: The step of monitoring the position of the diseased fish body in the second breeding site image, obtaining the movement path of the diseased fish body, and outputting the diseased fish body image and the movement path of the diseased fish body to the client specifically includes: Determine the position of the diseased fish in the second breeding site image as a first position; Acquire images of the breeding site according to a preset time period to obtain a plurality of third breeding site images; Determine the position of the target fish body in the position coordinate system, and obtain the position of the diseased fish body in the plurality of the third breeding site images; Determine the position of the diseased fish in the third breeding site image as a second position; Connecting the first position and the second position to obtain the movement path of the sick fish; The diseased fish image and the diseased fish movement path are output to a client.

8. A fish disease detection device, characterized in that: include: A first image acquisition module is used to acquire a detection instruction, acquire an image of a breeding site according to the detection instruction, obtain a first breeding site image, and perform target recognition on the first breeding site image to obtain a first target recognition result; An image segmentation module, used for performing image segmentation on the first breeding site image according to the first target recognition result to obtain a plurality of fish body images; A fish disease detection module, used for importing a plurality of fish body images into a pre-trained fish disease detection model to identify fish bodies carrying fish diseases; A fish disease body image matching module is used to identify a fish body image carrying the fish disease body among a plurality of fish body images to obtain a fish disease body image; A second image acquisition module is used to acquire the breeding site image again to obtain a second breeding site image, and perform image matching between the second breeding site image and the fish disease body image to determine the position of the fish disease body in the second breeding site image; The diseased fish monitoring module is used to monitor the position of the diseased fish in the second breeding site image, obtain the movement path of the diseased fish, and output the diseased fish image and the movement path of the diseased fish to the client.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the fish disease detection method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the fish disease detection method according to any one of claims 1 to 7 are implemented.

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