Fry sampling and analyzing device and working method thereof

Abstract

The invention relates to a fry sampling and analyzing device. A bottom plate of an observation chamber shell is a movable plate capable of moving up and down along a guide rail; the lifting motor is fixed on the lifting motor seat; the upper part of a top rod of the lifting motor is connected with a bottom plate of the observation chamber shell; a motor fixing seat is fixedly arranged on the side face of the lifting motor seat, a horizontal double-shaft motor is fixedly arranged in the motor fixing seat, an output shaft of the double-shaft motor is coaxially connected with a gear, the gear is meshed with a rack, and the rack is fixedly connected with the gate. Gates are arranged on a pair of opposite side surfaces of the observation chamber shell; the top surface of the observation chamber shell is a transparent glass cover; the connecting arm is fixedly connected with the observation chamber shell, the orthogonal sliding shaft device is fixedly arranged on the connecting arm, and the lower part of the orthogonal sliding shaft device is fixedly connected with the electron microscope. According to the invention, full-automatic and lossless sampling and real-time analysis beside the nursery pond are realized, accurate control of nursery key nodes is facilitated, and the breeding management efficiency is improved.

Description

Technical Field

[0001] This invention relates to a fish fry sampling and analysis device and its working method, belonging to the field of fish fry sampling technology. Background Technology

[0002] With the large-scale development of the domestic fish fry breeding industry, the limitations of traditional fry sampling methods have become increasingly apparent, such as high fry loss, low sampling frequency, and high manual labor intensity. Current operations mostly rely on manual sampling and microscopic observation. Fry are easily physically damaged during the transfer from the breeding workshop to the laboratory. At the same time, the analysis results are delayed, and it is impossible to obtain data on the density of fry in the fish pond. Summary of the Invention

[0003] The purpose of this invention is to provide a fish fry sampling and analysis device that can automatically, non-destructively, and rapidly sample and analyze fish fry-related parameters on-site in the nursery pond, thereby improving the level of informatization and precision management in the nursery process.

[0004] The present invention adopts the following technical solution:

[0005] A fish fry sampling and analysis device includes a lifting motor base 1, a lifting motor 3, an observation chamber shell, a bending clamp 7, a connecting arm 18, an orthogonal sliding shaft device, and an electron microscope. One side of the observation chamber shell is fixedly connected to the bending clamp 7 and clamped and fixed to the edge of a fishpond by the bending clamp 7. The bottom plate of the observation chamber shell is a movable plate that can move up and down along a guide rail. The lifting motor 3 is fixed to the lifting motor base 1, and the upper part of the top rod of the lifting motor 3 is connected to the bottom plate of the observation chamber shell. The side of the lifting motor base 1 is fixedly equipped with... A motor mounting base 8 is provided, and a horizontal dual-axis motor 9 is fixedly installed inside the motor mounting base 8. The output shaft of the dual-axis motor 9 is coaxially connected to a gear 10. The gear 10 meshes with a rack 11, and the rack 11 is fixedly connected to a gate 12. The gate 12 is located on a pair of opposite sides of the observation chamber shell. The top surface of the observation chamber shell is a transparent glass cover 13. The connecting arm 18 is fixedly connected to the observation chamber shell, and the orthogonal sliding shaft device is fixedly installed on it. The lower part of the orthogonal sliding shaft device is fixedly connected to the electron microscope.

[0006] Preferably, the microscope is connected to a monitoring system, and the monitoring system performs data analysis based on the data collected by the electron microscope.

[0007] Preferably, the orthogonal sliding axis device includes an X-axis slide 17, the lower part of the slider of the X-axis slide 17 is fixedly connected to a Y-axis slide 16, the lower part of the slider of the Y-axis slide 16 is fixedly connected to an electron eyepiece 15 of the electron microscope, and the lower part of the electron eyepiece 15 is fixedly connected to an objective lens 14.

[0008] Preferably, the movable plate is provided with an upward-facing light source, serving as a supplementary lighting platform 4.

[0009] A method for operating the above-mentioned fish fry sampling and analysis device includes the following steps:

[0010] S1. Fix the bending clamp 7 to the outer edge of the fish pond;

[0011] S2, the dual-shaft motor 9 is activated, which drives the gates on both sides to open through gears and racks, so that the shell of the observation chamber is completely open and integrated with the aquaculture water;

[0012] S3. The lifting motor 9 is activated, driving the movable plate to move upward. The upward movement of the movable plate is set according to the sampling capability and sampling requirements of the electron microscope, and finally the reduced and determined sampling volume of the observation chamber is obtained.

[0013] S4. Adjust the plane coordinates of the electron microscope according to the set program through the orthogonal sliding shaft device, and finally obtain the physical parameters of the fish fry in the entire observation chamber volume;

[0014] S5. The image analysis system of the control system automatically analyzes the physical parameters of the fish fry.

[0015] Preferably, in step S5, the image analysis system integrates image processing and machine learning algorithms, enabling the automatic extraction and evaluation of multiple biological parameters of fish fry. Its workflow is as follows:

[0016] S51. Image Preprocessing: Preprocess the raw images acquired by the electronic eyepiece: Denoising: Eliminate image noise by using median filtering or Gaussian filtering; Contrast Enhancement: Improve the contrast between the fish fry and the background by using histogram equalization or adaptive grayscale stretching; Background Segmentation: Separate the fish fry target from the water background by using threshold segmentation or feature extraction methods based on color and texture.

[0017] S52. Fish fry contour extraction and morphological analysis: Perform contour tracking on the segmented binary image to extract the complete outline of each fish fry; calculate the geometric features of the contour, including area, perimeter, minimum bounding rectangle, and major axis direction; fit the center line based on the contour for subsequent body length measurement.

[0018] S53. Automatic body length measurement: Based on the conversion relationship between pixel size and actual size obtained in advance through the calibration plate, the extracted contour centerline length is converted into the actual body length.

[0019] S54. Intelligent Weight Estimation: The system has a built-in "body length-weight" regression model for different fish species and developmental stages. The model format is: W=aL bWhere W is weight, L is body length, and a and b are model parameters; the image analysis system automatically calls the corresponding model to estimate weight based on the identified fish fry species and measured body length; users can also import historical data to correct and optimize model parameters;

[0020] S55. Dynamic survival rate assessment: The system assesses the survival rate by analyzing the outline integrity, movement status, and gill activity of the same fry in multiple consecutive frames of images. If the fry's outline remains intact, its position changes, or its gills open and close rhythmically, it is considered alive. By combining motion trajectory analysis and morphological change monitoring, the system can distinguish between healthy, weak, or dead individuals and calculate the overall survival rate.

[0021] S56. Automatic density calculation: Based on the known sampling volume of the observation room and the number of fish fry identified in the image, the fish fry density per unit volume is automatically calculated.

[0022] S57. Growth and Development Status Assessment: The image analysis system compares the measured body length and weight data with historical records and automatically generates growth curves and trend analysis reports.

[0023] S58. Data Output and Upload: All analysis results are automatically generated into structured reports and uploaded to the central aquaculture management platform in real time; supports visual report display, anomaly warnings, and decision-making suggestions.

[0024] Preferably, in step S56, multiple samples are taken and the average value is taken to improve the accuracy of the density statistics results.

[0025] Preferably, in step S57, the feeding records, water temperature, and dissolved oxygen environmental data are combined to comprehensively assess whether the growth and development of the fish fry are normal, and potential risks are promptly identified.

[0026] The beneficial effects of this invention are as follows:

[0027] 1) It enables in-situ sampling and analysis of fish fry, improving the immediacy and convenience of testing;

[0028] 2) The sampling and testing chamber is integrated with the fish pond water without any pressure. By determining the fish fry density within the volume of the sampling and testing chamber, it can be equivalent to the fish fry density in the entire fish pond. Compared with existing testing methods, this can significantly improve the accuracy of fish fry density detection.

[0029] 3) No damage was caused to the fish fry during the entire testing process;

[0030] 4) Fully automated sampling and intelligent analysis significantly improve the data-driven and precise management of the seedling raising process in the aquaculture pond.

[0031] 5) The device completes multi-dimensional motion through a lifting motor (to determine the detection volume), a horizontal dual-axis motor (to complete the gate opening action and integrate the detection chamber with the aquaculture pond water), and an orthogonal sliding shaft device (the microscope completes the scanning in the XY plane according to the program settings). The structure is reasonable, simple, and has good reliability. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the hardware structure of the fish fry sampling and analysis device of the present invention.

[0033] Figure 2 This is a schematic diagram of the fish fry sampling and analysis device of the present invention fixed on the outer edge of the fish pond.

[0034] Explanation of reference numerals in the attached drawings: 1-Lifting motor base; 2-Observation chamber; 3-Lifting motor; 4-Supplemental lighting stage; 5-Fish pond connector; 6-Hand-tightening screw; 7-Bending clamp; 8-Motor mounting base; 9-Dual-axis motor; 10-Gear; 11-Rack; 12-Dual-side gate; 13-Glass cover; 14-Objective lens; 15-Electronic eyepiece; 16-Y-axis slide; 17-X-axis slide; 18-Connecting arm. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0036] First see Figure 2 , Figure 2 A fish pond for raising fish fry was shown. It should be noted that the length of newly hatched fry up to one week old is measured in millimeters, depending on the species. Even after one month of age, the length is only 1-5 centimeters. Therefore, the fish fry in the rearing pond are very densely packed.

[0037] See Figure 1 and combined Figure 2 A fish fry sampling and analysis device includes a lifting motor base 1, a lifting motor 3, an observation chamber shell (the internal space of which is an observation chamber 2), a bending clamp 7, a connecting arm 18, an orthogonal sliding shaft device, and an electron microscope.

[0038] See Figure 1 One side of the observation chamber shell is fixedly connected to the bending clamp 7, and is clamped and fixed to the edge of the fish pond by the bending clamp 7, such as... Figure 2 As shown.

[0039] See Figure 1 The bottom plate of the observation chamber shell is a movable plate that can move up and down along a guide rail; preferably, the movable plate is equipped with an upward-facing light source, serving as a supplementary lighting platform 4. The supplementary lighting platform can be raised to be close to the lower surface of the glass cover, thereby gently fixing the fish fry.

[0040] See Figure 1 The lifting motor 3 is fixed on the lifting motor base 1, and the upper part of the top rod of the lifting motor 3 is connected to the bottom plate of the observation chamber shell. A motor fixing seat 8 is fixedly installed on the side of the lifting motor base 1, and a horizontal dual-axis motor 9 is fixedly installed inside the motor fixing seat 8. The output shaft of the dual-axis motor 9 is coaxially connected to the gear 10, the gear 10 meshes with the rack 11, and the rack 11 is fixedly connected to the gate 12. The gate 12 is located on a pair of opposite sides of the observation chamber shell. It can be seen that the dual-axis motor 9 can drive the pair of gates 12 on the left and right sides to move laterally, thereby completely integrating the observation chamber 2 with the aquaculture pond water. The dual gates are driven by the dual-axis motor to open and close, and are used to guide and temporarily accommodate fish fry.

[0041] See Figure 1 The top surface of the observation chamber shell is a transparent glass cover 13 (the microscope can observe downwards through the glass cover 13); the connecting arm 18 is fixedly connected to the observation chamber shell, and the orthogonal sliding shaft device is fixedly installed on it, and the lower part of the orthogonal sliding shaft device is fixedly connected to the electron microscope.

[0042] Preferred, see Figure 1 The orthogonal sliding axis device includes an X-axis slide 17, the lower part of which is fixedly connected to a Y-axis slide 16, the lower part of which is fixedly connected to an electron eyepiece 15 of the electron microscope, and the lower part of which is fixedly connected to an objective lens 14. It can be understood that this orthogonal sliding axis device can move the microscope within the XY plane.

[0043] In this embodiment, the microscope is connected to a monitoring system, and the monitoring system performs data analysis based on the data collected by the electron microscope.

[0044] A miniature XY-axis motorized stage drives a coaxial objective lens to perform a two-dimensional scan along the surface of the glass cover, while an electronic eyepiece simultaneously acquires images and transmits them to an image analysis system. Based on computer vision and machine learning algorithms, this system automatically estimates the body length, weight, survival rate, density, and growth and development status of the fish fry.

[0045] The operating method of the device includes the following steps:

[0046] S1 drives the double-sided gates to open via a dual-shaft motor, guiding the fish fry into the observation room before closing the gates;

[0047] S2 starts the lifting motor and raises the supplementary lighting platform to the preset height, so that the fish fry can be gently attached to the underside of the glass cover.

[0048] S3 controls a miniature XY-axis electric slide to move the coaxial objective lens in two dimensions, which, in conjunction with the electronic eyepiece, scans and acquires images of fish fry.

[0049] The S4 image analysis system automatically processes and analyzes the acquired images, and outputs various growth parameters and statistical results.

[0050] After the S5 analysis is completed, the supplementary lighting platform is lowered, the gates on both sides are opened, and the fish fry are released.

[0051] In step 4) above, the image analysis system integrates advanced image processing and machine learning algorithms, enabling the automatic extraction and evaluation of multiple biological parameters of fish fry. Its specific workflow is as follows:

[0052] 1. Image preprocessing

[0053] Preprocessing of raw images acquired through the electronic eyepiece:

[0054] Denoising: Median filtering or Gaussian filtering is used to eliminate image noise;

[0055] Contrast enhancement: Improve the contrast between the fish fry and the background through histogram equalization or adaptive grayscale stretching;

[0056] Background segmentation: Using threshold segmentation (such as the Otsu algorithm) or feature extraction methods based on color and texture, fish fry targets are separated from the water background.

[0057] 2. Fish fry outline extraction and morphological analysis

[0058] Contour tracing is performed on the segmented binary image to extract the complete outline of each fish fry; geometric features of the contour, such as area, perimeter, minimum bounding rectangle, and major axis direction, are calculated; a center line is fitted based on the contour for subsequent body length measurement.

[0059] 3. Automatic body length measurement

[0060] Based on the conversion relationship between pixel size and actual size obtained in advance through the calibration board, the extracted contour centerline length is converted into the actual body length. The system supports multiple length definitions, such as total length and body length (from snout to tail stalk end), and can automatically switch according to user settings.

[0061] 4. Intelligent weight estimation

[0062] The system incorporates "body length-weight" regression models for different fish species and developmental stages. The model format is as follows:

[0063] W=aL b

[0064] Where W represents weight, L represents body length, and a and b are model parameters. The system automatically calls the corresponding model to estimate weight based on the identified fish fry species and measured body length. Users can also import historical data to correct and optimize the model parameters.

[0065] 5. Dynamic assessment of survival rate

[0066] The determination is made by analyzing the outline integrity, movement status, and gill activity of the same fish fry in multiple consecutive frames:

[0067] If the outline of the fry remains intact, its position changes, or its gills open and close rhythmically, it is considered alive.

[0068] By combining motion trajectory analysis and morphological change monitoring, the system can distinguish between healthy, weak, or dead individuals and calculate the overall survival rate.

[0069] 6. Automatic density calculation

[0070] Based on the known sampling volume of the observation chamber (determined by the structural dimensions) and the number of fish fry identified in the image, the fish fry density per unit volume is automatically calculated. Multiple sampling and averaging are supported to improve the reliability of the statistical results.

[0071] 7. Growth and development status assessment

[0072] The system compares the measured body length and weight data with historical records, automatically generates growth curves and trend analysis reports; it can also combine feeding records, water temperature, dissolved oxygen and other environmental data to comprehensively assess whether the fish fry are growing and developing normally, and promptly alert to potential risks.

[0073] 8. Data Output and Upload

[0074] All analysis results (including individual data and statistical indicators) are automatically generated into structured reports and uploaded to the central aquaculture management platform in real time; it supports visual report display, anomaly warnings and decision-making suggestions, thereby realizing comprehensive informatization and intelligent management of the seedling process.

[0075] This image analysis system can be deployed on a local embedded processor or a cloud server, supports online model updates and algorithm optimization, and has good scalability and adaptability, making it suitable for various fish fry breeding scenarios.

[0076] This invention enables fully automated, non-destructive sampling and real-time analysis at the edge of the seedling pond, which helps to accurately control key points in seedling cultivation and improve the efficiency of aquaculture management.

[0077] The above are preferred embodiments of the present invention. Those skilled in the art can make various modifications or improvements based on these embodiments. Without departing from the overall concept of the present invention, such modifications or improvements should fall within the scope of protection claimed by the present invention.

Claims

1. A fish fry sampling and analysis device, characterized in that: Includes a lifting motor base (1), a lifting motor (3), an observation chamber shell, a bending clamp (7), a connecting arm (18), an orthogonal sliding shaft device, and an electron microscope; One side of the observation chamber shell is fixedly connected to the bending clamp (7) and is clamped and fixed to the edge of the fish pond by the bending clamp (7); The bottom plate of the observation chamber shell is a movable plate that can move up and down along the guide rail; The lifting motor (3) is fixed on the lifting motor base (1), and the upper part of the top rod of the lifting motor (3) is connected to the bottom plate of the observation chamber shell; A motor mounting base (8) is fixedly installed on the side of the lifting motor base (1). A horizontal dual-axis motor (9) is fixedly installed inside the motor mounting base (8). The output shaft of the dual-axis motor (9) is coaxially connected to the gear (10). The gear (10) meshes with the rack (11). The rack (11) is fixedly connected to the gate (12). The gate (12) is located on a pair of opposite sides of the observation chamber shell. The top surface of the observation chamber shell is a transparent glass cover (13). The connecting arm (18) is fixedly connected to the observation chamber shell, and the orthogonal sliding shaft device is fixedly installed on it. The lower part of the orthogonal sliding shaft device is fixedly connected to the electron microscope.

2. The fish fry sampling and analysis device as described in claim 1, characterized in that: The microscope is connected to a monitoring system, which performs data analysis based on the data collected by the electron microscope.

3. The fish fry sampling and analysis device as described in claim 1, characterized in that: The orthogonal sliding axis device includes an X-axis slide (17), the lower part of the slider of the X-axis slide (17) is fixedly connected to the Y-axis slide (16), the lower part of the slider of the Y-axis slide (16) is fixedly connected to the electronic eyepiece (15) of the electron microscope, and the lower part of the electronic eyepiece (15) is fixedly connected to the objective lens (14).

4. The fish fry sampling and analysis device as described in claim 1, characterized in that: The movable plate is equipped with an upward-facing light source, which serves as a supplementary lighting platform (4).

5. A method for operating the fish fry sampling and analysis device according to any one of claims 1-4, characterized in that... The following steps are included: S1. Fix the bending clamp (7) on the outer edge of the fish pond; S2, the dual-shaft motor (9) is started, and the gates on both sides are opened through gears and racks, so that the shell of the observation chamber is completely open and integrated with the aquaculture water; S3. The lifting motor (9) is started, which drives the movable plate to move upward. The range of upward movement of the movable plate is set according to the sampling capability and sampling requirements of the electron microscope, and finally the reduced and determined sampling volume of the observation room is obtained. S4. Adjust the plane coordinates of the electron microscope according to the set program through the orthogonal sliding shaft device, and finally obtain the physical parameters of the fish fry in the entire observation chamber volume; S5. The image analysis system of the control system automatically analyzes the physical parameters of the fish fry.

6. The working method of the fish fry sampling and analysis device as described in claim 1, characterized in that: In step S5, the image analysis system integrates image processing and machine learning algorithms, enabling the automatic extraction and evaluation of multiple biological parameters of fish fry. Its workflow is as follows: S51. Image Preprocessing: Preprocess the raw images acquired by the electronic eyepiece: Denoising: Median filtering or Gaussian filtering is used to eliminate image noise; Contrast enhancement: Histogram equalization or adaptive grayscale stretching is used to enhance the contrast between the fish fry and the background; Background segmentation: Using threshold segmentation or feature extraction methods based on color and texture, the fish fry target is separated from the water background; S52. Fish fry contour extraction and morphological analysis: Perform contour tracking on the segmented binary image to extract the complete outline of each fish fry; calculate the geometric features of the contour, including area, perimeter, minimum bounding rectangle, and major axis direction. The center line is fitted based on the contour and used for subsequent body length measurement; S53. Automatic body length measurement: Based on the conversion relationship between pixel size and actual size obtained in advance through the calibration plate, the extracted contour centerline length is converted into the actual body length. S54, Intelligent Weight Estimation: The system incorporates a "body length-weight" regression model for different fish species and developmental stages, with the model format: W=aL b Where W is weight, L is body length, and a and b are model parameters; the image analysis system automatically calls the corresponding model to estimate weight based on the identified fish fry species and measured body length; users can also import historical data to correct and optimize model parameters; S55. Dynamic survival rate assessment: The system assesses the survival rate by analyzing the outline integrity, movement status, and gill activity of the same fry in multiple consecutive frames of images. If the fry's outline remains intact, its position changes, or its gills open and close rhythmically, it is considered alive. By combining motion trajectory analysis and morphological change monitoring, the system can distinguish between healthy, weak, or dead individuals and calculate the overall survival rate. S56. Automatic density calculation: Based on the known sampling volume of the observation room and the number of fish fry identified in the image, the fish fry density per unit volume is automatically calculated. S57. Growth and Development Status Assessment: The image analysis system compares the measured body length and weight data with historical records and automatically generates growth curves and trend analysis reports. S58. Data Output and Upload: All analysis results are automatically generated into structured reports and uploaded to the central aquaculture management platform in real time; It supports visual report display, anomaly warnings, and decision-making suggestion push.

7. The working method of the fish fry sampling and analysis device as described in claim 6, characterized in that: In step S56, multiple samples are taken and the average value is calculated to improve the accuracy of the density statistics results.

8. The working method of the fish fry sampling and analysis device as described in claim 6, characterized in that: In step S57, the feeding records, water temperature, and dissolved oxygen data are combined to comprehensively assess whether the growth and development of the fish fry are normal, and potential risks are promptly identified.

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