Automatic identification method and device based on golden body color of large yellow croaker

Abstract

The invention provides an automatic identification method and device based on golden body color of large yellow croaker. The method provided by the invention comprises the following steps of: setting a yellow degree identification threshold value of a color sensor by using identified large yellow croakers as markers; the method comprises the following steps: determining first light shielding time of to-be-detected pseudosciaena crocea, processing the to-be-detected pseudosciaena crocea in a dark environment for the first light shielding time length, irradiating the to-be-detected pseudosciaena crocea by using a light source in the dark environment, and detecting target reflected light; filtering the target reflected light to obtain a yellow light electric signal; calculating a target color parameter according to the yellow light electric signal; and based on the target color parameter and the yellow degree identification threshold, determining the quality information of the to-be-detected pseudosciaena crocea. The automatic identification method and device based on the golden body color of the pseudosciaena crocea are used for efficiently and accurately identifying the body color of the pseudosciaena crocea.

Description

Technical Field

[0001] The present application relates to the technical field of fish detection, and in particular to an automatic identification method and device based on the golden body color of large yellow croaker. Background Art

[0002] With the development of aquaculture and fishing, the output of yellow croaker has increased year by year, and the demand for quality assessment of yellow croaker has become increasingly urgent. As an important aquaculture and fishing target, the quality of yellow croaker is directly related to market value and consumer satisfaction. In the quality assessment of yellow croaker, body color is a key indicator, especially golden body color, which is often regarded as a symbol of high-quality yellow croaker.

[0003] Among the existing technologies for determining the body color of large yellow croaker, one existing technology is to determine the body color of large yellow croaker through observation by fishermen and farmers. Although this method is simple and intuitive, it also has the problems of strong subjectivity and inconsistent evaluation results. Especially when there are a large number of large yellow croakers and the body color differences are subtle, manual observation is difficult to ensure the accuracy and consistency of the evaluation. Therefore, technicians thought of extracting the pigment in the large yellow croaker in the laboratory to determine its body color. Although this method can accurately analyze the pigment components in the large yellow croaker, it is complicated to operate and costly, and cannot be directly applied to the rapid evaluation in actual production, and the efficiency cannot meet the actual requirements. In summary, how to automatically, quickly and accurately identify the golden body color of large yellow croaker has become a problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the present application provides an automatic identification method and device based on the golden body color of large yellow croaker, so as to efficiently and accurately identify the body color of large yellow croaker.

[0005] Specifically, the present application is implemented through the following technical solutions: The first aspect of the present application provides an automatic identification method based on the golden body color of large yellow croaker, the method comprising: Using the identified large yellow croaker as a marker, the yellow degree recognition threshold of the color sensor is set; The yellow degree recognition threshold is used to recognize different degrees of yellow, and different degrees of yellow correspond to different qualities. After receiving the reflected light of the marker after being irradiated by the light source, the yellow degree recognition threshold of the color sensor is set, including: using the color sensor to extract a first yellow reflection component from the reflected light of the marker and calculating a first reference color, and taking the first reference color as the yellow degree recognition threshold; Determine a first light-avoiding time for the large yellow croaker to be tested, subject the large yellow croaker to the first light-avoiding time in a dark environment, and the color of the large yellow croaker to be tested changes before and after the treatment; Illuminate the large yellow croaker to be tested in a dark environment to detect the reflected light of the target; Filtering the target reflected light to obtain a yellow photoelectric signal; Calculating target color parameters according to the yellow photoelectric signal; Based on the target color parameter and the yellow degree recognition threshold, the quality information of the large yellow croaker to be tested is determined.

[0006] The second aspect of the present application provides an automatic identification device based on the golden body color of large yellow croaker, the device comprising a packaging module, a transmission module, a lighting module, a shooting module, a processing module and a screening module; each module except the packaging module is arranged in the packaging module, the lighting module is fixed above the transmission module, one end of the screening module is fixed on the transmission module, and the other end of the screening module is fixed on the processing module, wherein: The packaging module is used to serve as a basic frame of the device to fix the device; The transmission module is used to move the large yellow croaker to be tested to a position where the shooting module can collect reflected light; The lighting module is used to provide light to illuminate the large yellow croaker to be tested; The shooting module is used to capture the target reflected light of the large yellow croaker to be tested; The processing module is used to filter the target reflected light to obtain a yellow photoelectric signal, and calculate the target color parameter according to the yellow photoelectric signal; The screening module is connected to a plurality of quality channels; the screening module is used to determine the quality information of the large yellow croaker to be tested based on the target color parameter and the yellow degree recognition threshold, and to place the large yellow croaker to be tested into different quality channels based on the quality information.

[0007] The automatic identification method and device based on the golden body color of large yellow croaker provided by the present application realize the automatic identification of the quality of large yellow croaker by combining the color sensor and the reasonable setting of the threshold value, that is, the automatic judgment from color to quality is realized directly according to the relationship between the value of the yellow light reflection signal and the value of large yellow croaker of different quality levels, without the need for experience, and the accuracy and consistency of the judgment are improved. At the same time, there is no need to carry out complex model training to realize the quality identification of large yellow croaker, and the identification speed is fast and the identification cost is low. Further, by treating the large yellow croaker in a dark way, it can be ensured that the body color of the large yellow croaker to be tested is fully revealed during the detection, and combined with the wide spectrum coverage, it is helpful to capture the body color information of the large yellow croaker more comprehensively, and improve the scientificity and reliability of body color identification. Further, by using the large yellow croaker with the best quality as a marker and setting the yellow degree identification threshold, the consistency and objectivity of the evaluation standard can be ensured. In this way, compared with the method of manual observation, the influence of subjective factors on the detection results is reduced, and the accuracy of body color identification is guaranteed. At the same time, compared with traditional sensors that can only identify different colors such as red, yellow, and green, the method provided by the present invention uses a common color sensor to set different yellow degree recognition thresholds according to market demand or quality standards, thereby achieving very close recognition of different degrees of yellow, and achieving accurate recognition and grading of large yellow croakers of different qualities. In this way, combining physical optics, spectral analysis, and image processing technology, the identification of the body color of the large yellow croaker to be tested is more scientific and reliable, and the target color parameters are accurately calculated through the yellow photoelectric signal and compared with the yellow degree recognition threshold, so that the quality information of the large yellow croaker to be tested can be accurately and reliably determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of Example 1 of the automatic identification method based on the golden body color of large yellow croaker provided in the present application; Figure 2 This is a schematic diagram of the structure of Example 1 of the automatic identification device based on the golden body color of large yellow croaker provided in the present application. DETAILED DESCRIPTION

[0009] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0010] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0011] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0012] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0013] The automatic identification method based on the golden body color of large yellow croaker provided in the present application can be applied to the automatic identification device based on the golden body color of large yellow croaker. In specific implementation, in a possible implementation mode, the automatic identification device based on the golden body color of large yellow croaker can move large yellow croaker through a conveyor belt, which needs to have waterproof and anti-slip functions, and then install a high-resolution camera above the conveyor belt, which is specifically used to capture the color and shape of the fish body. Further, LED lighting can be arranged around the camera to ensure that the light source is uniform to improve the accuracy of color recognition, and then an image processing unit is set to calculate the values ​​of multiple color channels based on the constructed RGB model and perform real-time image analysis. The unit can distinguish the golden body color of large yellow croaker from other colors and classify them; further, according to the results of image processing, a mechanical screening system is set. For example, a pneumatic or electric push rod is installed to push the fish to different collection boxes according to the recognition results. Further, a control panel is designed on one side of the device for the operator to monitor and adjust the equipment settings, including parameters such as sensitivity and speed. Finally, the device can be equipped with a data recording system to record the recognition results in real time for subsequent analysis and optimization.

[0014] Furthermore, live large yellow croaker appears silvery white during the day due to the aggregation of xanthophoblast pigment granules, while it appears golden yellow at night due to the diffusion of xanthophoblast pigment granules. The pigment granules of melanocytes in live large yellow croaker are diffused regardless of day or night.

[0015] In a specific implementation, in one embodiment, the change in body color of large yellow croaker is related to light. After 3 to 5 minutes of full-spectrum light (1000 lux), the body color changes from golden yellow to silvery white, and the pigment granules of yellow pigment cells shrink; after 30 to 40 minutes of shading, the body color changes from silvery white to golden yellow, and the pigment granules of yellow pigment cells diffuse. Under light, the body of large yellow croaker (except the head) is shielded by opaque material. The shielded body part is golden yellow, while the unshielded head is silvery white. The pigment granules of yellow pigment cells shrink under light of different light intensities (10 to 6000 lux). After 2 minutes of blue light (435 nm) irradiation, the body color of large yellow croaker changes from golden yellow to silvery white, and the degree of contraction of yellow pigment cell pigment granules is the greatest under blue light.

[0016] Figure 1 This is a flow chart of Example 1 of the automatic identification method based on the golden body color of large yellow croaker provided in this application. Please refer to Figure 1 , the method provided in this embodiment may include: S101. Using the identified large yellow croaker as a marker, a yellow degree recognition threshold of a color sensor is set.

[0017] Among them, the yellow degree recognition threshold is used to identify different degrees of yellow, and different degrees of yellow correspond to different qualities. In a closed dark environment, the reflected light of the marker is received after being irradiated with illumination light. The yellow degree recognition threshold of the color sensor is set, including: using the color sensor to extract the first yellow reflection component from the reflected light of the marker and calculate the first reference color, and taking the first reference color as the yellow degree recognition threshold.

[0018] Specifically, the identified large yellow croaker is a large yellow croaker that has been manually or preliminarily automatically screened, and the identified large yellow croaker includes the body color information of the large yellow croaker. It should be noted that the body color information of the large yellow croaker is golden yellow, and the better the quality of the large yellow croaker, the more obvious the golden yellow; while the golden yellow of the large yellow croaker with poor quality is not obvious. For example, in one embodiment, the large yellow croaker with the best quality has a golden color feature, and the large yellow croaker with poor quality has a body color mixed with silver and golden.

[0019] Furthermore, the marker is a reference for the yellow level recognition threshold, which is used for subsequently setting the yellow level recognition threshold of the color sensor.

[0020] It should be noted that the marker can be the best quality large yellow croaker; or large yellow croakers of different grades can be classified according to their different body colors as markers. For example, in one embodiment, low-quality large yellow croakers, ordinary large yellow croakers, and high-quality large yellow croakers can be determined as markers.

[0021] Furthermore, the color sensor is used for a device that can sense the spectral characteristics of light reflected, transmitted or emitted by an object and determine the color of the object accordingly. In a specific implementation, the color sensor is used to capture the reflected light of the large yellow croaker.

[0022] Furthermore, the yellow degree recognition threshold is a preset color range or standard for distinguishing large yellow croakers of different qualities. In specific implementation, by setting the yellow degree recognition threshold, the color sensor can recognize large yellow croakers that meet or are close to the yellow degree recognition threshold, thereby judging their quality.

[0023] It is understandable that when screening the quality of large yellow croaker, one tends to obtain large yellow croaker with a body color close to that of the best quality large yellow croaker, so the best quality large yellow croaker is determined as the marker.

[0024] Furthermore, the first yellow reflection component is a spectral component related to yellow extracted from the reflected light of the marker, and then quantified by a color sensor to determine the first yellow reflection component.

[0025] Furthermore, the first reference color is a color value calculated based on the first yellow reflection component, which serves as a reference for the yellow degree recognition threshold. It should be noted that, since the marker is the best quality large yellow croaker, the first reference color represents the body color characteristics of the best quality large yellow croaker.

[0026] A specific embodiment is given below to introduce the process of obtaining the first reference color in detail: (1) Acquiring a first yellow reflection component of the marker in a dark environment based on the color sensor.

[0027] Specifically, a dark environment is an environment without interference from natural light or artificial light. In a dark environment, the color sensor can more accurately capture the color characteristics of the large yellow croaker itself and avoid the influence of other light on the recognition results.

[0028] In specific implementation, in a dark environment, the spectral component related to yellow captured by the color sensor from the best quality large yellow croaker is the first yellow reflection component, which is the basis for subsequent color analysis and recognition.

[0029] (2) Obtaining a first color parameter based on the first yellow reflection component to generate the first reference color.

[0030] Specifically, the first color parameter is a color characteristic value calculated based on the first yellow reflection component and used to describe the large yellow croaker. In specific implementation, the hue, saturation, brightness, etc. corresponding to the first reflection component of the large yellow croaker with the best quality are obtained through the first yellow reflection component.

[0031] It can be understood that the body color characteristics of the best quality large yellow croaker can be described by the first color parameter.

[0032] The automatic recognition method based on the golden body color of large yellow croaker provided in this embodiment uses large yellow croaker with the best quality as a marker, and the generated first reference color provides a stable benchmark for the subsequent recognition process, which helps the color sensor to maintain a consistent judgment standard in subsequent recognition tasks and reduce misjudgments and missed judgments. Furthermore, the recognition threshold set based on the first reference color enables the color sensor to automatically identify and screen large yellow croakers that meet the standards, thereby improving the screening efficiency and accuracy and reducing costs and error rates.

[0033] Furthermore, in the threshold adjustment mode of the color sensor, the lower limit of the yellow degree recognition threshold can be determined based on the color parameter of the marker. Then, the large yellow croaker is continuously detected, and the actual body color of the detected large yellow croaker is used as the color parameter of the upper limit of the yellow degree recognition threshold, and the upper limit of the yellow degree recognition threshold is set based on this.

[0034] In specific implementation, enter the yellow level recognition threshold adjustment mode, press a specific key or combination of keys until the sensor enters the adjustment state, at which point the sensor will display a specific indicator light or screen information to indicate that it is currently in the adjustment mode. Then, in the yellow level recognition threshold adjustment mode, use the color sensor to scan the marker (the marker can be a large yellow croaker with a known body color, a color card with a known color, or a large yellow croaker scale with a known color), press the "ON" key (or a similar "set" key) to make the sensor remember this color as a reference color, at which point the "GOOD" green indicator light may flash, indicating that the lower limit of the yellow level recognition threshold is being set. Next, this usually involves pressing the "OFF" key (or a similar "adjustment" key) to set the upper limit of the yellow level recognition threshold. During the adjustment process, the yellow level recognition threshold can be properly set by observing the flashing state of the "GOOD" and "ERR" indicators. If the "GOOD" indicator light is always on, it means that the current color is within the yellow level recognition threshold range; if the "ERR" indicator light flashes, it means that the current color exceeds the yellow level recognition threshold range. According to the actual situation, gradually adjust the upper limit of the yellow degree recognition threshold until the required yellow color of large yellow croaker can be accurately identified. Then, after adjusting the yellow degree recognition threshold, use multiple large yellow croaker samples of different colors for testing to verify whether the yellow degree recognition threshold of the color sensor is set accurately.

[0035] It should be noted that by setting an upper limit of the yellow level recognition threshold, it is possible to prevent the color sensor from misidentifying other colors.

[0036] Further, when detecting the body color of large yellow croaker, large yellow croaker is placed in a dark environment, so that there is no interference from ambient light. When the environment is not a dark environment, it is necessary to remove the interference of ambient light through adjustment.

[0037] Optionally, based on the difference between the real-time background illumination information and the standard background illumination information, the illumination data to be adjusted is generated; The color sensor is adjusted in combination with the illumination data to be adjusted.

[0038] Specifically, the real-time background illumination information is data of the surrounding illumination conditions captured by the color sensor in the real-time working environment. In specific implementation, the real-time background illumination information may include parameters such as illumination intensity and spectral distribution, and the real-time background illumination information directly affects the recognition result of the color sensor on the body color of the large yellow croaker.

[0039] Furthermore, the standard background illumination information is data of the most favorable illumination conditions for the body color recognition of large yellow croaker, which is preset or determined through multiple measurements. In specific implementation, the standard background illumination information can be determined based on a dark environment without light.

[0040] Furthermore, the illumination data to be adjusted is data calculated based on the difference between the real-time background illumination information and the standard background illumination information and used to adjust the illumination conditions. It should be noted that the illumination data to be adjusted may include parameters such as the illumination intensity to be increased or decreased and the spectral distribution to be adjusted.

[0041] Furthermore, the target controller determines the illumination data to be adjusted through the gap, and adjusts the recognition parameters of the color sensor accordingly, so that the color sensor can work in an environment closer to the standard background illumination information.

[0042] Furthermore, the illumination data to be adjusted can be determined based on the following formula: ; Among them, Y is the parameter value after calibration, X n Different color features read by the color sensor, a n is the corresponding weight parameter, and b is the calibration bias. It should be noted that the system error of the color sensor is compensated based on the calibration bias.

[0043] Optionally, generating the illumination data to be adjusted, and adjusting the color sensor in combination with the illumination data to be adjusted includes: Detecting the illumination difference between the real-time background illumination and the standard background illumination, and calculating the ambient yellow light reflection value according to the illumination difference; Calculate the yellow light reflection change value of the large yellow croaker under the real-time background illumination according to the change relationship between the color of the large yellow croaker to be tested and the light intensity; Utilizing the color sensor to detect the yellow photoelectric signal of the large yellow croaker to be tested; Subtracting the electrical signals corresponding to the ambient yellow light reflection value and the yellow light reflection change value of the large yellow croaker from the yellow photoelectric signal to obtain a calibrated yellow photoelectric signal; The quality information of the large yellow croaker to be tested is determined based on the calibrated yellow photoelectric signal.

[0044] The automatic identification method based on the golden body color of large yellow croaker provided in this embodiment can eliminate or reduce the influence of external environmental illumination changes on the detection results of the color sensor by detecting the illumination difference between the real-time background illumination and the standard background illumination and accurately calculating the ambient yellow light reflection value. It ensures that the color sensor can accurately capture the yellow light reflection characteristics of the large yellow croaker itself under different illumination conditions, thereby improving the accuracy and stability of the detection. Furthermore, according to the changing relationship between the color of the large yellow croaker and the illumination intensity, the influence of illumination changes on the body color reflection characteristics of the large yellow croaker is taken into account, and the true body color of the large yellow croaker under specific illumination conditions can be more accurately reflected, thereby improving the accuracy of the quality information determination. Furthermore, through the calibrated yellow photoelectric signal, the interference of ambient light and illumination changes on the detection results is eliminated, so that the final detection results are closer to the yellow light reflection characteristics of the large yellow croaker itself, providing reliable data support for the subsequent quality information determination.

[0045] The automatic recognition method based on the golden body color of large yellow croaker provided in this embodiment ensures that the color sensor always adapts to the current environment by real-time monitoring and adjusting the lighting conditions, thereby reducing the color recognition error caused by changes in lighting conditions. This dynamic adjustment mechanism enables the color sensor to more accurately capture the golden body color characteristics of large yellow croaker. Furthermore, in the face of working environments under different lighting conditions, the parameters of the color sensor are automatically adjusted through the target controller, so that even in complex and changeable lighting environments, the measurement work can be completed stably, thereby improving the reliability of large yellow croaker body color detection.

[0046] S102, determining a first light-avoiding time for the large yellow croaker to be tested, subjecting the large yellow croaker to be tested to a first light-avoiding time length in a dark environment, and causing the color of the large yellow croaker to be tested to change before and after the treatment.

[0047] Specifically, the first light-avoiding time is a suitable light-avoiding time length determined according to the color change mechanism of the large yellow croaker. It is understandable that the first light-avoiding time needs to be long enough to ensure that the yellow pigment cell pigment particles of the large yellow croaker can be fully diffused, and at the same time, the first light-avoiding time cannot be too long to avoid adverse effects on the large yellow croaker.

[0048] It should be noted that when large yellow croaker is protected from light, its body color will change from silvery white to golden yellow. In other words, if the body surface color of large yellow croaker changes from silvery white to golden yellow, it means that protecting from light allows the xanthophorus pigment particles to fully diffuse.

[0049] In specific implementation, the large yellow croaker to be tested is placed in a dark environment and treated according to a pre-set first light-avoiding time. During the treatment process, it is necessary to ensure that the large yellow croaker will not be stimulated by any external light.

[0050] A specific embodiment is given below to introduce the process of obtaining the first light-avoiding time in detail: (1) Detecting the color change of the large yellow croaker to be tested in the dark environment, and recording the time point when the color of the large yellow croaker to be tested stops changing.

[0051] Specifically, in a dark environment, the body surface color of the tested large yellow croaker gradually changes from silvery white to golden yellow. This change is caused by the diffusion of xanthophoric pigment granules in the absence of light stimulation.

[0052] In specific implementation, when the color of the large yellow croaker to be tested stops changing, the time point at this time is recorded.

[0053] (2) The time from when the large yellow croaker to be tested enters the dark environment to when the color stops changing is determined as the first light-avoiding time.

[0054] In specific implementation, the duration of the body color of the large yellow croaker to be tested stops changing is determined as the first light-avoiding time. After the first light-avoiding time, the large yellow croaker can reach the best body color state under the same conditions. In specific implementation, the time point when the color change amplitude of the large yellow croaker to be tested is less than the predetermined amplitude within a certain time can be determined as the time point when the color of the large yellow croaker to be tested stops changing. For example, in one embodiment, the certain time is set to 1 minute, and the preset amplitude is set to an extremely small amplitude. When the color change of the large yellow croaker to be tested is less than the preset amplitude within 1 minute, it can be determined that the color of the large yellow croaker to be tested stops changing.

[0055] The automatic identification method based on the golden body color of large yellow croaker provided in the present embodiment accurately extracts the time required for large yellow croaker to complete color change in the dark, and determines the first light-avoiding time based on this, which can ensure that large yellow croaker reaches the best body color state before color recognition, avoids the body color difference caused by insufficient or too long processing time, thereby improving the accuracy of color recognition. Further, each large yellow croaker is processed according to the first light-avoiding time, so that all large yellow croakers are in the same state during color evaluation, which helps to eliminate the influence of individual differences on the screening results and ensure the consistency of the screening results. Further, through the accurate first light-avoiding time, the adverse consequences caused by insufficient or excessive light-shielding time can be avoided, and the reliability and scientificity of large yellow croaker body color detection are improved.

[0056] S103, irradiating the large yellow croaker to be tested in a dark environment to detect target reflected light.

[0057] Specifically, the irradiation light is a mixture of multiple colors, including all colors in the visible spectrum, such as red, orange, yellow, green, blue, indigo, and purple. In specific implementation, a white light source (such as a white LED light) is used to illuminate the large yellow croaker to be tested in a dark environment. Through a wide spectrum coverage, the color sensor can capture more details.

[0058] Furthermore, when the irradiation light is irradiated to the large yellow croaker to be tested, the large yellow croaker's body surface will reflect part of the light, and the reflected light contains information about the large yellow croaker's body color, that is, the intensity and distribution of light of different wavelengths. The target reflected light is detected by a color sensor.

[0059] A specific embodiment is given below to introduce the specific implementation process of this step in detail: (1) A dark environment is determined as the dark environment.

[0060] In the specific implementation, an environment with extremely weak light or no light is selected, so that the color sensor cannot capture enough light signals from the surface of the large yellow croaker to be tested. In this environment, the large yellow croaker to be tested can be guaranteed to be in a lightless environment, which is convenient for the change of the body color of the large yellow croaker to be tested.

[0061] (2) A full-spectrum light source is determined as the illumination light, and the large yellow croaker to be tested is illuminated in the dark environment based on the illumination light.

[0062] Specifically, a full-spectrum light source can emit light containing all colors in the visible spectrum. The light emitted by the full-spectrum light source has a wide spectral coverage and can simulate natural light or provide sufficient lighting conditions so that the color sensor can capture the complete color information of the object.

[0063] In a specific implementation, irradiating the large yellow croaker to be tested can be achieved by using a white LED lamp or other types of irradiation light sources.

[0064] The automatic identification method based on the golden body color of large yellow croaker provided in this embodiment ensures that the large yellow croaker to be tested can obtain sufficient and uniform illumination in a dark environment through a full-spectrum light source, and can obtain stable target reflected light. Furthermore, using illuminating light to illuminate the large yellow croaker to be tested in a dark environment can significantly reduce the interference of ambient light. The traditional method of using natural light may affect the recognition results of the color sensor, and the method provided in this application can effectively eliminate these interference factors and improve the accuracy of color recognition by creating a dark environment that is close to no light. Furthermore, by adjusting the brightness and angle of the light source, it can adapt to large yellow croaker screening scenes of different scales and different lighting conditions, and improve the reliability of large yellow croaker body color detection.

[0065] S104, filtering the target reflected light to obtain a yellow photoelectric signal.

[0066] Specifically, the target reflected light is light that is reflected back by the large yellow croaker to be tested due to the irradiation light being irradiated on the large yellow croaker to be tested, and contains the body color information of the large yellow croaker.

[0067] Furthermore, by filtering the target reflected light, the spectral components related to the body color (golden yellow) of the large yellow croaker can be screened out. In specific implementation, a suitable filter can be selected according to the spectral characteristics of the yellow color of the large yellow croaker to be tested to screen out the spectral components related to yellow.

[0068] It should be noted that the quality of the filter needs to be ensured to avoid color recognition errors caused by filter quality problems.

[0069] A specific embodiment is given below to introduce the determination process of the yellow photoelectric signal in detail: (1) Filtering the reflected light with a filter to obtain light containing only yellow light wavelengths.

[0070] Specifically, the wavelength or frequency corresponding to the golden light is selected from the reflected light. In specific implementation, the reflected light is filtered through a filter, which only allows light with yellow wavelength to pass through and blocks light with other wavelengths, thereby obtaining light containing only yellow wavelength.

[0071] Furthermore, the sensor lens and filter should be cleaned regularly to prevent dust and dirt from affecting the accuracy of color recognition.

[0072] (2) Based on the photoelectric converter receiving light of yellow light wavelength, the current change of the photoelectric converter is obtained.

[0073] Specifically, the photoelectric converter is a device that can convert an optical signal into an electrical signal. In other words, the photoelectric converter is used to receive filtered yellow light wavelength light and convert it into an electrical signal for subsequent processing and analysis.

[0074] In specific implementation, the photoelectric converter can be determined as a photodiode. It should be noted that a high-sensitivity, low-noise photodiode can be selected to improve the accuracy of photoelectric conversion. It should be noted that when light shines on the photodiode, the energy of the photon is absorbed by the electron, resulting in a change in current. This change is related to the intensity and wavelength of the light. The golden wavelength is about 550 nanometers.

[0075] Furthermore, the electrical signal output by the photodiode can be processed with high precision, including amplification, filtering, etc., to reduce signal noise and interference.

[0076] (3) Determine the yellow photoelectric signal based on the current change.

[0077] Specifically, the yellow photoelectric signal refers to an electrical signal that contains only yellow light wavelength information after filtering and photoelectric conversion processing.

[0078] In a specific implementation, based on the monitored current change, a yellow photoelectric signal is determined through processing and analysis based on a high-resolution A / D converter.

[0079] Furthermore, after obtaining the yellow photoelectric signal, a microcontroller with stable performance and fast processing speed can be selected to perform real-time processing and analysis on the yellow photoelectric signal.

[0080] The automatic recognition method based on the golden body color of large yellow croaker provided in this embodiment filters out unnecessary spectral components through a filter, retaining only the light wavelengths related to yellow, significantly reducing interference factors in the color recognition process, thereby improving the accuracy of color recognition. Furthermore, the filtering process ensures that the signal received by the color sensor mainly comes from the golden body color of large yellow croaker, reducing interference from external factors such as ambient light and reflected light of other colors. Furthermore, the combination of the filter and the photoelectric converter makes the color recognition system more adaptable to changes in lighting conditions. Even when the light intensity is unstable or the light angle changes, the screening effect of the filter can still maintain stable reception of yellow light wavelengths.

[0081] A specific embodiment is given below to introduce the filtering process in detail: (1) Obtain the average spectral characteristics of a specified number of large yellow croakers.

[0082] Specifically, the specific value of the specified quantity is determined according to actual needs and is not limited in this embodiment.

[0083] Furthermore, the average spectral characteristics refer to the average reflection or transmission characteristics of these samples at each wavelength of the spectrum calculated after measuring the spectral data of a specified number of large yellow croakers.

[0084] It should be noted that by averaging the spectral characteristics, the influence of individual differences of large yellow croaker on the spectral data can be reduced, and more representative spectral characteristics of large yellow croaker can be obtained.

[0085] (2) Inputting the average spectral characteristics into a correspondence table between spectral information and filter models to determine the optimal filter model.

[0086] Specifically, the correspondence table of spectral information and filter models is a pre-established table, which contains the correspondence between different spectral characteristics and the best filter models. By querying this table, the best filter model suitable for filtering the target reflected light can be quickly determined based on the average spectral characteristics of large yellow croaker.

[0087] Furthermore, the optimal filter can effectively screen out the light wavelengths related to the golden body color of large yellow croaker and filter out other interfering light wavelengths.

[0088] (3) Filtering the target reflected light based on the optimal filter.

[0089] In the specific implementation, the best filter is selected and installed. When the target reflected light of the large yellow croaker passes through the best filter, the best filter will screen out the light wavelengths related to the golden body color of the large yellow croaker and allow these lights to pass through. Other light wavelengths that are not related to identification will be blocked by the filter.

[0090] The automatic identification method based on the golden body color of large yellow croaker provided in this embodiment can reduce the fluctuation of spectral data caused by individual differences by obtaining the average spectral characteristics of multiple large yellow croakers, making the subsequent color identification more accurate. Further, by selecting the best filter model according to the average spectral characteristics, it is possible to more effectively filter out light wavelengths that are not related to the golden body color of large yellow croaker, and reduce interference factors in the color recognition process. Further, due to the use of a filter selection method based on spectral characteristics, it is possible to adjust the spectral characteristics of large yellow croaker samples from different batches and different growth environments, thereby ensuring the accuracy and reliability of large yellow croaker body color recognition.

[0091] S105, calculating target color parameters according to the yellow photoelectric signal.

[0092] Specifically, the target color parameters are used to describe and quantify a set of values ​​of the golden body color characteristics of large yellow croaker. In specific implementation, the target color parameters may include hue, saturation, brightness, etc., which together constitute the color space representation of the large yellow croaker body color.

[0093] In this step, the processed electrical signal is further analyzed using a color recognition algorithm, which calculates the target color parameters based on the intensity and waveform of the yellow photoelectric signal.

[0094] A specific embodiment is given below to introduce the process of obtaining the target color parameters in detail: (1) Obtaining spectrum information of different wavelengths of the yellow photoelectric signal; each of the spectrum information has a corresponding preset number of color components.

[0095] Specifically, spectral information refers to the intensity and distribution characteristics of the yellow photoelectric signal at different wavelengths.

[0096] It should be noted that the specific value of the preset number is set according to actual needs, and is not limited in this embodiment. For example, in one embodiment, the preset number is 3.

[0097] In specific implementation, the color component is the basic unit that constitutes the color space, such as the red color component, the green color component, and the blue color component.

[0098] Furthermore, the spectral information can be calculated by the following formula: S(λ)=[S(λ1), S(λ2),..., S(λ n )]; Where λ represents the wavelength and S(λ) represents the vector of spectral information.

[0099] (2) Adding the spectral information contained in each of the color components, and calculating the component parameter value corresponding to the color component.

[0100] In specific implementation, for each color component, the spectral information contained in it is added to calculate the parameter value of the color component. This parameter value reflects the intensity and characteristics of a color component in the body color of large yellow croaker.

[0101] (3) Fusing the component parameter values ​​to obtain the target color parameter.

[0102] In specific implementation, the target color parameters can be determined according to the following formula: ; Wherein, R is the red color component, G is the green color component, B is the blue color component, λ is the wavelength data, and R λ , the G λ and the B λ They are the wavelength index sets corresponding to the red, green, and blue channels respectively.

[0103] It should be noted that RGB is an additive color model that produces other colors based on different intensities of the three basic colors of red, green, and blue. In specific implementation, the color of each pixel is mixed by different proportions of these three colors. In the process of identifying the golden body color of large yellow croaker, RGB can be used to capture the red, green, and blue component values ​​of the yellow target reflected light in the large yellow croaker image for further color analysis and processing.

[0104] In the process of identifying the golden body color of large yellow croaker, the RGB model may be used to capture the red, green, and blue component values ​​of each pixel in the large yellow croaker image, which can then be used for further color analysis and processing.

[0105] The automatic identification method based on the golden body color of large yellow croaker provided in this embodiment can more accurately identify the target color parameters by accurately obtaining the spectral information of the yellow light component in the body color of large yellow croaker and performing detailed analysis and calculation on it. Furthermore, by fusing the parameter values ​​of each color component to obtain the target color parameters, a more accurate and scientific screening standard can be formulated, thereby improving the accuracy and effectiveness of screening.

[0106] S106. Determine the quality information of the large yellow croaker to be tested based on the target color parameter and the yellow degree recognition threshold.

[0107] Specifically, the quality information refers to the quality assessment result of the large yellow croaker obtained after color recognition and processing. It should be noted that the yellower the golden body color of the large yellow croaker is, the higher its quality information is. In specific implementation, the specific data type included in the quality information is determined according to actual needs, and this embodiment is not limited to this. For example, the quality information can be set to high quality and ordinary.

[0108] In specific implementation, the target color parameter of the large yellow croaker to be tested is compared with the yellow degree recognition threshold. If the target color parameter reaches the yellow degree recognition threshold, the quality of the large yellow croaker is considered to be "high quality". If the target color parameter is lower than the yellow degree recognition threshold, the quality of the large yellow croaker is considered to not meet the high quality standard, and its quality information is recorded as "ordinary".

[0109] It should be noted that during the detection process of large yellow croaker, the color detector can be adjusted in real time based on the PID controller to make the detection result more accurate.

[0110] In specific implementation, the color sensor can be adjusted in real time based on the following formula: ; Among them, u(t) is the control output (such as the sensor adjustment value), e(t) is the error between the expected value and the actual value, Kp is the proportional gain, Ki is the integral gain, Kd is the differential gain, and ∫e(τ)dτ is the integral of the error between the expected value and the actual value over time. It should be noted that Kp reflects the direct impact of the deviation on the control quantity; Ki eliminates static errors by accumulating deviations; Kd predicts future behavior based on the rate of change of the deviation; ∫e(τ)dτ represents past deviations.

[0111] The automatic identification method based on the golden body color of large yellow croaker provided in this embodiment realizes the automatic identification of the golden body color of large yellow croaker through the color sensor and the subsequent processing of the yellow photoelectric signal. Compared with the traditional manual screening method, the screening efficiency is improved, and the labor cost and error are reduced. Furthermore, the spectral characteristics of the large yellow croaker body color are accurately sensed by the color sensor, and the target color parameters are calculated by the algorithm, so that the identification result of the large yellow croaker body color is more accurate and reliable. Furthermore, by setting the yellow degree recognition threshold, it is ensured that the large yellow croaker screened meets a certain standard in body color, thereby ensuring the reliability and scientificity of the acquisition of large yellow croaker quality information. In this way, the quality information of large yellow croaker can be accurately and reliably determined according to the target color parameters and the yellow degree recognition threshold.

[0112] Corresponding to the aforementioned embodiment of an automatic identification method based on the golden body color of large yellow croaker, the present application also provides an embodiment of an automatic identification device based on the golden body color of large yellow croaker.

[0113] Figure 2 This is a schematic diagram of the structure of the first embodiment of the automatic identification device based on the golden body color of large yellow croaker provided by this application. Figure 2 The device provided in this embodiment includes a packaging module 210, a transmission module 220, a lighting module 230, a shooting module 240, a processing module 250, a screening module 260, a control module 270 and a recording module 280; each module except the packaging module 210 is arranged in the packaging module 210, the lighting module 230 is fixed above the transmission module 220, the shooting module 240 is connected to the lighting module 230, the processing module 250 is connected to the shooting module 240, one end of the screening module 260 is fixed on the transmission module 220, and the other end of the screening module 260 is fixed on the processing module 270, the control module 270 is connected to the processing module 250, and the recording module 280 is connected to the processing module 250, wherein: The packaging module 210 is used as the basic framework of the device, packaging all other parts of the device and fixing the device; The transmission module 220 is used to move the large yellow croaker to be tested to a position where the shooting module can collect reflected light; The lighting module 230 is used to provide a light source to illuminate the large yellow croaker to be tested; The shooting module 240 is used to capture the target reflected light of the large yellow croaker to be tested; The processing module 250 is used to filter the target reflected light to obtain a yellow photoelectric signal, and calculate the target color parameter according to the yellow photoelectric signal; The screening module 260 is used to determine the quality information of the large yellow croaker to be tested based on the target color parameter and the yellow degree recognition threshold, and classify the large yellow croaker to be tested based on the quality information; The control module 270 is used to adjust the parameters of the device based on the adjustment information; The recording module 280 is used to record the classification result of the large yellow croaker to be tested.

[0114] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0115] Furthermore, solid metal material is used as the basic frame of the device to ensure stability and durability.

[0116] Furthermore, the transport module 220 is used to move the large yellow croaker and needs to have waterproof and anti-slip functions.

[0117] Further, the shooting module 240 can be a high-resolution camera installed above the transmission module 220, which is specially used to capture the color and shape of the fish body. It is conceivable to use a multi-angle camera to obtain comprehensive image data.

[0118] Furthermore, the lighting module 230 may be an LED lighting device arranged around the shooting module 240 to ensure uniform light source to improve the accuracy of color recognition.

[0119] Furthermore, the processing module 250 uses a machine learning algorithm to perform real-time image analysis. The unit can distinguish the golden body color of large yellow croaker from other colors and classify them.

[0120] Furthermore, the control module 270 can be a control panel designed on one side of the device for the operator to monitor and adjust the device settings, including parameters such as sensitivity and speed. In specific implementation, an intuitive and easy-to-use user interface can be designed to facilitate users to view color data, set parameters, and receive alarm information.

[0121] Furthermore, the screening module 260 may be a mechanical screening system arranged according to the result of image processing. In specific implementation, the screening module 260 may be equipped with a pneumatic or electric push rod to push the fish to different collection boxes according to the recognition result.

[0122] Furthermore, the recording module 280 is equipped with a data recording system to record the recognition results in real time for subsequent analysis and optimization.

[0123] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0124] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0125] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An automatic identification method based on the golden body color of large yellow croaker, characterized in that: The method comprises: Using the identified large yellow croaker as a marker, the yellow degree recognition threshold of the color sensor is set; The yellow level recognition threshold is used to recognize different levels of yellow, and different levels of yellow correspond to different qualities. After receiving the reflected light of the marker after being irradiated by the light source, the yellow level recognition threshold of the color sensor is set, including: using the color sensor to extract a first yellow reflection component from the reflected light of the marker and calculating a first reference color, and taking the first reference color as the yellow level recognition threshold; Determine a first light-avoiding time for the large yellow croaker to be tested, subject the large yellow croaker to the first light-avoiding time in a dark environment, and the color of the large yellow croaker to be tested changes before and after the treatment; Illuminate the large yellow croaker to be tested in a dark environment to detect the reflected light of the target; Filtering the target reflected light to obtain a yellow photoelectric signal; Calculating target color parameters according to the yellow photoelectric signal; Based on the target color parameter and the yellow degree recognition threshold, the quality information of the large yellow croaker to be tested is determined.

2. The method according to claim 1, characterized in that The step of determining a first light-avoiding time for the large yellow croaker to be tested, and subjecting the large yellow croaker to be tested to a first light-avoiding time length in a dark environment, comprises: Detecting the change in color of the large yellow croaker to be tested under the dark environment, and recording the time point when the color of the large yellow croaker to be tested stops changing; The time from when the large yellow croaker to be tested enters the dark environment to when the color stops changing is determined as the first light-avoiding time.

3. The method according to claim 1, characterized in that The step of irradiating the large yellow croaker to be tested in a dark environment and detecting target reflected light comprises: Determine a dark environment as the dark environment; A full-spectrum light source is determined as the illumination light, and the large yellow croaker to be tested is illuminated in the dark environment based on the illumination light.

4. The method according to claim 1, characterized in that: The filtering of the target reflected light to obtain a yellow photoelectric signal comprises: Filtering the reflected light based on a filter to obtain light containing only yellow light wavelengths; Based on the photoelectric converter receiving light of yellow light wavelength, obtaining a current change of the photoelectric converter; The yellow photoelectric signal is determined based on the current change.

5. The method according to claim 1, characterized in that: The calculating the target color parameter according to the yellow photoelectric signal comprises: Acquire spectrum information of different wavelengths of the yellow photoelectric signal; each spectrum information has a corresponding preset number of color components; Adding the spectral information contained in each of the color components, and calculating the component parameter value corresponding to the color component; The component parameter values ​​are fused to obtain the target color parameter.

6. The method according to claim 1, characterized in that The method of using the identified large yellow croaker as a marker to set the yellow degree recognition threshold of the color sensor includes: Acquire a first yellow reflection component of the marker in a dark environment based on the color sensor; A first color parameter is acquired based on the first yellow reflection component to generate the first reference color.

7. The method according to claim 1, characterized in that The filtering of the target reflected light comprises: Get the average spectral characteristics of a specified number of large yellow croakers; Inputting the average spectral characteristics into a correspondence table of spectral information and filter models to determine the model of the best filter; The target reflected light is filtered based on the optimal filter.

8. The method according to claim 1, characterized in that Before setting the yellow level recognition threshold of the color sensor, the method further includes: Based on the gap between the real-time background illumination information and the standard background illumination information, the illumination data to be adjusted is generated; The color sensor is adjusted in combination with the light data to be adjusted.

9. The method according to claim 8, characterized in that The step of generating the illumination data to be adjusted and adjusting the color sensor in combination with the illumination data to be adjusted includes: Detecting the illumination difference between the real-time background illumination and the standard background illumination, and calculating the ambient yellow light reflection value according to the illumination difference; Calculate the yellow light reflection change value of the large yellow croaker under the real-time background illumination according to the change relationship between the color of the large yellow croaker to be tested and the light intensity; Utilizing the color sensor to detect the yellow photoelectric signal of the large yellow croaker to be tested; Subtracting the electrical signals corresponding to the ambient yellow light reflection value and the yellow light reflection change value of the large yellow croaker from the yellow photoelectric signal to obtain a calibrated yellow photoelectric signal; The quality information of the large yellow croaker to be tested is determined based on the calibrated yellow photoelectric signal.

10. An automatic identification device based on the golden body color of large yellow croaker, characterized in that: The device is applied to the method according to any one of claims 1 to 9, and the device comprises a packaging module, a transmission module, a lighting module, a shooting module, a processing module and a screening module; each module except the packaging module is arranged in the packaging module, the lighting module is fixed above the transmission module, one end of the screening module is fixed on the transmission module, and the other end of the screening module is fixed on the processing module, wherein: The packaging module is used to serve as a basic frame of the device to fix the device; The transmission module is used to move the large yellow croaker to be tested to a position where the shooting module can collect reflected light; The lighting module is used to provide light to illuminate the large yellow croaker to be tested; The shooting module is used to capture the target reflected light of the large yellow croaker to be tested; The processing module is used to filter the target reflected light to obtain a yellow photoelectric signal, and calculate the target color parameter according to the yellow photoelectric signal; The screening module is connected to a plurality of quality channels; the screening module is used to determine the quality information of the large yellow croaker to be tested based on the target color parameter and the yellow degree recognition threshold, and to place the large yellow croaker to be tested into different quality channels based on the quality information.

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