Goose egg laying and egg quality detection method based on machine recognition

Abstract

The invention discloses a goose egg laying and egg quality detection method based on machine recognition. The method comprises the steps that the initial weight of an egg laying area in different time periods, the goose egg weight and the goose weight corresponding to a goose egg are obtained; based on the goose egg contour line and the goose egg weight, obtaining a goose egg weight parameter; based on the information identifier of the goose entering the egg laying area, obtaining the goose egg weight parameter and a goose weight data set corresponding to the goose egg; and predicting goose egg weight parameters of the gooses in the egg laying area based on the expression state of the gooses entering the egg laying area and the goose weight data set corresponding to the goose eggs to obtain predicted goose egg weight parameters, and determining the egg laying time of the gooses corresponding to the goose eggs based on the goose egg weight parameters, the goose weight data set corresponding to the goose eggs, the egg laying time of the gooses corresponding to the goose eggs and the predicted goose egg weight parameters. And obtaining the goose egg laying quality. The problems that in the prior art, precision is insufficient due to manual goose egg quality detection, and the goose egg laying quality is difficult to detect are solved, and high-precision detection of the goose egg quality and the goose egg laying quality is achieved.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and specifically, belongs to the field of goose egg laying and egg quality detection based on machine recognition. Background Art

[0002] Goose eggs have become an important part of residents' catering, which has put forward new requirements for modern goose farms. In order to be able to detect and monitor the quality of goose eggs, traditional technical solutions mainly use methods based on manual detection, which undoubtedly consumes a lot of manpower and material resources. At the same time, a large number of perceptual factors will be introduced into the detection of goose eggs, which makes it difficult to follow the same quality inspection standards for the detection of goose egg quality. In addition, it is also difficult to track the quality of goose eggs. They can only be screened and eliminated according to the batch feeding cycle of geese. As a result, although a large number of geese are still in the cycle of producing high-quality goose eggs, they are mistakenly eliminated based on the screening and elimination time. In the final analysis, it is the failure to establish an effective detection plan for the quality of egg production, which leads to the failure to effectively apply the effective egg-laying age of geese.

[0003] Therefore, how to achieve effective detection of goose egg quality and goose egg quality is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] The purpose of this application is to provide a method for detecting goose egg production and egg quality based on machine recognition, so as to solve the current problems of failure to standardize goose egg quality detection, resulting in low detection accuracy, and failure to conduct continuous and effective detection of goose egg quality.

[0005] In order to achieve the above objectives, the present application discloses a method for detecting goose egg laying and egg quality based on machine recognition, comprising:

[0006] Obtain the initial weight of the egg-laying area at different time periods, the weight of the goose eggs, and the weight of the goose corresponding to the eggs;

[0007] Based on the goose egg outline and the goose egg weight, obtaining the goose egg weight parameter;

[0008] Based on the information identification of the goose entering the egg-laying area, obtaining the goose egg weight parameter and the goose weight data group corresponding to the goose egg;

[0009] Predicting the egg weight parameters of the geese in the egg-laying area based on the performance status of the geese entering the egg-laying area and the goose weight data group corresponding to the goose eggs, and obtaining the predicted goose egg weight parameters;

[0010] The goose egg quality is obtained based on the goose egg weight parameter, the goose weight data group corresponding to the goose egg, the egg-laying time of the goose corresponding to the goose egg and the predicted goose egg weight parameter.

[0011] Optionally, obtaining the initial weight of the egg-laying area at different time periods, the weight of the goose eggs, and the weight of the goose corresponding to the goose eggs includes:

[0012] Detect whether there are geese entering the egg-laying area, and based on the time when the geese are in the egg-laying area, obtain a time period when no geese enter, a time period when geese enter, and a time period when geese leave;

[0013] When the egg-laying area is in the period where no geese enter, obtaining the weight of the egg-laying area to obtain the initial weight;

[0014] When the egg-laying area is in the period where geese enter, the weight of the egg-laying area is obtained to obtain the overall weight of the geese and the egg-laying area;

[0015] When the egg-laying area is in the period when the geese leave, the weight of the egg-laying area is obtained to obtain the overall weight of the goose eggs and the egg-laying area;

[0016] Based on the initial weight and the overall weight of the goose and the egg-laying area, obtaining the goose weight corresponding to the goose egg;

[0017] The weight of the goose eggs is obtained based on the initial weight and the overall weight of the goose eggs and the egg-laying area.

[0018] Optionally, also include:

[0019] The egg-laying area is based on a weighing system to obtain the initial weight, the overall weight of the goose and the egg-laying area, and the overall weight of the goose eggs and the egg-laying area;

[0020] The egg-laying area obtains the information identification of the goose based on the information identification system.

[0021] Optionally, obtaining the goose egg weight parameter based on the goose egg outline and the goose egg weight includes:

[0022] Obtain the length and width of the goose egg based on the image recognition system;

[0023] Based on the length and width of the goose egg and the weight of the goose egg, the goose egg weight parameter is obtained. The goose egg weight parameter equation is:

[0024]

[0025] Among them, m is the weight of the goose egg, l is the length of the goose egg, h is the width of the goose egg, and m c is the goose egg weight parameter;

[0026] Based on the goose egg weight parameter, the evaluation grade of the goose egg quality is determined.

[0027] Optionally, the step of obtaining the goose egg weight parameter and the goose weight data group corresponding to the goose egg based on the information identification of the goose entering the egg-laying area includes:

[0028] Based on the chip information of the goose, obtaining the information identification of the goose;

[0029] Based on the information identifier of the goose, obtaining a correspondence between the information identifier of the goose and the weight parameter of the goose egg;

[0030] A data group is established by using the goose information identifier, the goose egg weight parameter and the goose weight corresponding to the goose egg, and the goose information identifier is set for the data group.

[0031] Optionally, the step of predicting the egg weight parameters of the geese in the egg-laying area based on the performance status of the geese entering the egg-laying area and the goose weight data group corresponding to the goose eggs to obtain the predicted goose egg weight parameters includes:

[0032] Based on the image acquisition system of the egg-laying area, identifying the egg-laying status of the geese, and obtaining the performance status evaluation of the geese in the egg-laying area;

[0033] Obtaining information identification of the geese in the egg-laying area, and obtaining the weight of the geese corresponding to the goose eggs;

[0034] The goose weight data group corresponding to the goose eggs is obtained, the information identifier of the goose is identified, and the predicted goose egg weight parameters are obtained.

[0035] Optionally, the image acquisition system based on the egg-laying area identifies the egg-laying status of the geese and obtains the performance status evaluation of the geese in the egg-laying area, including:

[0036] Acquire the performance status of the geese in the egg-laying area based on the image acquisition system, and assign a value to the performance status of the geese in the egg-laying area to obtain a performance status parameter;

[0037] Based on the performance state parameter, the performance state evaluation is obtained, and the performance state evaluation equation is:

[0038]

[0039] Among them, p b Indicates the performance status evaluation value, e b Indicates the anal state of the goose, with a value of 0 or 1, n is the number of feathers dropped by the goose in the egg-laying area, h is the number of twitching times of the goose in the egg-laying area, s i represents the feather state of the goose, and S represents the mask area of ​​the goose image.

[0040] Optionally, the step of obtaining the goose weight data group corresponding to the goose egg, identifying the goose information identifier, and obtaining the predicted goose egg weight parameter includes:

[0041] Based on the image acquisition devices arranged on the top plate and the side plates of the egg-laying area, a top view and left and right side views of the goose are obtained;

[0042] Setting masks on the top view and the left and right side views of the goose to obtain a mask map of the goose;

[0043] Based on the mask image of the goose, obtaining the number of protruding feathers of the goose and the mask area of ​​the fallen feather region;

[0044] Based on the number of protruding feathers of the goose and the mask area of ​​the fallen feather region, the feather state of the goose is obtained. The feather state evaluation equation of the goose is:

[0045]

[0046] Among them, s i Indicates the feather status of the goose, m v The number of feather protrusions of the goose in the top view, m r Indicates the amount of feather protrusion of the goose in the left or right view, s j represents the mask area of ​​the goose's falling feathers, s v The mask area of ​​the goose in the top view, s r The mask area of ​​the goose representing the left or right view;

[0047] The predicted goose egg weight parameter is obtained based on the predicted goose egg mass equation, and the predicted goose egg weight parameter equation is:

[0048]

[0049] Among them, M p represents the predicted goose egg weight parameter, M represents the goose weight corresponding to the goose egg, and p b Indicates the performance status evaluation value, represents the historical goose egg mean, represents the mean weight of geese corresponding to historical goose eggs, Represents the mean value of historical performance status evaluation, is the mean length of goose eggs in history, is the mean width of the historical goose egg, m c It is the quality parameter of goose eggs.

[0050] Optionally, obtaining the goose egg quality based on the goose egg weight parameter, the goose weight data group corresponding to the goose egg, the egg-laying time of the goose corresponding to the goose egg and the predicted goose egg weight parameter includes:

[0051] Obtaining the information identifier of the goose corresponding to the goose egg and the egg-laying time of the goose corresponding to the goose egg;

[0052] Based on a goose egg quality evaluation equation, determining the goose egg quality of the goose corresponding to the goose egg;

[0053] The egg quality of the goose corresponding to the goose egg is recorded and updated, and the egg quality of the goose is predicted.

[0054] Optionally, determining the egg quality of the goose corresponding to the goose egg based on the goose egg quality evaluation equation includes:

[0055] The evaluation equation for goose egg quality is:

[0056]

[0057] Among them, t n represents the time when the goose lays eggs for the nth time, t n-1 represents the time when the goose lays eggs for the (n-1)th time, t 1 Indicates the time when the goose first laid eggs, m p Represents the predicted goose egg weight parameter, m c represents the weight of goose eggs, Q represents the quality parameter of goose eggs;

[0058] Based on the goose egg quality parameters, the goose egg quality is evaluated to determine the goose egg quality.

[0059] The beneficial effects of this application include:

[0060] 1. Improve the quality detection accuracy of goose eggs. After the goose lays eggs, the goose eggs are weighed, but not only based on weight processing, but also by calculating the weight parameters of the goose eggs. By setting the parameters, the quality of the goose eggs is evaluated, so that by setting the evaluation parameters, a more accurate evaluation result can be obtained.

[0061] 2. Evaluate the quality of goose eggs. By setting the information of the goose and analyzing and predicting the weight parameters of the goose eggs, the quality of goose eggs can be analyzed through algorithms, so as to comprehensively analyze the egg-laying capacity of the goose in the current breeding cycle and evaluate the quality of goose eggs.

[0062] 3. Realize accurate analysis of goose egg-laying status. Based on the analysis and determination of factors affecting goose egg-laying, analyze these influencing factors and incorporate these factors into the analysis process of goose egg-laying quality to achieve accurate analysis of goose egg-laying quality, thereby obtaining analysis of goose egg-laying status and predicting goose egg-laying status. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. The drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific implementation methods, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the drawings:

[0064] Figure 1 A flow chart of a method for detecting goose egg laying and egg quality based on machine recognition provided in an embodiment of the present application;

[0065] Figure 2 A schematic diagram of a method for measuring goose egg size disclosed in an embodiment of the present application;

[0066] Figure 3 A mask schematic diagram of a top view and left and right views of a goose provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, in the embodiments of the present invention, "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0068] The egg-laying status of geese is affected by many factors. In modern goose farms, the density of geese is relatively large, and the appearance of geese is extremely similar. It is not realistic to detect the egg-laying quality and egg quality of geese based on the influencing factors. Therefore, the quality of goose eggs is currently mainly determined by manual detection of goose eggs, such as the appearance uniformity and quality of goose eggs. This method can detect the quality of goose eggs to a certain extent, but it will undoubtedly consume a lot of manpower and material resources. At the same time, manual detection will introduce a lot of perceptual factors, which will lead to a decrease in the accuracy of goose egg quality detection. For the detection of goose egg quality, the main method is to determine the pen cycle, that is, record the entry time of the goose, and eliminate all the geese after a certain time. This is obviously difficult to give full play to the egg-laying capacity of all geese. At the same time, it is difficult to detect the egg quality problems of individual geese in the farm in time.

[0069] In the technical solution of the present application, the above two types of problems need to be solved at the same time, namely: the insufficient detection accuracy of goose egg quality and the poor detection effect of goose egg quality. It should be noted here that the goose egg quality mentioned in this application does not refer to the quality of goose eggs, but a comprehensive parameter of factors including the goose's egg-laying ability, the goose's ability to produce high-quality goose eggs, and the proportion of high-quality goose eggs produced by the goose. In the technical solution of the present application, a goose egg weight parameter is set for the goose egg quality to realize the evaluation of the goose egg quality. At the same time, based on the goose egg weight parameter and other parameters that affect the goose's egg-laying level, the goose egg quality parameter is obtained to realize the evaluation of the goose egg quality.

[0070] like Figure 1 As shown, it is a flow chart of a method for detecting goose egg laying and egg quality based on machine recognition provided in an embodiment of the present application, so as to realize real-time detection of goose egg quality and full and accurate analysis of goose egg laying quality. It includes:

[0071] S110, obtaining the initial weight of the egg-laying area in different time periods, the weight of the goose eggs, and the weight of the goose corresponding to the goose eggs; S120, obtaining goose egg weight parameters based on the goose egg outline and the goose egg weight;

[0072] S130, based on the information identification of the goose entering the egg-laying area, obtaining the goose egg weight parameter and the goose weight data group corresponding to the goose egg;

[0073] S140, predicting the egg weight parameters of the geese in the egg-laying area based on the performance status of the geese entering the egg-laying area and the goose weight data group corresponding to the goose eggs, to obtain the predicted goose egg weight parameters;

[0074] S150, obtaining the goose egg quality based on the goose egg weight parameter, the goose weight data group corresponding to the goose egg, the egg-laying time of the goose corresponding to the goose egg and the predicted goose egg weight parameter.

[0075] The purpose of the above steps is to detect the quality of goose eggs and obtain the information of the goose corresponding to the goose eggs. Then, based on the quality of goose eggs and other parameters, the quality of goose eggs is detected and analyzed, thereby fully solving the problems existing in the prior art. The specific description of the above steps is as follows:

[0076] In the process of detecting the quality of goose eggs and the quality of goose eggs, it is obviously necessary to obtain the original data, as described in step S110, including:

[0077] S111, detecting whether there are geese entering the egg-laying area, and obtaining a time period when no geese enter, a time period when geese enter, and a time period when geese leave based on the time when the geese are in the egg-laying area.

[0078] The purpose of this step is to confirm the usage status of the egg-laying area and determine the egg-laying time of the geese. In addition, the operating time of the egg-laying area is decomposed into multiple different time periods, forming a connection relationship between the idle time of the egg-laying area - the time when there are geese - the idle time, laying the foundation for the subsequent weighing accuracy maintenance work.

[0079] Among them, the egg-laying area is equipped with temperature sensors, radar devices, light sensors and other devices to detect the entry status of geese in the egg-laying area.

[0080] Among them, the egg-laying area can be set as a rectangular structure, and goose entry status detection sensors, image acquisition devices, etc. are set in the egg-laying area, and different sensors are set based on the detection targets and requirements.

[0081] Among them, the sensor for the egg-laying area is connected to a timing device, so that when a goose is detected to enter, the entry time and exit time of the goose are immediately recorded.

[0082] In some embodiments, an identification chip, such as an RFID chip, is placed on the goose. In this case, an identification device for the identification chip is set up in the egg-laying area to confirm the information of the goose entering the egg-laying area.

[0083] In some embodiments, an image recognition system is provided to identify geese entering the egg-laying area.

[0084] S112, when the egg-laying area is in the period without geese entering, obtaining the weight of the egg-laying area to obtain the initial weight.

[0085] The purpose of this step is to weigh the egg-laying area during the period when no geese enter, and the weighing result is used as the initial weight. The reason is that geese may drop feathers when laying eggs, and in order to improve the weighing accuracy of goose eggs, such feather drop also needs to be considered. By weighing the egg-laying area during the period when no geese enter, all factors that affect the weighing accuracy can be considered and eliminated. In addition, some geese will enter and lay eggs when there are eggs in the egg-laying area. When the weight of the egg-laying area during the period when no geese are in the egg-laying area is used as the initial weight, confusion in the corresponding relationship between geese and goose eggs can be avoided.

[0086] Wherein, a weighing device is arranged under the egg-laying area to weigh the egg-laying area and obtain the weight of the egg-laying area.

[0087] In some embodiments, when the weight of the egg-laying area suddenly decreases, the current weight of the egg-laying area is immediately acquired, and this weight is the initial weight in the corresponding time period.

[0088] S113. When the egg-laying area is in the period when geese enter, the weight of the egg-laying area is obtained to obtain the overall weight of the geese and the egg-laying area.

[0089] The purpose of this step is to obtain the overall weight of the egg-laying area and the goose when the goose enters the egg-laying area, so that the weight of the goose can be determined.

[0090] When it is detected that the goose enters the egg-laying area, the goose and the egg-laying area are weighed to obtain the overall weight.

[0091] In some embodiments, when the weight of the egg-laying area suddenly increases, it is confirmed that there are geese in the current egg-laying area, and the obtained weight data is the overall weight of the geese and the egg-laying area.

[0092] In some embodiments, an upper limit on the overall weight of the geese and the egg-laying area is set based on the average weight of the geese. For example, the weight of the geese is less than twice the weight, and the sum of this weight and the weight of the egg-laying area is the upper limit of the overall weight. When the weight increase of the egg-laying area exceeds the upper limit of the overall weight of the geese and the egg-laying area, it means that there is more than one goose in the egg-laying area. At this time, it is obviously difficult to detect the quality of a single goose egg, and an alarm is issued in the egg-laying area.

[0093] S114. When the egg-laying area is in the period when the geese leave, obtain the weight of the egg-laying area to obtain the overall weight of the goose eggs and the egg-laying area.

[0094] The purpose of this step is to obtain the total weight of the goose eggs and the egg-laying area after the goose leaves. After obtaining this data, the weight of the goose eggs can be obtained.

[0095] Among them, after the goose is detected to leave the egg-laying area, the current weight of the egg-laying area is immediately obtained.

[0096] In some embodiments, when the weighing device of the egg-laying area detects that the overall weight of the egg-laying area drops suddenly, the weight of the egg-laying area is immediately acquired, and the weight parameter is the weight of the egg-laying area and the goose eggs.

[0097] S115. Based on the initial weight and the overall weight of the goose and the egg-laying area, obtain the goose weight corresponding to the goose eggs.

[0098] The purpose of this step is to obtain the weight of the goose by determining the initial weight of the egg-laying area and the overall weight of the egg-laying area.

[0099] Among them, the initial weight of the egg-laying area and the overall weight of the goose and the egg-laying area are obtained, and the difference between the two is calculated to obtain the weight of the goose.

[0100] S116. Obtain the weight of the goose eggs based on the initial weight and the overall weight of the goose eggs and the egg-laying area.

[0101] The purpose of this step is to obtain the weight of goose eggs by determining the initial weight of the egg-laying area and the overall weight of the goose eggs and the egg-laying area.

[0102] Among them, the initial weight of the egg-laying area and the overall weight of the goose eggs and the egg-laying area are obtained, and the difference between the two is calculated to obtain the weight of the goose eggs.

[0103] In the process of calculating the weight of goose eggs and geese, specifically:

[0104] The egg-laying area is based on a weighing system to obtain the initial weight, the overall weight of the goose and the egg-laying area, and the overall weight of the goose eggs and the egg-laying area.

[0105] Wherein, a weighing system is arranged below the egg-laying area to obtain the weight of the egg-laying area under different conditions.

[0106] S117. The egg-laying area obtains the information identification of the goose based on the information identification system.

[0107] The purpose of this step is to associate the obtained goose egg weight and goose weight values ​​with the goose information identifier to establish a corresponding relationship between the goose and the goose eggs and the goose weight.

[0108] The egg-laying area obtains the information identification of the goose, and obtains the corresponding goose code in the implementation system of the method to obtain the relevant information of the goose.

[0109] Among them, a recognition device for the information identification chip of the goose is set in the egg-laying area, and when the goose enters the egg-laying area, the information identification of the goose currently in the egg-laying area is immediately obtained.

[0110] In some embodiments, based on an image analysis system, information of the geese entering the egg-laying area is identified, thereby obtaining the correspondence between the geese, goose eggs, and the weight of the geese.

[0111] In the above steps S111 to S117, the ultimate goal is to determine the weight of goose eggs and the weight of the geese in the egg-laying area, and based on the information identification of the goose obtained, establish the correlation relationship between the goose information identification - goose egg weight - goose weight, laying the foundation for subsequent goose egg quality and goose egg laying quality testing work.

[0112] After obtaining the weight of the goose eggs, the quality of the goose eggs needs to be tested. In this application, goose egg quality parameters are used for evaluation. Specifically, as described in S120, the following steps are included:

[0113] S121. Obtain the length and width of the goose egg based on the image recognition system.

[0114] The purpose of this step is to obtain the size parameters of the goose eggs involved in the calculation of the goose egg weight parameters. In fact, the technical solution of this application is more similar to calculating the density of goose eggs. However, considering the special shape of goose eggs, the method of calculating the volume needs to consume a lot of computing resources. At the same time, to obtain the edge contour of the goose egg, it is necessary to obtain a curve. This obviously puts too high a requirement on the image processing system and the implementation system of the entire method. In order to reduce the consumption of computing resources, this application adopts the solution of obtaining the length and width of the goose egg.

[0115] Among them, a camera is set on the top plate of the egg-laying area to collect images of goose eggs in the egg-laying area.

[0116] A mask is set on the goose egg image, and the longest distance on the goose egg is the length of the goose egg. Then, a perpendicular line of the longest distance line is obtained, and the maximum parameter of the perpendicular line is the width of the goose egg.

[0117] In some embodiments, Figure 2 As shown, a schematic diagram of a method for measuring the size of goose eggs disclosed in an embodiment of the present application, wherein the mask uses the maximum value of the relative sides between the goose egg and the egg-laying area background as the length and width of the goose egg, wherein the white background portion represents the base plate, and in order to better obtain the background color and the outline of the goose egg, the base plate can be set to a dark color (not limited here, in order to obtain a higher contrast, it can be considered to be set to black) to highlight the goose egg, and the gray portion is the mask area, and there is no need to limit the coverage form of the mask, it only needs to ensure that the edge of the mask is tangent to the outline of the goose egg, and then the length and width of the goose egg can be obtained based on the size parameters of the mask, and the white part in the mask represents the top view of the goose egg, and its color is significantly different from the color of the base plate, so the outline of the goose egg can be better identified. In addition, Figure 2 The form given in the specification is that there are two goose eggs in one egg-laying area. In the method disclosed in the present application, a correspondence between goose eggs and geese is established to avoid the mismatch between goose eggs and the geese that laid the eggs.

[0118] In some embodiments, for the obtained goose egg image, the length is determined based on the pixels in the image. It is necessary to obtain the number of pixels between the line connecting any two points on the outer edge of the goose egg, and then obtain the line connecting the two points with the largest number of pixels as the length of the goose egg. Then obtain the perpendicular line of the line, and the two endpoints of the perpendicular line are the points on the goose egg contour line in the goose egg image, and obtain the number of pixel points of all the perpendicular lines, then the line with the largest number of pixel points is the width of the goose egg.

[0119] In some embodiments, a rectangular mask is set with the outer edge of a goose egg. When ensuring that the four sides of the rectangular mask are tangent to the edge contour line of the goose egg, the relative side lengths of the rectangular mask are respectively the length and width of the goose egg.

[0120] In some embodiments, the position of goose eggs in the egg-laying area is recorded, so that when there are multiple goose eggs in the egg-laying area, the length and width of the latter goose egg can be accurately analyzed, thereby ensuring that all goose egg size parameters in the egg-laying area can be accurately measured.

[0121] S122, based on the length and width of the goose egg and the weight of the goose egg, obtain the goose egg weight parameter, the goose egg weight parameter equation is:

[0122]

[0123] Among them, m is the weight of the goose egg, l is the length of the goose egg, h is the width of the goose egg, and m c is the goose egg weight parameter.

[0124] The purpose of this step is to, after determining the length and width of the goose egg, compare the overall shape of the goose egg to a cylinder, thereby greatly simplifying the volume calculation process for the goose egg, and there is no need to calculate the actual volume of the goose egg.

[0125] Among them, after determining the length and width of the goose egg, the non-actual volume of the goose egg is calculated to obtain the denominator part of the above equation. In the technical solution of the present application, in order to further reduce computing resources, the calculation is directly performed according to the obtained width parameter without processing it into the base radius of the cylinder.

[0126] Among them, in the calculation of the non-actual volume of the goose egg, the width of the goose egg is directly used as the ground diameter of the cylinder.

[0127] S123. Determine the evaluation grade of the goose egg quality based on the goose egg weight parameter.

[0128] The purpose of this step is to evaluate the quality of goose eggs based on the calculated goose egg quality parameters, thereby obtaining an evaluation result of the goose egg quality.

[0129] The quality of the goose eggs is processed according to the goose egg quality parameters, and the quality parameter grade of the goose eggs is obtained based on the processing result. When the goose egg quality parameters fall into the corresponding quality parameter grade interval, the quality grade of the goose egg is obtained.

[0130] In some embodiments, the evaluation grade of goose egg quality is set based on the egg quality parameters of the same goose, so that the quality of newly laid goose eggs of the same goose is rated within the grade range.

[0131] In some embodiments, considering that the quality of goose eggs produced by geese will gradually decline as the feeding time increases, the quality grade of goose eggs is determined based on the time period or the number of goose eggs produced by the geese. For example, one week or five goose eggs are used as a goose egg quality evaluation interval, and the quality grade of goose eggs in the evaluation interval is calculated. When it is determined that the quality parameters of newly produced goose eggs fall into the quality grade that has been obtained, the quality grade of the newly produced goose eggs is obtained.

[0132] In the above steps S121 to S123, the ultimate goal is to obtain the quality parameters of goose eggs, and determine the rating of goose eggs based on the quality parameters of goose eggs, thereby realizing the detection of goose egg quality.

[0133] After the egg quality is detected, the egg quality of the goose needs to be analyzed. Considering that multiple parameters are needed to evaluate the egg quality of the goose, all influencing factors need to be analyzed in the specific process. Among them, the weight of the goose is a key factor that can affect the egg quality of the goose. It is necessary to associate the weight of the goose with the goose information. Specifically, as described in step S130, it includes:

[0134] S131. Based on the chip information of the goose, obtain the information identification of the goose.

[0135] The purpose of this step is to take into account that in modern goose farms, independent chips will be installed on the bodies of geese. Therefore, in the specific processing, based on the chip information of the goose, it is easier to obtain the goose information identification and determine the goose that is currently laying eggs based on the information identification.

[0136] Among them, a goose chip information reading device is set in the egg-laying area, so that when the goose enters the egg-laying area, the code carried on the chip of the current goose is identified, and the goose information identification is obtained based on the code, or the goose information identification is directly obtained.

[0137] In some embodiments, an RFID chip is disposed on the body of the goose, and a reading device for the RFID chip is disposed on the egg-laying area, so as to read and identify the information identification of the goose.

[0138] In some embodiments, after the goose is eliminated, the corresponding goose information identifier is deleted in the execution system of the method described in the present application to avoid the goose information identifier being reused, thereby causing system operation disorder.

[0139] S132. Based on the information identifier of the goose, obtain the corresponding relationship between the information identifier of the goose and the weight parameter of the goose egg.

[0140] The purpose of this step is to establish an association between the goose information identification and the goose weight after the goose enters the egg-laying area, thereby ensuring that the correct goose weight information can be used for calculation in the subsequent goose egg quality analysis.

[0141] Among them, when the goose enters the egg-laying area and after the goose leaves the egg-laying area, the weight of the goose is directly obtained, and the information identification of the goose is also obtained. Then, a corresponding relationship is directly established between these two types of information to generate an "information identification of the goose-weight of the goose" association relationship.

[0142] In some embodiments, the obtained information identification and weight of the goose are input into the implementation system of the method disclosed in the present application. Only after the system receives these two types of information can a corresponding relationship be generated.

[0143] S133, creating a data group with the goose information identifier, the goose egg weight parameter and the goose weight corresponding to the goose egg, and setting the goose information identifier for the data group.

[0144] The purpose of this step is to describe the status of the goose itself by establishing a data group. In order to facilitate the subsequent detection and monitoring of the egg quality of the goose, the goose information identifier is set for the generated data group, which can ensure faster identification of the goose information.

[0145] Among them, the above steps have generated the corresponding relationship between the goose information identification, goose egg weight parameters and goose weight, so such parameters can be constructed into the corresponding data group.

[0146] After the data group is obtained, a corresponding relationship is established between the data group and the information identifier of the goose. Specifically, the information identifier of the goose is set for the data group.

[0147] In some embodiments, the goose information identifier is not added to the created data group, thereby saving storage space for the data group.

[0148] The following is a correspondence between a data group and the information identifier of a goose:

[0149] a01w055t230923n19: [0.78,4.8],

[0150] Among them, the corresponding prefix code is the goose information identification. Specifically, a01 represents farm No. a01, w055 represents white goose No. 55, t230923 represents the entry time as September 23, 2023, and n19 represents the end of the record time. The goose has laid 19 eggs. That is to say, in some embodiments, the goose information identification can also carry some information in a changing state. 0.78 corresponds to the goose egg weight parameter. Specifically, the goose egg weighs 195g, the length is 5.5cm, and the width is 3.8cm. 4.8 corresponds to the weight of the goose, which means that at this egg-laying time, the weight of the goose is 4.8kg.

[0151] The purpose of the above steps S131 to S133 is to obtain the weight of the goose eggs and the weight of the goose respectively. At the same time, it is found through practice that some geese may enter the egg-laying area when they see eggs. Therefore, it is necessary to judge the correspondence between eggs and geese based on the goose's information identification to avoid establishing an incorrect correspondence between the goose and the goose eggs.

[0152] After establishing the goose information identification, the correspondence between the goose weight and the goose egg weight parameters and the data group, it is equivalent to obtaining the initial data in the goose egg quality detection, and it is necessary to detect the goose egg quality based on the initial data. This application is based on the comparison of the goose egg weight parameters and the actual goose egg weight parameters to obtain the results, and it is necessary to first confirm the predicted goose egg weight parameters. Specifically, as described in step S140, it includes:

[0153] S141. Based on the image acquisition system of the egg-laying area, identify the egg-laying status of the geese and obtain the performance status evaluation of the geese in the egg-laying area.

[0154] The purpose of this step is to take into account the profound correlation between the performance status of geese during the egg-laying process and the quality of the eggs themselves, as well as the quality of the eggs produced by geese. Therefore, it is necessary to obtain an evaluation of the performance status of the geese during the specific egg-laying quality testing stage.

[0155] In the specific performance status evaluation, it is necessary to determine based on the external performance of the goose, specifically:

[0156] S1411. Acquire the performance status of the geese in the egg-laying area based on the image acquisition system, and assign values ​​to the performance status of the geese in the egg-laying area to obtain performance status parameters.

[0157] The purpose of this step is to decompose the performance status of the goose into different performances. By assigning values ​​to these performances, the different performance usage data contained in the performance status of the goose can be described, so that the performance status can be quantitatively described.

[0158] Among them, for different categories of performance parameter state parameters, detection boxes are assigned values ​​according to different performance situations.

[0159] Among them, the performance status of the goose is divided into the anal status of the goose, the number of fallen feathers of the goose in the egg-laying area, the number of convulsions of the goose in the egg-laying area, and the feather status of the goose. For the anal status of the goose, when the anus of the goose is red and swollen, the goose cannot lay eggs, and it is assigned a value of 0. When the goose does not have the problem of redness and swelling of the anus, the goose can lay eggs, and it is assigned a value of 1; the number of fallen feathers of the goose in the egg-laying area is assigned based on the number of fallen feathers in the egg-laying area; the number of convulsions of the goose in the egg-laying area is measured and assigned based on the cameras set up in the egg-laying area; the feather status of the goose can be further decomposed to analyze the degree of disorder of the goose feathers and the area of ​​baldness on the goose's body, which is determined based on actual measurements.

[0160] Among them, based on all the parameters obtained above, the performance status parameters of the goose are obtained.

[0161] S1412. Based on the performance state parameter, obtain the performance state evaluation, the performance state evaluation equation is:

[0162]

[0163] Among them, p b Indicates the performance status evaluation value, e b Indicates the anal state of the goose, with a value of 0 or 1, n is the number of feathers dropped by the goose in the egg-laying area, h is the number of twitching times of the goose in the egg-laying area, s i represents the feather state of the goose, and S represents the mask area of ​​the goose image.

[0164] The purpose of this step is to obtain the performance status parameters of the goose based on the decomposition content of the performance status of the goose and the assignment of the performance status of the goose in the egg-laying area to obtain the calculation results.

[0165] Among them, based on the above equation and the egg-laying status of the goose in the egg-laying area, the calculation results of the performance status parameters of the goose are obtained.

[0166] S142, obtaining information identification of the geese in the egg-laying area, and obtaining the weight of the geese corresponding to the goose eggs.

[0167] The purpose of this step is that the weight of the goose itself will have a profound impact on the quality of goose eggs. Therefore, in the specific calculation, it is necessary to determine the weight of the goose corresponding to the goose eggs based on the goose information identification.

[0168] Wherein, based on the correspondence between the data group in step S133 and the information identifier of the goose, the weight data of the goose is determined.

[0169] S143, obtaining the goose weight data group corresponding to the goose eggs, identifying the goose information identifier, and obtaining the predicted goose egg weight parameters.

[0170] The purpose of this step is to obtain the predicted goose egg weight parameters based on the acquisition of relevant data in the goose weight data group and the determination of the goose information identification, so as to lay the foundation for the subsequent work of determining the goose egg production quality by comparing the predicted goose egg weight parameters and the goose egg weight parameters.

[0171] The device for acquiring the performance status of the goose is configured, and various data involved are calculated to obtain the evaluation result. Specifically:

[0172] S1431. Based on the image acquisition devices arranged on the top plate and the side plates of the egg-laying area, a top view and left and right side views of the goose are obtained.

[0173] The purpose of this step is to obtain a top view and left and right side views of the goose by setting up image acquisition devices on the top and side panels of the egg-laying area. Based on the images obtained, the degree of feather disorder and the area of ​​baldness of the goose can be analyzed.

[0174] Among them, the image acquisition device arranged on the top plate of the egg-laying area can be arranged at the geometric center of the top plate to achieve high-quality shooting of the bird's-eye view of the geese.

[0175] Among them, the image acquisition device arranged on the side panel of the egg-laying area can be arranged at the geometric center of the goose's body when the goose is squatting, so as to achieve the maximum high-precision measurement of the goose's lateral body.

[0176] S1432. Set masks for the top view and the left and right side views of the goose to obtain a mask image of the goose.

[0177] The purpose of this step is to calculate the degree of disorder of the goose's feathers. This application adopts counting the number of feathers protruding from the goose's body. However, if the camera is used directly for analysis and counting, the image recognition system needs to consume a lot of resources, especially in large modern goose farms, where multiple geese may lay eggs at the same time. By setting a mask, the consumption of computing resources is minimized.

[0178] Among them, the top view and left and right side views of the goose are obtained, and the images are gray-scaled.

[0179] Herein, image recognition is performed on the grayscale processed image and the mask is evenly covered.

[0180] Among them, for the mask coverage, it is necessary to obtain the outer edge contour line of the goose, and set the side line of the rectangular mask based on the contour line. The side line of the rectangular mask needs to ensure that it is tangent to the outer edge contour line of the goose.

[0181] In some embodiments, the outer edge contour line of the goose is segmented, a tangent segment is set for each segment, and the distance between adjacent endpoints of adjacent tangent segments is calculated. If the distance between a tangent segment and two adjacent tangent segments increases suddenly, the tangent segment is located in the protruding feather interval.

[0182] In some embodiments, the outer edge contour line of the goose is segmented, and two adjacent endpoints are connected as line segments. If the angle between two line segments changes suddenly, it is considered that these two branches are in the protruding feather area.

[0183] In some embodiments, with respect to the identification of angle mutations between the two line segments, the system automatically identifies the goose's tail, the connection between the body and neck, and the head. When the angle mutation is found to occur at any of these three positions, the mutation is not counted in the goose's feather protrusion count.

[0184] Among them, for all the above methods, it is necessary to set a rectangular mask in the grayscale image and count the masks of feathers protruding from the overall body mask of the goose to determine the number of protruding feathers.

[0185] Among them, it is the alopecia area in the grayscale processed image, and the edge line of the alopecia area is identified, and the edge line of the rectangular mask is set in the alopecia area. The edge line of the rectangular mask needs to be tangent to the edge line of the alopecia area.

[0186] Among them, for the number and area of ​​alopecia areata in geese, it is necessary to analyze the top view, left view and right view of the goose together to obtain the total area.

[0187] like Figure 3 As shown, it is a mask schematic diagram of a top view and left and right views of a goose provided in an embodiment of the present application, wherein: Figure 3 (a) is a top view, Figure 3 (b) is the left view, Figure 3 (c) is the right side view.

[0188] Among them, all the areas in the dotted rectangular box represent the masks of the top view, left view and right view of the goose, and Figure 3 In the images (a) to (c), the part surrounded by the curve in the white area is the outline of the goose, which is obtained after contrast processing of the goose image. All the gray parts are mask areas of the goose's performance state. The gray area at the edge of the goose outline indicates the area where the goose feathers are protruding. In obtaining the number of protruding goose feathers, only the number of protruding goose feathers needs to be counted. Figure 3 (a) and (c), or Figure 3The number of masked areas of (a) and (b) is sufficient to obtain the number of protruding feathers of the goose. The gray area with blank areas indicates the area with alopecia on the goose's body. A mask is set for such areas, and the edge line of the mask and the alopecia area is tangent to determine the approximate area of ​​the alopecia area. In calculating the mask area of ​​the alopecia area, it is necessary to obtain Figure 3 The area of ​​all alopecia areata areas from (a) to (c). In addition, the area of ​​the top view and side view of the goose is determined based on the set mask, wherein the set mask frame needs to ensure that it is tangent to the contour line of the goose. In the calculation of the goose performance state, the calculation is directly based on the mask area of ​​the side view and top view of the goose, so there is no need to calculate based on the contour line, thereby fully reducing the difficulty of calculation and saving computing resources.

[0189] S1433. Based on the mask image of the goose, obtain the number of protruding feathers of the goose and the mask area of ​​the fallen feather region.

[0190] The purpose of this step is to obtain the number of protruding feathers and the mask area of ​​the fallen feathers of the goose after correctly processing the goose image, so as to determine the feather performance status.

[0191] Among them, based on the set mask, the number of protruding feathers of the goose is counted, and the mask area of ​​the fallen feather area is calculated.

[0192] S1434. Based on the number of protruding feathers of the goose and the mask area of ​​the fallen feather region, obtain the feather state of the goose. The feather state evaluation equation of the goose is:

[0193]

[0194] Among them, s i Indicates the feather status of the goose, m v The number of feather protrusions of the goose in the top view, m r Indicates the amount of feather protrusion of the goose in the left or right view, s j represents the mask area of ​​the goose's falling feathers, s v The mask area of ​​the goose in the top view, s r The mask area representing the goose in the left or right view.

[0195] The purpose of this step is to evaluate the feather condition of the goose. Relatively speaking, the feather condition of the goose is more difficult to evaluate and can better represent the physical condition indicators of the goose. Therefore, by obtaining the feather evaluation parameters of the goose, the performance status of the goose can be determined.

[0196] Among them, the number of protruding feathers of the goose is actually based on the principle of three views. Combined with the purpose of this step, it can be seen that only the number of protruding feathers needs to be determined. At the same time, taking the top view as an example, the protruding feathers in the back area of ​​the goose are difficult to find based on the attached figure, because the color difference between the protruding feathers and the goose back is not enough to support machine recognition. The number of protruding feathers on the two sides of the goose can be determined based on the top view, and the number of protruding feathers on the back can be obtained from a separate left view or right view. Therefore, to be able to obtain the number of all protruding feathers, it is necessary to obtain a top view and an image of a left view or a right view for counting.

[0197] In some embodiments, the number of protruding feathers is verified based on the projection relationship of the three views to obtain a more accurate value of the number of protruding feathers.

[0198] S1435, obtaining the predicted goose egg weight parameter based on the predicted goose egg mass equation, the predicted goose egg weight parameter equation is:

[0199]

[0200] Among them, M p represents the predicted goose egg weight parameter, M represents the goose weight corresponding to the goose egg, and p b Indicates the performance status evaluation value, represents the historical goose egg mean, represents the mean weight of geese corresponding to historical goose eggs, Represents the mean value of historical performance status evaluation, is the mean length of goose eggs in history, is the mean width of the historical goose egg, m c It is the quality parameter of goose eggs.

[0201] The purpose of this step is to obtain the predicted goose egg weight parameters after determining the performance status of the goose, so as to predict the quality of the goose eggs. In addition, based on the obtained predicted goose egg weight parameters, they can be used for comparison with the goose egg weight, thereby introducing more evaluation parameters in the goose egg quality detection.

[0202] Among them, in the process of predicting the weight parameters of goose eggs, the evaluation is based on historical goose egg weight, length and width parameters. At the same time, the performance status of the goose is also introduced, and predictions are made based on these two parameters.

[0203] Among them, for the performance status of the goose, since the anus status of the goose is assigned a value of 0 or 1, when the anus is red and swollen, it means that the goose cannot lay eggs, so the performance status and predicted egg weight parameters are both 0 after calculation.

[0204] Among them, for all the historical goose egg parameters, it is necessary to ensure that all such historical goose egg parameters come from the same goose, rather than calculating the average of all the goose eggs that have been produced, so as to ensure that the historical goose egg data of the same goose is analyzed and the predicted goose egg parameters of the next goose egg produced by the goose are predicted.

[0205] The purpose of the above steps S141 to S143 is to obtain the historical goose egg data and the correspondence between the historical goose eggs and the geese, so as to obtain the predicted goose egg weight parameters. At the same time, in the acquisition of the predicted goose egg weight parameters, the performance status of the goose is introduced, so as to obtain more accurate predicted goose egg weight parameters based on the specific performance status of the goose.

[0206] After obtaining the predicted goose egg weight parameters, it is necessary to conduct a final test on the goose egg quality. In the technical solution disclosed in the present application, the key technical idea adopted is to introduce the number of predicted goose egg weight adoptions, goose egg weight parameters and goose feeding time, etc., to test the goose egg quality. At the same time, although this method is used to test the goose egg quality under the technical concept of the present application, it can also be used to predict the goose egg quality based on this method. Specifically, as described in step S150, it includes:

[0207] S151, obtaining the information identifier of the goose corresponding to the goose egg and the egg-laying time of the goose corresponding to the goose egg.

[0208] The purpose of this step is to record the egg-laying time of the goose and update it in the system. This is equivalent to generating a special egg-laying file for each goose. The egg-laying file includes not only the number of eggs laid, but also the egg-laying time, so as to detect the egg-laying quality of the goose.

[0209] Among them, the information identification of the goose is obtained to identify the information code of the goose, and then the detected egg-laying data is recorded and such information is integrated and processed.

[0210] Among them, for the information identification of the goose, supplementary processing can be performed on the basis of S133 above, such as: the time for the goose to enter the pen has been set, and then after each time the goose lays eggs, the egg-laying time of the goose is supplemented based on the time for entering the pen.

[0211] In some embodiments, no additional processing is performed on the set goose information identification, but a data group of goose egg production time, goose egg weight parameters and predicted goose egg parameters is established and associated with the goose information identification to generate multiple groups of data.

[0212] S152. Determine the egg quality of the goose corresponding to the goose egg based on the goose egg quality evaluation equation.

[0213] The purpose of this step is to obtain the evaluation equation for goose egg quality and to conduct a specific analysis on the goose egg quality. The analysis of goose egg quality is based on the relevant data that have been obtained.

[0214] Among them, this step includes:

[0215] S1521, the goose egg quality evaluation equation is:

[0216]

[0217] Among them, t n represents the time when the goose lays eggs for the nth time, t n-1 represents the time when the goose lays eggs for the (n-1)th time, t 1 Indicates the time when the goose first laid eggs, m p Represents the predicted goose egg weight parameter, m c represents the weight of goose eggs, and Q represents the quality parameter of goose eggs.

[0218] The purpose of this step is to determine the goose egg quality equation, thereby calculating the goose egg quality based on the equation and determining the goose egg quality in the form of data.

[0219] Among them, the analysis process of goose egg quality needs to be determined based on the interval between each egg-laying time and the length of time the geese have been in the pen, so as to establish a quantitative relationship between the goose egg quality and the time the geese have been in the pen.

[0220] Among them, in the technical solution of the present application, the time at which geese enter the pen in a farm may be different. The principle is: when it is found that the quality of the eggs produced and the egg laying quality of a certain goose are significantly lower than the set threshold, it can be determined that the goose is not suitable for continued breeding and needs to be eliminated. At the same time, new geese will be added to the farm, which means that the time at which each goose enters the pen is different from the time at which other geese enter the pen. Therefore, it is necessary to calculate the egg laying quality of the goose separately and independently by recording the time and determining the egg-laying time.

[0221] Among them, the change rate of the predicted goose egg weight parameters and the goose egg weight parameters is calculated, so as to better analyze the difference between the predicted goose egg weight parameters and the goose egg weight in the specific processing process.

[0222] In some embodiments, the change rate between the predicted goose egg weight parameter and the goose egg weight parameter corresponding to the goose egg-laying time is also established to achieve accurate confirmation and description of the egg-laying time of different geese.

[0223] In some embodiments, based on the egg quality evaluation equation, the goose egg quality evaluation data corresponding to each goose laying eggs is obtained, and a goose egg quality parameter curve is generated, and then the goose egg quality in a subsequent period of time is predicted based on the trend of the curve.

[0224] S1522. Based on the goose egg quality parameters, the goose egg quality is evaluated to determine the goose egg quality.

[0225] The purpose of this step is to obtain the goose egg quality parameters and then accurately confirm the goose egg quality based on the obtained goose egg quality parameters, so as to achieve better confirmation of the goose egg quality.

[0226] Among them, different evaluation parameter intervals are set for the goose egg quality parameters. When the obtained goose egg quality parameters fall into the corresponding evaluation parameter interval, the goose egg quality rating corresponding to the evaluation parameter interval is the corresponding goose egg quality rating.

[0227] In some embodiments, the mean of the historical goose egg quality parameters obtained is calculated and updated, and then the newly obtained goose egg quality parameters are compared with the mean of the historical goose egg quality parameters, and analyzed by calculating the error rate. When it is found that a plurality of consecutive newly obtained goose egg quality parameters are significantly lower than the mean already obtained, the goose entry time is confirmed. When it is found that the goose entry time and the error rate exceed the set threshold value, it is considered that the goose's egg quality is not sufficient to support its production of high-quality eggs, and the goose needs to be eliminated.

[0228] The purpose of the above steps S1521 to S1522 is to set up a goose egg production evaluation equation, introduce the goose egg production time, the goose entry time, the predicted goose egg weight parameters and the goose egg weight parameters into the equation, so as to achieve a comprehensive analysis of the goose egg production quality and scientifically evaluate the goose egg production quality.

[0229] S153, recording and updating the egg laying quality of the goose corresponding to the goose egg, and predicting the egg laying quality of the goose.

[0230] The purpose of this step is to record and update the egg quality of the geese corresponding to the goose eggs, laying the foundation for further judgment and prediction of the subsequent goose egg quality.

[0231] Among them, the obtained goose egg quality is recorded and updated in the implementation system of the method disclosed in this application, and the method is used to record the goose egg quality.

[0232] In some embodiments, for the egg laying quality of the goose that has been recorded and updated, a goose egg laying quality parameter curve is established based on the obtained parameters, so as to analyze and predict the goose egg laying quality.

[0233] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to computer program instructions, and the aforementioned computer program can be stored in a non-volatile storage medium. When the computer program is executed, it executes the steps of the above method embodiments. Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, a server, a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention.

[0234] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for detecting goose egg laying and egg quality based on machine recognition, characterized in that: include: Obtain the initial weight of the egg-laying area at different time periods, the weight of the goose eggs, and the weight of the goose corresponding to the eggs; Based on the goose egg outline and the goose egg weight, obtaining the goose egg weight parameter; Based on the information identification of the goose entering the egg-laying area, obtaining the goose egg weight parameter and the goose weight data group corresponding to the goose egg; Predicting the egg weight parameters of the geese in the egg-laying area based on the performance status of the geese entering the egg-laying area and the goose weight data group corresponding to the goose eggs, and obtaining the predicted goose egg weight parameters; The goose egg quality is obtained based on the goose egg weight parameter, the goose weight data group corresponding to the goose egg, the egg-laying time of the goose corresponding to the goose egg and the predicted goose egg weight parameter.

2. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 1, characterized in that: The method of obtaining the initial weight of the egg-laying area at different time periods, the weight of the goose eggs, and the weight of the goose corresponding to the goose eggs comprises: Detect whether there are geese entering the egg-laying area, and based on the time when the geese are in the egg-laying area, obtain a time period when no geese enter, a time period when geese enter, and a time period when geese leave; When the egg-laying area is in the period where no geese enter, obtaining the weight of the egg-laying area to obtain the initial weight; When the egg-laying area is in the period where geese enter, the weight of the egg-laying area is obtained to obtain the overall weight of the geese and the egg-laying area; When the egg-laying area is in the period when the geese leave, the weight of the egg-laying area is obtained to obtain the overall weight of the goose eggs and the egg-laying area; Based on the initial weight and the overall weight of the goose and the egg-laying area, obtaining the goose weight corresponding to the goose egg; The weight of the goose eggs is obtained based on the initial weight and the overall weight of the goose eggs and the egg-laying area.

3. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 2, characterized in that: Also includes: The egg-laying area is based on a weighing system to obtain the initial weight, the overall weight of the goose and the egg-laying area, and the overall weight of the goose eggs and the egg-laying area; The egg-laying area obtains the information identification of the goose based on the information identification system.

4. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 1, characterized in that: The step of obtaining the goose egg weight parameter based on the goose egg outline and the goose egg weight includes: Obtain the length and width of the goose egg based on the image recognition system; Based on the length and width of the goose egg and the weight of the goose egg, the goose egg weight parameter is obtained. The goose egg weight parameter equation is: Among them, m is the weight of the goose egg, l is the length of the goose egg, h is the width of the goose egg, and m c is the goose egg weight parameter; Based on the goose egg weight parameter, the evaluation grade of the goose egg quality is determined.

5. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 1, characterized in that: The step of obtaining the goose egg weight parameter and the goose weight data group corresponding to the goose egg based on the information identification of the goose entering the egg-laying area includes: Based on the chip information of the goose, obtaining the information identification of the goose; Based on the information identifier of the goose, obtaining a correspondence between the information identifier of the goose and the weight parameter of the goose egg; A data group is established by using the goose information identifier, the goose egg weight parameter and the goose weight corresponding to the goose egg, and the goose information identifier is set for the data group.

6. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 1, characterized in that: The method of predicting the weight parameters of the eggs of the geese in the egg-laying area based on the performance status of the geese entering the egg-laying area and the goose weight data group corresponding to the goose eggs, and obtaining the predicted goose egg weight parameters, comprises: Based on the image acquisition system of the egg-laying area, identifying the egg-laying status of the geese, and obtaining the performance status evaluation of the geese in the egg-laying area; Obtaining information identification of the geese in the egg-laying area, and obtaining the weight of the geese corresponding to the goose eggs; The goose weight data group corresponding to the goose eggs is obtained, the information identifier of the goose is identified, and the predicted goose egg weight parameters are obtained.

7. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 6, characterized in that: The image acquisition system based on the egg-laying area identifies the egg-laying status of the geese and obtains the performance status evaluation of the geese in the egg-laying area, including: Acquire the performance status of the geese in the egg-laying area based on the image acquisition system, and assign a value to the performance status of the geese in the egg-laying area to obtain a performance status parameter; Based on the performance state parameter, the performance state evaluation is obtained, and the performance state evaluation equation is: Among them, p b Indicates the performance status evaluation value, e b Indicates the anal state of the goose, with a value of 0 or 1, n is the number of feathers dropped by the goose in the egg-laying area, h is the number of twitching times of the goose in the egg-laying area, s i represents the feather state of the goose, and S represents the mask area of ​​the goose image.

8. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 6, characterized in that: The step of obtaining the goose weight data group corresponding to the goose egg, identifying the goose information identifier, and obtaining the predicted goose egg weight parameter includes: Based on the image acquisition devices arranged on the top plate and the side plates of the egg-laying area, a top view and left and right side views of the goose are obtained; Setting masks on the top view and the left and right side views of the goose to obtain a mask map of the goose; Based on the mask image of the goose, obtaining the number of protruding feathers of the goose and the mask area of ​​the fallen feather region; Based on the number of protruding feathers of the goose and the mask area of ​​the fallen feather region, the feather state of the goose is obtained. The feather state evaluation equation of the goose is: Among them, s i Indicates the feather status of the goose, m v The number of feather protrusions of the goose in the top view, m r Indicates the amount of feather protrusion of the goose in the left or right view, s j represents the mask area of ​​the goose's falling feathers, s v The mask area of ​​the goose in the top view, s r The mask area of ​​the goose representing the left or right view; The predicted goose egg weight parameter is obtained based on the predicted goose egg mass equation, and the predicted goose egg weight parameter equation is: Among them, M p represents the predicted goose egg weight parameter, M represents the goose weight corresponding to the goose egg, and p b Indicates the performance status evaluation value, represents the historical goose egg mean, represents the mean weight of geese corresponding to historical goose eggs, Represents the mean value of historical performance status evaluation, is the mean length of goose eggs in history, is the mean width of the historical goose egg, m c It is the quality parameter of goose eggs.

9. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 1, characterized in that: The step of obtaining the goose egg quality based on the goose egg weight parameter, the goose weight data group corresponding to the goose egg, the egg-laying time of the goose corresponding to the goose egg and the predicted goose egg weight parameter comprises: Obtaining the information identifier of the goose corresponding to the goose egg and the egg-laying time of the goose corresponding to the goose egg; Determining the egg quality of the goose corresponding to the goose egg based on the goose egg quality evaluation equation; The egg quality of the goose corresponding to the goose egg is recorded and updated, and the egg quality of the goose is predicted.

10. The method for detecting goose egg laying and egg quality based on machine recognition according to claim 9, characterized in that: The step of determining the egg quality of the goose corresponding to the goose egg based on the goose egg quality evaluation equation comprises: The evaluation equation for goose egg quality is: Among them, t n represents the time when the goose lays eggs for the nth time, t n-1 represents the time when the goose lays eggs for the (n-1)th time, t1 represents the time when the goose lays eggs for the first time, m p Represents the predicted goose egg weight parameter, m c represents the weight of goose eggs, Q represents the quality parameter of goose eggs; Based on the goose egg quality parameters, the goose egg quality is evaluated to determine the goose egg quality.

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