A method and system for breeding management of a breeder chicken

Abstract

The embodiment of the present application relates to the technical field of breeding management, and discloses a breeding management method for breeding hens, comprising: acquiring a temperature detection image group of each breeding area in a henhouse; acquiring position information of each infrared detection pixel point in the temperature detection image, and performing segmentation processing on the temperature detection image according to the position information to obtain a segmented infrared image; inputting the segmented infrared image into a pre-constructed hen temperature identification model to obtain an identification temperature set of each monitored hen in the temperature detection image; and matching the temperature identification set with a set temperature condition, and if the matching fails, performing early warning according to the position of the hen to remind the corresponding management personnel. The scheme of the embodiment of the present application can monitor the body temperature of the hen in real time, can discover possible health problems such as fever in time, and thus can take preventive measures to avoid the occurrence and spread of diseases. This is helpful to improve the survival rate of the hen and reduce the breeding cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of breeding management, and in particular to a breeding management method and system for breeding hens. BACKGROUND

[0002] In livestock breeding, animal body parameters are one of the key growth indicators. However, due to differences between individual animals and differences in the environment, efficient measurement of animal parameters cannot be achieved. The existing general method is to use manual monitoring, which has a large degree of subjectivity. It cannot achieve fine measurement, and this method cannot timely and efficiently feedback the actual state of each hen in the whole henhouse, and cannot achieve fine breeding monitoring and management. Therefore, designing a scheme capable of fine breeding management has become a technical problem to be solved by those skilled in the art. SUMMARY

[0003] To address the defects, the present application discloses a breeding management method for breeding hens, which can achieve efficient breeding monitoring of breeding hens, improve the overall breeding management level of the henhouse, and timely detect abnormal states of the monitored breeding hens.

[0004] The first aspect of the present application discloses a breeding management method for breeding hens, comprising:

[0005] A group of temperature detection images of each breeding area in the henhouse is obtained by a group of infrared cameras arranged in the henhouse, wherein the group of temperature image detection images includes a plurality of temperature detection images, the group of infrared cameras includes a plurality of infrared cameras, and each breeding area corresponds to an infrared camera;

[0006] The position information of each infrared detection pixel point in the temperature detection image is obtained, and the temperature detection image is segmented according to the position information to obtain a segmented infrared image associated with each monitored breeding hen;

[0007] The segmented infrared image associated with each monitored breeding hen is input into a pre-constructed temperature identification model for identification to obtain a set of identification temperatures of each monitored breeding hen in the temperature detection image;

[0008] The temperature identification set is matched with a set temperature condition, and if it does not match, a warning is given according to the position of the breeding hen to remind the corresponding management personnel.

[0009] As an optional implementation, in the first aspect of the present application, the group of temperature detection images of the breeding area in the henhouse is obtained by the group of infrared cameras arranged in the henhouse, comprising:

[0010] The temperature detection images of each breeding area in the chicken house are acquired by an infrared camera group arranged in the hatching chicken house, and the temperature detection images include temperature images of the hatching hens;

[0011] The temperature detection images of each breeding area in the chicken house are acquired by an infrared camera group arranged in the hatching chicken house, and the temperature detection images include temperature images of the hatching hens;

[0012] The temperature detection images of each breeding area in the chicken house are acquired by an infrared camera group arranged in the hatching chicken house, and the temperature detection images include temperature images of the hatching hens;

[0013] The segmented infrared images associated with each monitored hen are input into the pre-constructed hen temperature identification model for identification, including:

[0014] According to the first position information, the corresponding hen temperature identification model is determined, and the hen temperature identification model includes a hatching hen temperature model, a brooding and growing hen temperature model, and an egg-laying hen temperature model;

[0015] The segmented infrared images associated with each monitored hen are input into the pre-constructed hen temperature identification model for identification.

[0016] As an optional implementation, in the first aspect of the embodiment of the present application, the segmented infrared images associated with each monitored hen are input into the pre-constructed hen temperature identification model for identification to obtain the identification temperature set of each monitored hen in the temperature detection image, including:

[0017] The segmented infrared images associated with each monitored hen are input into the pre-constructed hen temperature identification model to obtain a first region of interest, a second region of interest, a third region of interest, and a fourth region of interest; and each pixel point parameter associated with the first region of interest, the second region of interest, the third region of interest, and the fourth region of interest is acquired;

[0018] According to the pre-constructed temperature calibration mapping table, the temperature of each pixel point data associated with the first region of interest, the second region of interest, the third region of interest, and the fourth region of interest is counted to determine the average temperature information, the highest temperature information, and the lowest temperature information of each region of interest;

[0019] According to the average temperature information, the highest temperature information, and the lowest temperature information of each region of interest, the identification temperature set of each monitored hen is obtained.

[0020] As an optional implementation, in the first aspect of the embodiment of the present application, before the segmented infrared image associated with each monitored breeder hen is input into the pre-constructed breeder hen temperature identification model, the method further comprises:

[0021] According to the set gray threshold, the segmented infrared image is identified to determine the position information of each fence in the segmented infrared image, and the fence image in the segmented infrared image is removed.

[0022] The pixel point parameters on the left and right sides of the position information of each fence are obtained, and the corresponding segmented infrared image is color-filled according to the pixel point parameters on the left and right sides of the position information of each fence to obtain a filled segmented infrared image.

[0023] As an optional implementation, in the first aspect of the embodiment of the present application, the breeder hen temperature identification model is obtained by the following steps:

[0024] A sample training set is obtained, the sample training set includes breeder hen sample images, and the sample training set is divided to obtain a training set, a validation set and a test set;

[0025] The breeder hen sample images are preprocessed;

[0026] The preprocessed breeder hen sample images are input into the breeder hen temperature identification model with completed parameter configuration for training and parameter configuration adjustment until the corresponding breeder hen temperature identification model meets the training requirements, and the current best model parameters are saved;

[0027] The trained breeder hen temperature identification model is used to predict the breeder hen test images in the test set to obtain each part image in each breeder hen test image;

[0028] According to the prediction result of the breeder hen temperature identification model and the real annotation, a part confusion matrix is generated; the rows of the part confusion matrix represent the real parts, and the columns represent the predicted parts.

[0029] According to the part confusion matrix, the overall performance index of the breeder hen temperature identification model is calculated, and the overall performance index includes accuracy, precision, recall and F1 score.

[0030] And according to the analysis result of the part confusion matrix, the breeder hen temperature identification model is adjusted and optimized.

[0031] As an optional implementation, in the first aspect of the embodiment of the present application, after the identification temperature set of each monitored breeder hen in the temperature detection image is obtained, the method further comprises:

[0032] According to all the obtained identification temperature sets, data updating operation is performed on the corresponding breeder temperature table, and the breeder temperature table is mapped according to the positions of the breeders in the chicken house.

[0033] The breeding management method further includes:

[0034] The temperature information in the corresponding chicken house is determined through the temperature sensor arranged in the chicken house, and the set temperature condition is updated according to the temperature information.

[0035] As an optional implementation, in the first aspect of the embodiment of the present application, the breeding management method further includes:

[0036] The temperature monitoring images of each breeding area in the chicken house are obtained through the infrared camera group arranged in the chicken house every set time;

[0037] The position information of each infrared detection pixel point in the temperature monitoring image is obtained, and the temperature monitoring image is segmented according to the position information to obtain a segmented infrared image associated with each monitored breeder;

[0038] The segmented infrared image associated with each monitored breeder is input into a pre-constructed breeder temperature identification model to obtain a head and neck region, a leg and foot region, and a wing region;

[0039] According to a pre-constructed temperature calibration mapping table, the temperature of each pixel point data associated with the head and neck region, the leg and foot region, and the wing region is counted to determine the average temperature information of each region of interest;

[0040] The temperature difference information between the average temperature information of each region of interest obtained from adjacent detection images is determined, and the motion state of the breeder is determined according to the temperature difference information. If the motion state of the breeder does not match the set motion state, a warning operation is performed.

[0041] The second aspect of the embodiment of the present application discloses a breeding management method of breeders, including:

[0042] The first acquisition module is used to obtain the temperature detection image group of each breeding area in the chicken house through the infrared camera group arranged in the chicken house, wherein the temperature image detection group includes a plurality of temperature detection images, and the infrared camera group includes a plurality of infrared cameras, each breeding area corresponds to an infrared camera;

[0043] The second acquisition module is used to obtain the position information of each infrared detection pixel point in the temperature detection image, and to segment the temperature detection image according to the position information to obtain a segmented infrared image associated with each monitored breeder;

[0044] The identification module is configured to input the segmented infrared images associated with each monitored breeder into a pre-constructed breeder temperature identification model to obtain an identification temperature set of each monitored breeder in the temperature detection image.

[0045] The early warning module is configured to match the temperature identification set with a set temperature condition, and if the matching fails, an early warning is performed according to the position of the breeder to remind the corresponding manager.

[0046] The third aspect of the embodiment of the present application discloses an electronic device, comprising a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory, and is configured to execute the breeding management method of the breeder disclosed in the first aspect of the embodiment of the present application.

[0047] The fourth aspect of the embodiment of the present application discloses a computer readable storage medium storing a computer program, wherein the computer program causes a computer to execute the breeding management method of the breeder disclosed in the first aspect of the embodiment of the present application.

[0048] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0049] In the embodiment of the present application, the position information of each infrared detection pixel point in the temperature detection image is obtained, and the image is segmented and processed, so that the segmented infrared images associated with each monitored breeder can be accurately obtained. These images are input into a pre-constructed breeder temperature identification model for identification, so that the actual body temperature of each breeder can be quickly obtained. This intelligent processing method not only improves the management efficiency, but also timely warns when an abnormal temperature is found, reminding the manager to take corresponding measures.

[0050] By monitoring the body temperature of the breeder in real time, possible health problems such as fever can be found in time, so that preventive measures can be taken to avoid the occurrence and spread of diseases. This helps to improve the survival rate of breeders and reduce breeding costs. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0052] Figure 1 is a flowchart of the breeding management method of the breeder disclosed in the embodiment of the present application;

[0053] Figure 2is a specific temperature detection flowchart disclosed by the embodiment of the present application;

[0054] Figure 3 is a flowchart of the hen movement state detection disclosed by the embodiment of the present application;

[0055] Figure 4 is a structure diagram of a hen breeding management system provided by the embodiment of the present application;

[0056] Figure 5 is a structure diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0058] It should be noted that the terms "first", "second", "third", "fourth" and the like in the specification and claims of the present application are used to distinguish different objects, rather than to describe a specific sequence. The terms "include" and "have" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units need not be limited to those clearly listed steps or units, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0059] In livestock breeding, animal body parameters are one of the key growth indicators. However, due to differences between individual animals and differences in the environment, efficient measurement of animal parameters cannot be achieved, and existing general manual monitoring methods have a large degree of subjectivity; fine measurement cannot be achieved, and this method cannot timely and efficiently feedback the actual state of each breed chicken in the whole chicken coop, and fine breeding monitoring and management cannot be achieved. Based on this, the present application embodiment discloses a breeding management method and system for chickens, an electronic device and a storage medium, by acquiring the position information of each infrared detection pixel point in the temperature detection image, and performing segmentation processing on the image, the segmented infrared image associated with each monitored breed chicken can be accurately obtained. These images are input into a pre-constructed chicken temperature identification model for identification, and the actual body temperature of each breed chicken can be quickly obtained. This intelligent processing method not only improves management efficiency, but also timely alerts when an abnormal temperature is found, reminding the management personnel to take appropriate measures. By monitoring the body temperature of the breed chicken in real time, possible health problems such as fever can be found in time, so that preventive measures can be taken to avoid the occurrence and spread of diseases. This helps to improve the survival rate of breed chickens and reduce breeding costs.

[0060] Embodiment one

[0061] Please refer to Figure 1 , Figure 1 is a flowchart of the chicken breeding management method disclosed by the present application embodiment. The execution subject of the method described in the present application embodiment is composed of software or hardware, which can receive relevant information through wired or wireless means and can send certain instructions. Of course, it can also have certain processing and storage functions. The execution subject can control multiple devices, such as remote physical servers or cloud servers and related software, or local hosts or servers and related software that perform related operations on devices placed in a certain place. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places.

[0062] As Figure 1 shown, the chicken breeding management method based on the chicken includes the following steps:

[0063] S101: Obtain a group of temperature detection images of each breeding area in the chicken coop through a group of infrared cameras arranged in the chicken coop, wherein the temperature image detection group includes a plurality of temperature detection images, and the infrared camera group includes a plurality of infrared cameras, each breeding area corresponds to an infrared camera;

[0064] S102: Obtain the position information of each infrared detection pixel point in the temperature detection image, and perform segmentation processing on the temperature detection image according to the position information to obtain a segmented infrared image associated with each monitored breeder hen;

[0065] S103: Input the segmented infrared image associated with each monitored breeder hen into a pre-constructed breeder hen temperature identification model for identification to obtain an identification temperature set of each monitored breeder hen in the temperature detection image.

[0066] S104: Match the temperature identification set with the set temperature condition, and if they do not match, perform a pre-warning according to the position of the breeder hen to remind the corresponding manager.

[0067] Generally, the area of a chicken coop is large, and the area covered by each infrared camera is limited, so multiple cameras need to be set up to achieve comprehensive monitoring of the entire chicken coop. When each infrared camera acquires an infrared image, it is not only for one breeder hen, but for all breeder hens in a region.

[0068] The scheme of the embodiment of the present application can capture the temperature information of each breeding area in real time through the infrared camera group arranged in the chicken coop, forming a temperature detection image group. This method has higher efficiency and accuracy compared to traditional manual temperature measurement or fixed-point temperature measurement, and can achieve comprehensive and real-time monitoring of the temperature in the entire chicken coop. Before measurement, the temperature of the measurement environment should be ensured to be stable to avoid the influence of environmental temperature fluctuations on the measurement results. The stability of the environmental temperature can be achieved by adjusting the ventilation and heating equipment of the chicken coop. During measurement, external interference factors such as direct sunlight and strong wind should be minimized. These interference factors may cause errors in the measurement results of the infrared thermal imager. The measurement is performed when the breeder hens are in a stationary state to avoid the influence of heat generated by movement on the measurement results. Measurement can usually be performed in the morning or evening. During measurement, the measurement distance between the infrared thermal imager and the breeder hens should be kept consistent to ensure the accuracy of the measurement results. Different measurement distances may cause differences in the temperature information captured by the infrared thermal imager.

[0069] This method can also help managers better understand the temperature distribution in the chicken coop, so as to adjust the breeding environment such as ventilation, warmth, etc. according to the actual situation, provide a more suitable living environment for breeder hens, and promote their healthy growth. In the specific implementation, further breeding environment control can be realized through the detected temperature distribution.

[0070] When the specific temperature condition configuration is performed, there can be multiple configuration methods. One is to use a single temperature interval method, and the other is to set different threshold intervals for different parts. In this way, when subsequent matching is performed, each part must meet the conditions to not perform a warning. If a part does not meet the interval, a warning is performed.

[0071] When the specific temperature is identified, the specific breeder behavior pattern needs to be combined for identification. If the breeder has just exercised, the overall temperature will be higher. If it is not at a higher overall temperature level, a warning is performed. That is, the breeder's exercise state can be combined for comprehensive evaluation of the temperature.

[0072] More preferably, the temperature detection image group of the breeding area in the henhouse is obtained by the infrared camera group arranged in the henhouse, comprising:

[0073] S1011: Obtain the temperature detection image of each breeding area in the incubation henhouse by the infrared camera group arranged in the incubation henhouse, wherein the temperature detection image includes the temperature image of the breeder to be incubated;

[0074] S1012: Obtain the temperature detection image of each breeding area in the brooding and growing henhouse by the infrared camera group arranged in the brooding and growing henhouse, wherein the temperature detection image includes the temperature image of the brooding and growing breeder;

[0075] S1013: Obtain the temperature detection image of each breeding area in the laying henhouse by the infrared camera arranged in the laying henhouse, wherein the temperature detection image includes the temperature image of the laying breeder; the infrared camera includes first position information and second position information, the first position information is used to represent the installation position of the infrared camera, and the second position information is used to represent the position number information of the infrared camera in the corresponding henhouse;

[0076] The segmented infrared image associated with each monitored breeder is input into the pre-constructed breeder temperature identification model for identification, comprising:

[0077] According to the first position information, the corresponding breeder temperature identification model is determined, wherein the breeder temperature identification model includes an incubation breeder temperature model, a brooding and growing breeder temperature model, and a laying breeder temperature model;

[0078] The segmented infrared image associated with each monitored breeder is input into the pre-constructed breeder temperature identification model for identification.

[0079] By setting up infrared camera groups in the hatching house, brooding and growing house, and laying house, precise temperature monitoring can be conducted for breeders at different growth stages. This phased management approach better meets the actual needs of breeder growth and helps ensure that the environmental conditions at each growth stage are optimal. The temperature parameters for breeders at each stage are different, so corresponding parameter updates need to be made for each stage. During the incubation stage, temperature is a key factor affecting embryo development. The incubation temperature of breeder eggs is generally controlled at around 37.8°C (incubation for 1 to 19 days), while during the hatching period (19 to 21 days), the temperature decreases slightly, usually between 36.9°C and 37.2°C. At the same time, factors such as relative humidity and ventilation in the incubation environment also affect the accurate control of temperature. The environmental temperature during the incubation stage should be maintained between 22°C and 26°C, with good ventilation. The brooding stage is a critical period for the growth and development of chicks. During the early brooding period (1 to 3 days), the temperature in the brooding room needs to be maintained between 40°C and 35°C, and then gradually reduced as the chicks age. For example, at 4 to 7 days, the brooding room temperature decreases to 34°C to 33°C; at 2 weeks old, it decreases to 33°C to 32°C; at 3 weeks old, it decreases to 32°C to 30°C; at 4 weeks old, it decreases to 30°C to 28°C. After that, the brooding room temperature can gradually decrease to normal temperature. In addition, the relative humidity during the brooding stage should be maintained at around 65%.

[0080] During the growth stage, the temperature requirement for breeders is relatively low, but it still needs to be maintained at an appropriate temperature to promote their normal growth and development. Generally, it is appropriate to control the temperature in the chicken house during the growth stage between 20°C and 25°C. At this time, the chicken house should maintain appropriate ventilation and relative humidity to provide a comfortable living environment.

[0081] During the laying stage, breeders need stable and appropriate temperature to maintain their high production performance. The temperature in the laying house during the laying period is generally controlled between 18°C and 25°C, with a higher egg production rate at 13°C to 16°C and a higher feed conversion rate at 15°C to 20°C. However, there are also views that the temperature in the laying house should not exceed 28°C, with the optimal temperature being 22°C to 28°C. At the same time, it is necessary to ensure that the minimum temperature in the laying house is not lower than 13°C and the maximum temperature is not higher than 30°C, in order to avoid the adverse effects of excessively high or low temperature on egg production rate. In different stages, due to different environments, it is necessary to adjust the corresponding detection and recognition model in combination with the specific environment to improve the accuracy of recognition.

[0082] According to the first position information (installation position) of the infrared camera, the corresponding breeder temperature recognition model (incubation breeder temperature model, brooding and growing breeder temperature model, laying breeder temperature model) is determined. This design makes the temperature recognition model more suitable for actual application scenarios and can more accurately identify the actual body temperature of breeders at different growth stages.

[0083] Through the second position information (the position number information of the infrared camera in the corresponding henhouse), the specific position and coverage range of each infrared camera can be clearly known, so that the camera resources can be more reasonably arranged and managed. This helps to avoid overlapping coverage and blind areas between cameras, and improves monitoring efficiency.

[0084] The segmented infrared image is input into the corresponding temperature identification model of the breeding hen to identify, so that the body temperature data of each monitored breeding hen can be quickly obtained. This automatic processing method greatly reduces the frequency and intensity of manual intervention, and improves management efficiency.

[0085] Accurate temperature monitoring and identification can help to discover abnormal changes in the body temperature of the breeding hen in a timely manner, so that preventive measures can be taken to avoid the occurrence and spread of diseases. At the same time, the optimized breeding environment helps to improve the production performance of the breeding hen, such as increasing the hatching rate, survival rate, and egg production rate.

[0086] Through intelligent temperature monitoring and management, the diseases and deaths of the breeding hen caused by unsuitable environment can be reduced, thereby reducing the breeding cost. In addition, reasonable resource allocation and optimization can also reduce energy consumption and labor cost. Through the above-mentioned manner, another aspect that can be achieved is that the corresponding egg production and the number of breeding hens that can be supplied can be accurately predicted.

[0087] More preferably, as shown in Figure 2 The method further comprises:

[0088] S1031: inputting the segmented infrared image associated with each monitored breeding hen into the pre-constructed temperature identification model of the breeding hen to obtain a first region of interest, a second region of interest, a third region of interest, and a fourth region of interest; and obtaining each pixel point parameter associated with the first region of interest, the second region of interest, the third region of interest, and the fourth region of interest;

[0089] S1032: according to the pre-constructed temperature calibration mapping table, performing temperature statistics on each pixel point data associated with the first region of interest, the second region of interest, the third region of interest, and the fourth region of interest to determine average temperature information, maximum temperature information, and minimum temperature information of each region of interest;

[0090] S1033: according to the average temperature information, the maximum temperature information, and the minimum temperature information of each region of interest, obtaining an identification temperature set of each monitored breeding hen.

[0091] By inputting the segmented infrared image into the temperature identification model of the breeder, the first, second, third and fourth regions of interest associated with each monitored breeder can be identified. These regions of interest may correspond to different parts of the breeder's body (such as the head, chest, wings, legs, etc.), allowing for more detailed analysis of the breeder's temperature.

[0092] The pixel point parameters associated with each region of interest are obtained, and the temperature of these pixel point data is counted according to the pre-constructed temperature calibration mapping table, so that the average temperature, maximum temperature and minimum temperature of each region of interest can be accurately calculated. This processing method improves the accuracy of temperature identification and helps to more accurately assess the health status of the breeder.

[0093] By counting the temperature information of each region of interest, the identification temperature set of each monitored breeder can be obtained, which contains temperature data of different parts of the breeder. This comprehensive temperature monitoring method helps to detect small changes in the body temperature of the breeder and take preventive measures in a timely manner to prevent the occurrence of diseases.

[0094] Accurate temperature data provides an important reference for breeding management. According to the identification temperature set of each monitored breeder, more scientific and reasonable breeding plans and management measures can be developed, such as adjusting the breeding density, improving the feeding environment, optimizing the feed formula, etc., so as to improve the growth performance and health level of the breeder.

[0095] The above technical process realizes the automation and intelligentization of breeder temperature monitoring, reduces the frequency and intensity of manual intervention, and improves the efficiency and accuracy of breeding management. At the same time, this intelligent temperature monitoring method also helps to improve the overall intelligent level of the breeding industry and promote the transformation and upgrading of the breeding industry.

[0096] Through the above-mentioned way, comprehensive and fine management can be realized, especially when diseases occur later, the corresponding chicken house management personnel can detect abnormalities earlier. The maximum and minimum temperatures as sensitive indicators can reflect the abnormal changes of the breeder's body temperature earlier. When the breeder is infected with a disease or is in a stress state, its body temperature will often fluctuate significantly. By monitoring these sensitive indicators in real time, potential disease risks can be detected in a timely manner. Combined with temperature data and pre-set threshold range, an abnormal warning mechanism can be established. When the temperature data exceeds the normal range, the system will automatically send a warning signal to remind the chicken house management personnel to take timely measures. This warning mechanism helps to achieve early detection and early intervention of diseases.

[0097] More preferably, before the segmented infrared image associated with each monitored breeder is input into the pre-constructed breeder temperature identification model, it further includes:

[0098] The segmented infrared image is identified and processed according to the set gray threshold to determine the position information of each fence in the segmented infrared image, and the fence image in the segmented infrared image is removed.

[0099] The pixel point parameters on the left and right sides of the position information of each fence are obtained, and the corresponding segmented infrared image is color filled according to the pixel point parameters on the left and right sides of the position information of each fence to obtain the filled segmented infrared image.

[0100] By setting the gray threshold to identify and remove the fence image in the segmented infrared image, the interference factors in the image can be effectively reduced. As a common element in the breeding environment, the fence may block or affect the temperature monitoring of the breeding chicken. After removing the fence image, the subsequent temperature identification model can focus more on the temperature characteristics of the breeding chicken itself, thereby improving the image quality and the accuracy of temperature identification.

[0101] After removing the fence image, the pixel point parameters on the left and right sides of the fence position information are obtained, and color filling operation is performed, which can fill the image gap caused by the removal of the fence. This color filling not only helps to maintain the integrity of the image, but also makes the temperature identification model smoother when processing the image, avoiding recognition errors caused by discontinuous images.

[0102] Through the preprocessing steps of fence identification, removal and color filling of the segmented infrared image, the breeding chicken temperature identification model can be more adapted to image input under different breeding environments and conditions. This enhanced adaptability helps the model to maintain stable performance in complex and variable breeding environments, improving the reliability and accuracy of temperature identification.

[0103] The automation of the preprocessing step can significantly reduce the frequency and intensity of manual intervention, improving the efficiency of breeding management. At the same time, through the optimization of the preprocessing step, the temperature monitoring and analysis process can be faster and more accurate, providing timely and effective support for breeding decisions.

[0104] The above technical requirements reflect the trend of the breeding industry towards intelligent and automated development. By introducing image processing technology and machine learning algorithms, real-time monitoring and analysis of breeding chicken temperature can be achieved, providing strong support for intelligent management of the breeding industry. This intelligent development not only helps to improve breeding efficiency and quality, but also reduces labor costs and environmental pressure.

[0105] More preferably, the breeding chicken temperature identification model is obtained by the following steps:

[0106] A sample training set is obtained, which includes breeding chicken sample images; and the sample training set is divided to obtain a training set, a validation set and a test set;

[0107] performing a preprocessing operation on the sample image of the breeder hen;

[0108] inputting the preprocessed sample image of the breeder hen into the breeder hen temperature recognition model with completed parameters for training and parameter configuration adjustment until the corresponding breeder hen temperature recognition model meets the training requirements, and saving the current best model parameters;

[0109] using the trained breeder hen temperature recognition model to predict the breeder hen test images in the test set to obtain each part image in each breeder hen test image;

[0110] generating a part confusion matrix according to the prediction results and the real labels of the breeder hen temperature recognition model; the rows of the part confusion matrix represent the real parts, and the columns represent the predicted parts;

[0111] calculating the overall performance indicators of the breeder hen temperature recognition model according to the part confusion matrix, wherein the overall performance indicators include accuracy, precision, recall, and F1 score;

[0112] and adjusting and optimizing the breeder hen temperature recognition model according to the analysis results of the part confusion matrix.

[0113] In specific implementation, the sample image and the later recognition detection image should have consistency, for example, if the image with fence is used for early training, then the image with fence should also be used for later input, and the parameter adjustment parameters should be consistent during preprocessing to avoid recognition errors caused by image processing.

[0114] Through the explicit steps, from sample collection to model training, verification, testing, to performance evaluation and optimization, a complete and repeatable process is formed. This helps to ensure the stability and reliability of the model, and facilitates the update and maintenance of the subsequent model. Dividing the sample training set into training set, validation set and test set can make more effective use of data. The training set is used for model learning, the validation set is used for adjusting model parameters to prevent overfitting, and the test set is used for evaluating the final performance of the model. This division method helps to ensure the generalization ability of the model.

[0115] Performing preprocessing operations (such as denoising, enhancement, normalization, etc.) on the breeder hen sample image can significantly improve the training efficiency and recognition accuracy of the model. Preprocessing helps to reduce noise and redundant information in the data, making it easier for the model to learn useful features.

[0116] By inputting the preprocessed images into the parameter-configured temperature identification model for training and adjusting the model parameters according to the results of the validation set, the model can be continuously optimized during the training process. Meanwhile, saving the current best model parameters helps to obtain the best-performing model after training is completed.

[0117] Using the test images of the hens in the test set to predict the trained model and generating a part confusion matrix can comprehensively evaluate the performance of the model. The accuracy, precision, recall rate and F1 score in the confusion matrix can intuitively reflect the performance of the model in different aspects, such as identification accuracy, identification precision of a certain class, recall ability, etc.

[0118] According to the analysis results of the confusion matrix, the performance of the model on specific categories can be adjusted and optimized. For example, if the model performs poorly in identifying a certain category, the identification accuracy can be improved by increasing the number of samples in that category, adjusting the model parameters, or improving the feature extraction method.

[0119] More preferably, after obtaining the set of recognized temperatures for each monitored hen in the temperature detection image, the method further comprises:

[0120] According to the obtained set of all recognized temperatures, a data update operation is performed on the corresponding hen temperature table, which is mapped according to the positions of each hen in the henhouse.

[0121] The breeding management method further comprises:

[0122] The temperature information in the corresponding henhouse is determined by the temperature sensor installed in the henhouse, and the set temperature condition is updated according to the temperature information.

[0123] By associating the set of recognized temperatures with the hen temperature table and mapping according to the positions of each hen in the henhouse, real-time updating and tracking of the hen temperature can be achieved. This data updating mechanism helps managers to timely understand the health status of hens, providing data support for precision breeding.

[0124] The temperature information in the henhouse is determined by the temperature sensor installed in the henhouse, and the set temperature condition is updated according to the information, which can achieve precise control of the breeding environment. This environmental control strategy helps to provide suitable growth environment for hens, improve production performance and health level. And because the indoor temperature will have a certain impact on the accuracy of infrared detection, the surface temperature of the hen detected by infrared image detection is different in high temperature environment and low temperature environment, so it is necessary to combine specific environmental temperature parameters for subsequent comparison and correction.

[0125] The real-time updated temperature table of breeder hens and the temperature information of the henhouse provide a scientific basis for breeding decisions. Management personnel can make more reasonable breeding plans and management measures based on these data, such as adjusting the feeding density, improving the ventilation conditions, optimizing the feed formula, etc., so as to improve the breeding efficiency and economic benefits.

[0126] The temperature table of the breeder hen here is the temperature parameter of each breeder hen. In real-time, the data of all detected temperature parameters are stored, which facilitates subsequent analysis. For example, when a breeder hen in the henhouse is sick, the change in the detected temperature is manifested. Then, due to the acquisition of a large amount of data, the spread can be predicted by combining the temperature change position, the approximate path of the disease spread can be predicted, and the user can be warned in advance. And in the specific implementation, if the disease occurs, the detection accuracy can be improved by reducing the temperature threshold, and the user is reminded of the risk of spread.

[0127] More preferably, as shown in Figure 3 The breeding management method further comprises:

[0128] S105: Obtain temperature monitoring images of each breeding area in the henhouse by the infrared camera group arranged in the henhouse every set time;

[0129] S106: Obtain the position information of each infrared detection pixel point in the temperature monitoring image, and perform segmentation processing on the temperature detection image according to the position information to obtain a segmented infrared image associated with each monitored breeder hen;

[0130] S107: Input the segmented infrared image associated with each monitored breeder hen into a pre-constructed breeder hen temperature recognition model to obtain the head and neck region, the leg and foot region, and the wing region;

[0131] S108: According to the pre-constructed temperature calibration mapping table, the temperature of each pixel point data associated with the head and neck region, the leg and foot region, and the wing region is counted to determine the average temperature information of each region of interest;

[0132] S109: Determine the temperature difference information between the two from the average temperature information of each region of interest obtained from the adjacent detection images, and determine the motion state of the breeder hen according to the temperature difference information. If the motion state of the breeder hen does not match the set motion state, a warning operation is performed.

[0133] The existing methods are generally based on image detection of action behavior. In the embodiments of the present application, a temperature difference detection method is innovatively adopted as a standard for measuring action, so that the parameters can better represent the corresponding functions of the breeding hens. Through image segmentation and region recognition technology, the temperature monitoring image can be accurately segmented into segmented infrared images associated with each monitored breeding hen, and further, the head and neck region, leg and foot region, and wing region, and other regions of interest can be identified. This accurate region recognition helps to realize independent statistics and analysis of the temperature of different parts of the breeding hen, improving the accuracy and reliability of temperature identification.

[0134] By comparing the average temperature information of each region of interest in adjacent detection images, the temperature difference information can be determined, and the motion state of the breeding hen can be judged. This motion state monitoring not only helps to understand the activity of the breeding hen, but also serves as an important basis for health assessment. When the motion state of the breeding hen does not match the set motion state, timely warning can remind the management personnel to take appropriate measures to prevent potential health problems.

[0135] The specific principles of the embodiments of the present application are as follows: leg movement recognition: when the breeding hen walks, runs or stands, the leg muscles will generate heat and radiate to the body surface. Through infrared image recognition technology, this temperature change can be captured, and the leg movement state of the breeding hen can be analyzed accordingly. For example, when the leg muscles of the breeding hen are active, the temperature of the corresponding region in the infrared image will rise, reflecting the activity level of the breeding hen. Wing flapping recognition: when the wings flap, the pectoral muscles of the breeding hen will generate heat, which will be dissipated into the air through the flapping of the wings. Through infrared image recognition technology, this heat dissipation process can be captured, and the flapping of the wings of the breeding hen can be judged accordingly. In the specific implementation, the change in the coverage area of the wing region can also be used to determine the corresponding motion state. The change in the coverage area of the wing region in combination with the temperature change of the chicken breast can be used to realize the recognition of the corresponding motion state. Through high-precision infrared image recognition technology and appropriate image processing algorithms, the recognition of this motion state can be realized. Head rotation and pecking behavior recognition: head rotation and pecking behavior will cause temperature changes in the head region of the breeding hen. For example, when the breeding hen rotates its head, the neck muscles will generate heat; when the breeding hen pecks, the temperature around the beak will also change. Through infrared image recognition technology, these subtle temperature changes can be captured, and the head rotation and pecking behavior of the breeding hen can be analyzed accordingly. Different detection thresholds are set for different temperature change intervals to determine the motion state of the breeding hen; these changes in motion state will cause subtle differences in the temperature distribution of the body surface of the breeding hen, which can be captured and processed to realize the recognition of the motion state of the breeding hen. The above temperature detection method can better realize the monitoring of animal behavior, and further provide various breeding state information to the corresponding users.

[0136] The embodiment of the present application can also detect the ambient temperature, which installs infrared cameras in each key area (such as the entrance, exit, central area, corner, etc.) of the henhouse to ensure that the entire henhouse can be fully covered. The setting can also realize the overall henhouse closed detection in combination with the above-mentioned infrared temperature detection method; and the construction of the henhouse is evaluated.

[0137] In the embodiment of the present application, by acquiring the position information of each infrared detection pixel point in the temperature detection image and performing segmentation processing on the image, the segmented infrared image associated with each monitored breeder hen can be accurately obtained. These images are input into the pre-constructed breeder hen temperature identification model for identification, so that the actual body temperature of each breeder hen can be quickly obtained. This intelligent processing method not only improves the management efficiency, but also timely warns when an abnormal temperature is found, reminding the management personnel to take appropriate measures.

[0138] By monitoring the body temperature of the breeder hen in real time, possible health problems such as fever can be found in time, so that preventive measures can be taken to avoid the occurrence and spread of diseases. This helps to improve the survival rate of breeder hens and reduce breeding costs.

[0139] Embodiment two

[0140] Please refer to Figure 4 , Figure 4 is a structural schematic diagram of the breeder hen breeding management system disclosed by the embodiment of the present application. As Figure 4 shown, the breeder hen breeding management system can include:

[0141] The first acquisition module 21 is configured to acquire a temperature detection image group of each breeding area in the henhouse by an infrared camera group arranged in the henhouse, wherein the temperature image detection group includes a plurality of temperature detection images, and the infrared camera group includes a plurality of infrared cameras, each breeding area corresponding to an infrared camera;

[0142] The second acquisition module 22 is configured to acquire position information of each infrared detection pixel point in the temperature detection image, and perform segmentation processing on the temperature detection image according to the position information to obtain a segmented infrared image associated with each monitored breeder hen;

[0143] The identification module 23 is configured to input the segmented infrared image associated with each monitored breeder hen into a pre-constructed breeder hen temperature identification model for identification to obtain an identification temperature set of each monitored breeder hen in the temperature detection image;

[0144] The warning module 24 is configured to match the temperature identification set with a set temperature condition, and if the matching fails, a warning is given according to the position of the breeder hen to remind the corresponding management personnel.

[0145] The position information of each infrared detection pixel point in the temperature detection image is acquired, and the image is segmented, so that the segmented infrared images associated with each monitored breeder hen can be accurately obtained. The images are input into a pre-constructed breeder hen temperature identification model for identification, so that the actual body temperature of each breeder hen can be quickly obtained. This intelligent processing method not only improves the management efficiency, but also timely warns when an abnormal temperature is found, reminding the management personnel to take corresponding measures.

[0146] By monitoring the body temperature of the breeder hen in real time, possible health problems such as fever can be found in time, so that preventive measures can be taken to avoid the occurrence and spread of diseases. This helps to improve the survival rate of breeder hens and reduce breeding costs.

[0147] Embodiment three

[0148] Please refer to Figure 5 , Figure 5 is a structural schematic diagram of an electronic device disclosed by an embodiment of the present application. The electronic device can be a computer, a server, and the like. Of course, in some cases, it can also be a smart device such as a mobile phone, a tablet computer, and a monitoring terminal, and an image acquisition device with processing function. As shown in Figure 5 , the electronic device can include:

[0149] a memory 510 storing executable program codes;

[0150] a processor 520 coupled with the memory 510;

[0151] The processor 520 calls the executable program codes stored in the memory 510 to execute part or all of the steps of the breeder hen breeding management method in embodiment one.

[0152] An embodiment of the present application discloses a computer readable storage medium storing a computer program, wherein the computer program causes a computer to execute part or all of the steps of the breeder hen breeding management method in embodiment one.

[0153] An embodiment of the present application further discloses a computer program product, wherein when the computer program product runs on a computer, the computer program product causes the computer to execute part or all of the steps of the breeder hen breeding management method in embodiment one.

[0154] An embodiment of the present application further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, and when the computer program product runs on a computer, the computer program product causes the computer to execute part or all of the steps of the breeder hen breeding management method in embodiment one.

[0155] In various embodiments of the present application, it should be understood that the size of the sequence number of the processes does not mean the inevitable sequence of execution, and the execution sequence of the processes should be determined according to their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0156] The units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0157] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0158] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-accessible memory. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of steps for executing the method described in each embodiment of the present application, or part or all of the steps.

[0159] In the embodiments provided by the present application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean that B is determined only according to A, but B can also be determined according to A and / or other information.

[0160] Those skilled in the art can understand that part or all of the steps in the various methods of the embodiments can be completed by instructing the relevant hardware by a program, and the program can be stored in a computer readable storage medium, including Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk memories, magnetic disk memories, magnetic tape memories, or any other computer readable medium capable of carrying or storing data.

[0161] The above discloses a breeding management method and system for hens, an electronic device and a storage medium in detail. The principles and implementation manners of the present application are described by applying specific examples. The above description of the embodiments is only used to help understand the method and core idea of the present application. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for raising and managing breeding chickens, characterized in that, include: Temperature detection images of various breeding areas in the chicken house are obtained by setting up an infrared camera group in the chicken house. The temperature detection image group includes multiple temperature detection images, and the infrared camera group includes multiple infrared cameras, with each breeding area corresponding to one infrared camera. The position information of each infrared detection pixel in the temperature detection image is obtained, and the temperature detection image is segmented according to the position information to obtain a segmented infrared image associated with each monitored breeder chicken. The segmented infrared images associated with each monitored breeder chicken are input into a pre-built breeder chicken temperature recognition model for recognition to obtain the recognition temperature set of each monitored breeder chicken in the temperature detection image; The identified temperature set is matched with the set temperature conditions. If they do not match, an early warning is issued based on the location of the breeding chickens to remind the relevant management personnel. The aquaculture management method also includes: Temperature monitoring images of various breeding areas in the chicken house are obtained at set intervals using infrared camera arrays installed in the chicken house. The position information of each infrared detection pixel in the temperature monitoring image is obtained, and the temperature monitoring image is segmented according to the position information to obtain a segmented infrared image associated with each monitored breeder chicken. The segmented infrared images associated with each monitored breeder chicken are input into a pre-built breeder chicken temperature recognition model to obtain the head and neck region, leg and foot region, and wing region. Temperature statistics are performed on the pixel data associated with the head and neck region, leg and foot region, and wing region based on a pre-built temperature calibration mapping table to determine the average temperature information of each region of interest. The temperature difference between adjacent detection images is determined based on the average temperature information of each region of interest. The movement state of the breeding chicken is then determined based on the temperature difference information. If the movement state of the breeding chicken does not match the set movement state, an early warning operation is performed.

2. The breeding and management method for breeder chickens as described in claim 1, characterized in that, The method of acquiring temperature detection images of the breeding area in the chicken coop using an infrared camera group installed in the chicken coop includes: Temperature detection images of various breeding areas in the chicken house are obtained by an infrared camera group installed in the hatchery, and the temperature detection images include temperature images of the breeding chickens to be hatched. Temperature detection images of various breeding areas in the chicken house are obtained by an infrared camera group installed in the brooding and rearing chicken house. The temperature detection images include temperature images of brooding and rearing breeding chickens. Temperature detection images of various breeding areas in the chicken house are obtained by infrared cameras installed in the laying hen house. The temperature detection images include temperature images of laying hens. The infrared camera includes first location information and second location information. The first location information is used to characterize the installation location of the infrared camera, and the second location information is used to characterize the location number information of the infrared camera in the corresponding chicken house. The step of inputting the segmented infrared images associated with each monitored breeder chicken into a pre-constructed breeder chicken temperature recognition model for identification includes: Based on the first location information, a corresponding breeder chicken temperature identification model is determined. The breeder chicken temperature identification model includes an incubation breeder chicken temperature model, a brooding and rearing breeder chicken temperature model, and a laying breeder chicken temperature model. The segmented infrared images associated with each monitored breeder chicken are input into a pre-built breeder chicken temperature recognition model for identification.

3. The breeding and management method for breeder chickens as described in claim 1, characterized in that, The step of inputting the segmented infrared images associated with each monitored breeder chicken into a pre-constructed breeder chicken temperature recognition model for identification to obtain the identification temperature set of each monitored breeder chicken in the temperature detection image includes: The segmented infrared images associated with each monitored breeder chicken are input into a pre-built breeder chicken temperature recognition model to obtain a first region of interest, a second region of interest, a third region of interest, and a fourth region of interest; the parameters of each pixel point associated with the first region of interest, the second region of interest, the third region of interest, and the fourth region of interest are obtained; Based on a pre-built temperature calibration mapping table, temperature statistics are performed on the pixel data associated with the first region of interest, the second region of interest, the third region of interest, and the fourth region of interest to determine the average temperature, the highest temperature, and the lowest temperature information of each region of interest. The identification temperature set for each monitored breed of chicken is obtained based on the average temperature, maximum temperature, and minimum temperature information of each region of interest.

4. The breeding and management method for breeder chickens as described in claim 3, characterized in that, Before inputting the segmented infrared images associated with each monitored breeder chicken into the pre-built breeder chicken temperature recognition model, the method further includes: The segmented infrared image is processed according to a set grayscale threshold to determine the position information of each fence in the segmented infrared image, and the fence image in the segmented infrared image is removed. Obtain the pixel parameters on the left and right sides of the position information of each fence, and perform color filling operation on the corresponding segmented infrared image according to the pixel parameters on the left and right sides of the position information of each fence to obtain the filled segmented infrared image.

5. The breeding and management method for breeder chickens as described in claim 1, characterized in that, The breeder chicken temperature recognition model is obtained through the following steps: Obtain a sample training set, which includes images of breeding chickens; and perform a partitioning operation on the sample training set to obtain a training set, a validation set, and a test set. The images of the breeding chicken samples were preprocessed. The pre-processed breeder chicken sample images are input into the breeder chicken temperature recognition model with configured parameters for training and parameter adjustment until the corresponding breeder chicken temperature recognition model meets the training requirements, and the current best model parameters are saved. The trained breeder chicken temperature recognition model is used to predict the breeder chicken test images in the test set to obtain images of each part in each breeder chicken test image. Based on the prediction results of the breeder chicken temperature recognition model and the actual annotations, a part confusion matrix is ​​generated; the rows of the part confusion matrix represent the actual parts, and the columns represent the predicted parts. The overall performance index of the breeder chicken temperature recognition model is calculated based on the location confusion matrix. The overall performance index includes accuracy, precision, recall and F1 score. The temperature recognition model for breeder chickens is adjusted and optimized based on the analysis results of the confusion matrix of the aforementioned parts.

6. The breeding and management method for breeder chickens as described in claim 1, characterized in that, After obtaining the identified temperature set of each monitored breeder chicken in the temperature detection image, the method further includes: The corresponding breeder chicken temperature table is updated based on all the obtained temperature sets. The breeder chicken temperature table is mapped according to the location of each breeder chicken in the chicken house. The aforementioned aquaculture management method also includes: Temperature information within the chicken coop is determined by temperature sensors installed inside the coop, and the set temperature conditions are updated based on this temperature information.

7. A breeding management system for breeder chickens, characterized in that, include: First acquisition module: used to acquire temperature detection image groups of various breeding areas in the chicken house through an infrared camera group set in the chicken house, wherein the temperature detection image group includes multiple temperature detection images, the infrared camera group includes multiple infrared cameras, and each breeding area corresponds to one infrared camera; The second acquisition module is used to acquire the position information of each infrared detection pixel in the temperature detection image, and to segment the temperature detection image according to the position information to obtain a segmented infrared image associated with each monitored breeding chicken. Identification module: used to input the segmented infrared images associated with each monitored breeder chicken into the pre-built breeder chicken temperature identification model for identification in order to obtain the identification temperature set of each monitored breeder chicken in the temperature detection image; Early warning module: used to match the identified temperature set with the set temperature conditions. If they do not match, an early warning will be issued based on the location of the breeding chickens to remind the relevant management personnel. The aquaculture management system also includes: Temperature monitoring images of various breeding areas in the chicken house are obtained at set intervals using infrared camera arrays installed in the chicken house. The position information of each infrared detection pixel in the temperature monitoring image is obtained, and the temperature monitoring image is segmented according to the position information to obtain a segmented infrared image associated with each monitored breeder chicken. The segmented infrared images associated with each monitored breeder chicken are input into a pre-built breeder chicken temperature recognition model to obtain the head and neck region, leg and foot region, and wing region. Temperature statistics are performed on the pixel data associated with the head and neck region, leg and foot region, and wing region based on a pre-built temperature calibration mapping table to determine the average temperature information of each region of interest. The temperature difference between adjacent detection images is determined based on the average temperature information of each region of interest. The movement state of the breeding chicken is then determined based on the temperature difference information. If the movement state of the breeding chicken does not match the set movement state, an early warning operation is performed.

8. An electronic device, characterized in that, include: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the breeding and management method for breeding chickens as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program causes a computer to perform the breeding and management method for breeding chickens as described in any one of claims 1 to 6.

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