Automatic acquisition and analysis system for laying hen breeding data

Abstract

The invention relates to the technical field of laying hen breeding, and discloses an automatic acquisition and analysis system for laying hen breeding data. The information acquisition unit is used for collecting physiological parameters, behavior data, environmental parameters and health indexes of laying hens in real time; the disease monitoring unit is used for analyzing the collected data and identifying physiological abnormality and behavior change before the disease occurs; the antibody level monitoring unit is used for evaluating the immune response capability of the laying hens to specific pathogens; the environment adaptability evaluation unit is used for evaluating the adaptability of the laying hens to the extreme environment according to the environment parameter change and the physiological response of the laying hens; the breeding decision support unit is used for integrating all data and analyzing the production performance, disease resistance and environmental adaptability of laying hens; an optimal feeding scheme is recommended, medicine use is reduced, and resource allocation is optimized.

Description

Technical Field

[0001] The invention relates to the technical field of laying hen breeding, and in particular to an automatic collection and analysis system for laying hen breeding data. Background Art

[0002] As an important part of agricultural production, laying hen farming faces the challenges of improving production efficiency, ensuring food safety, reducing disease risks and achieving sustainable development. Traditional manual management and monitoring methods are difficult to meet the needs of large-scale and refined farming.

[0003] Therefore, the research and development and application of an automatic collection and analysis system for laying hen breeding data is particularly important. This system aims to achieve comprehensive monitoring of laying hen physiology, behavior, environmental adaptability and other aspects through automated and intelligent technical means, providing a scientific basis for disease prevention, breeding optimization and green farming.

[0004] After searching, the invention patent with Chinese patent number CN104642263B discloses a new breed of modern high-yield laying hens, a breeding method and an application. Compared with the prior art, the invention patent with Chinese patent number CN104642263B provides an automatic collection and analysis system for laying hen breeding data, and a new breed of modern high-yield laying hens with egg production performance equivalent to that of the introduced high-yield laying hens, such as egg production rate, feed conversion rate, egg weight, and egg production peak duration;

[0005] However, while paying attention to egg production performance such as egg production rate, feed conversion rate, egg weight, and duration of peak egg production, disease outbreaks and environmental adaptability are also major threats to production performance and economic benefits. By evaluating individual laying hens with strong disease resistance and good adaptability, high egg production performance can be maintained more stably, reducing production interruptions caused by disease or environmental pressure, thereby improving overall breeding efficiency and economic benefits. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art, such as disease outbreaks and potential environmental adaptability problems, and to propose an automatic data collection and analysis system for laying hen breeding.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An automatic data collection and analysis system for laying hen breeding, the collection and analysis module includes:

[0009] Information collection unit: real-time collection of laying hens’ physiological parameters, behavioral data, environmental parameters and health indicators;

[0010] Disease monitoring unit: analysis of collected data to identify physiological abnormalities and behavioral changes before disease occurs;

[0011] Antibody Level Monitoring Unit: Evaluate the immune response of laying hens to specific pathogens;

[0012] Environmental adaptability assessment unit: assesses the adaptability of laying hens to extreme environments based on changes in environmental parameters and their physiological responses;

[0013] Breeding decision support unit: Integrate all data to analyze the production performance, disease resistance and environmental adaptability of laying hens; recommend the best feeding plan, reduce drug use and optimize resource allocation.

[0014] The disease monitoring unit includes an early warning unit, and the antibody level detection unit is provided with an immune response analysis unit.

[0015] The information collection unit is equipped with image sensors, temperature and humidity sensors, light sensors, and ammonia concentration sensors to collect environmental variable information in the chicken house;

[0016] The information collection unit captures the activity images of laying hens based on image sensors, uses image processing technology to identify pecking, resting, and abnormal behaviors, and analyzes and determines the health status of the chickens;

[0017] Environmental data is collected regularly or continuously and sent to the disease monitoring unit via wired or wireless transmission. The collection frequency and accuracy are set according to breeding needs, once every minute or every hour.

[0018] Disease monitoring units are based on flexible temperature patches, heart rate monitoring chest straps or micro-biosensors, which transmit physiological parameters of body temperature, heart rate and respiratory rate in real time;

[0019] Through AI image recognition technology, the behavioral characteristics of laying hens are extracted from the video stream, including gait analysis, standing time ratio, and pecking behavior frequency;

[0020] For physiological data, apply machine learning models to identify physiological indicators that deviate from the normal range;

[0021] Train behavior classifiers based on deep learning models to distinguish normal and abnormal behavior patterns;

[0022] Combining physiological and behavioral characteristics, feature learning and classification are performed through a multi-layer neural network:

[0023] h l =σ(W l h l-1 +b l )

[0024] σ is the activation function, W l is the weight matrix, b l is the bias vector;

[0025] Establish a comprehensive disease prediction model for laying hens based on deep models.

[0026] Based on the prediction model of the antibody level monitoring unit, the early warning unit inputs the integrated feature vector into the deployed comprehensive disease prediction model, and the model outputs the disease risk score or disease probability of laying hens;

[0027] According to the risk score output by the model, set the risk level standards as low risk, medium risk, and high risk;

[0028] According to the actual needs of disease prevention and the distribution of model output, set the warning threshold and initiate the warning when the predicted risk reaches medium risk or above;

[0029] When the disease risk score of laying hens exceeds the preset threshold, the early warning mechanism is automatically triggered, and an early warning message is sent to designated breeders through a pre-set notification channel. The message includes the laying hen number, current risk level, recommended inspection or intervention measures, and activates the antibody level monitoring unit and environmental adaptability assessment unit.

[0030] The antibody level monitoring unit draws blood samples from the chickens;

[0031] Using enzyme-linked immunosorbent assay technology, specific antibodies are coated on a solid phase carrier to react with the antigen in the sample, and then enzyme-labeled secondary antibodies are added to quantitatively analyze the antibody level through a color reaction;

[0032] Mark samples with barcode or RFID technology, use scanners to automatically read information, use statistical software to organize data, calculate average antibody levels, compare with historical data, and test and evaluate antibody level trends and herd immunity status;

[0033] Construct a dynamic model of antibody levels, taking into account the dynamic process of antibody production, attenuation, and disease transmission;

[0034] Using historical antibody testing data, estimate model parameters, antibody production rate, decay rate, and transmission coefficient;

[0035] Verify the model's predictive ability through simulation experiments or comparison with actual epidemic data;

[0036] When training the model, the model is optimized based on:

[0037]

[0038] y i is the true label, p i is the predicted probability, p i is the sample size;

[0039] Finally, a dynamic model of antibody levels was trained to predict the risk of disease transmission and determine the optimal vaccination time, dose, and recipients;

[0040] The immune response analysis unit simulates different disease transmission scenarios in the model to predict the disease's spread rate in the chicken flock, the number of infected people, and possible epidemic trends;

[0041] According to the prediction results of the model, formulate or adjust the vaccination plan, recommend the best time for vaccination, and vaccinate chickens with low antibody levels;

[0042] When the model predicts that immunity levels are insufficient to protect against disease threats, the early warning system is automatically triggered.

[0043] The environmental adaptability assessment unit analyzes the correlation between environmental changes and physiological indicators based on the chicken house environmental data and physiological data, combined with environmental monitoring data. In high temperature environments, chickens drink more water and eat less, and the chickens' physiological adjustment ability to environmental changes is assessed.

[0044] Determine the key indicators for evaluating adaptability, including survival rate, egg production, weight growth rate, and disease incidence, and quantify them;

[0045] Assign weights to each indicator based on its importance and design an adaptability scoring model;

[0046] The total adaptability score is calculated for each chicken or each flock based on the performance of each indicator. The higher the score, the better the adaptability to the current environment.

[0047] The breeding decision support unit collects genetic information of chickens, including breed, parental information, and genetic markers;

[0048] Based on the environmental adaptability assessment unit, the adaptability score is associated with genetic information to identify genetic markers or gene loci related to environmental adaptability;

[0049] Predicting an individual's genetic value based on consideration of inter-family genetic correlations and environmental effects to assess their genetic potential, based on:

[0050] where GEBV is the expected breeding value of the genotype;

[0052] is an estimate based on additive genetic effects;

[0054] It is an estimate based on non-additive genetic effects, obtained by iteratively solving a system of mixed model equations;

[0055] Based on the prediction results, identify genetic markers or gene loci that are significantly associated with environmental adaptability, as well as the genetic values ​​of individuals;

[0056] Based on the results of genetic evaluation, individuals with strong adaptability are selected as breeders.

[0057] Green breeding management module monitors feed consumption, water use, and optimizes feeding plans and water cycles based on breeding results;

[0058] Analyze chicken manure production, guide organic fertilizer production and waste resource utilization;

[0059] According to the environmental adaptability assessment results, the environmental parameters, temperature and ventilation in the chicken house are automatically adjusted to reduce energy consumption;

[0060] Finally, a green breeding report is generated to evaluate the environmental impact of the breeding process.

[0061] The present invention has the following beneficial effects:

[0062] 1. In the present invention, by establishing a comprehensive prediction model for laying hens, timely warnings are issued when sick chickens appear, and subsequent modules are responded to for subsequent detection of laying hen flocks, which can reduce the occurrence of diseases and fluctuations in production performance, maintain a stable egg production rate and high-quality egg output, thereby improving the overall economic benefits of breeding.

[0063] 2. In the present invention, by evaluating the disease resistance and ability to adapt to extreme environments of laying hens after abnormalities are found in the model, corresponding decisions can be made in the early stages of the disease or even before symptoms appear, which helps to take timely measures to reduce the spread of the disease and economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a system block diagram of an automatic layer breeding data collection and analysis system proposed by the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of 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.

[0066] Embodiment 1

[0067] like Figure 1 As shown, the present invention proposes an automatic data collection and analysis system for laying hen breeding, and the collection and analysis module includes:

[0068] Information collection unit: real-time collection of laying hens’ physiological parameters, behavioral data, environmental parameters and health indicators;

[0069] Disease monitoring unit: analysis of collected data to identify physiological abnormalities and behavioral changes before disease occurs;

[0070] Antibody Level Monitoring Unit: Evaluate the immune response of laying hens to specific pathogens;

[0071] Environmental adaptability assessment unit: assesses the adaptability of laying hens to extreme environments based on changes in environmental parameters and their physiological responses;

[0072] Breeding decision support unit: Integrate all data to analyze the production performance, disease resistance and environmental adaptability of laying hens; recommend the best feeding plan, reduce drug use and optimize resource allocation.

[0073] The disease monitoring unit includes an early warning unit, and the antibody level detection unit is provided with an immune response analysis unit.

[0074] The information collection unit is equipped with image sensors, temperature and humidity sensors, light sensors, and ammonia concentration sensors to collect environmental variable information in the chicken house;

[0075] The information collection unit captures the activity images of laying hens based on image sensors, uses image processing technology to identify pecking, resting, and abnormal behaviors, and analyzes and determines the health status of the chickens;

[0076] Environmental data is collected regularly or continuously and sent to the disease monitoring unit via wired or wireless transmission. The collection frequency and accuracy are set according to breeding needs, once every minute or every hour.

[0077] Disease monitoring units are based on flexible temperature patches, heart rate monitoring chest straps or micro-biosensors, which transmit physiological parameters of body temperature, heart rate and respiratory rate in real time;

[0078] Through AI image recognition technology, the behavioral characteristics of laying hens are extracted from the video stream, including gait analysis, standing time ratio, and pecking behavior frequency;

[0079] For physiological data, apply machine learning models to identify physiological indicators that deviate from the normal range;

[0080] Train behavior classifiers based on deep learning models to distinguish normal and abnormal behavior patterns;

[0081] Combining physiological and behavioral characteristics, feature learning and classification are performed through a multi-layer neural network:

[0082] h l =σ(W l h l-1 +b l )

[0083] σ is the activation function, W l is the weight matrix, b l is the bias vector;

[0084] Establish a comprehensive disease prediction model for laying hens based on deep models.

[0085] Based on the prediction model of the antibody level monitoring unit, the early warning unit inputs the integrated feature vector into the deployed comprehensive disease prediction model, and the model outputs the disease risk score or disease probability of laying hens;

[0086] According to the risk score output by the model, set the risk level standards as low risk, medium risk, and high risk;

[0087] According to the actual needs of disease prevention and the distribution of model output, set the warning threshold and initiate the warning when the predicted risk reaches medium risk or above;

[0088] When the disease risk score of laying hens exceeds the preset threshold, the early warning mechanism is automatically triggered, and an early warning message is sent to designated breeders through a pre-set notification channel. The message includes the laying hen number, current risk level, recommended inspection or intervention measures, and activates the antibody level monitoring unit and environmental adaptability assessment unit.

[0089] In this embodiment, the formula h l =σ(W l h l-1 +b l ) describes a basic computing unit in a neural network and reflects how information is passed from one layer to the next;

[0090] h l Represents the activation value or output value of the lth layer, that is, the signal after nonlinear transformation. In the context of laying hen breeding data analysis, these activation values ​​can represent more abstract and high-level feature representations obtained from the original input data (such as physiological indicators and behavioral characteristics) after multi-layer processing, which helps to identify the health status, disease resistance or adaptability of laying hens;

[0091] W l is the weight matrix of the lth layer, which determines the previous layer h l-1 How each feature affects the current layer h l For each node, in the context of laying hen data analysis, the weight matrix is ​​continuously adjusted through the training process to learn the complex relationship from input data to predicted targets (such as disease risk, production performance);

[0092] h l-1 It is l-1 The activation value of the layer, that is, the output of the previous layer, serves as the input of the current layer.

[0093] b l is the bias term of the lth layer, which is a vector used to adjust the output range of the activation function to ensure that the model can fit a wider data distribution. In the laying hen breeding application, the bias term helps the model adapt to the deviation in the data and improve the accuracy of the prediction;

[0094] σ is the activation function. In laying hen data analysis, the activation function helps the model capture nonlinear patterns in laying hen physiological and behavioral data, such as nonlinear trends in disease development or adaptive changes.

[0095] Embodiment 2

[0096] like Figure 1 As shown, based on the first embodiment, the antibody level monitoring unit extracts blood samples from chickens;

[0097] Using enzyme-linked immunosorbent assay technology, specific antibodies are coated on a solid phase carrier to react with the antigen in the sample, and then enzyme-labeled secondary antibodies are added to quantitatively analyze the antibody level through a color reaction;

[0098] Mark samples with barcode or RFID technology, use scanners to automatically read information, use statistical software to organize data, calculate average antibody levels, compare with historical data, and test and evaluate antibody level trends and herd immunity status;

[0099] Construct a dynamic model of antibody levels, taking into account the dynamic process of antibody production, attenuation, and disease transmission;

[0100] Using historical antibody testing data, estimate model parameters, antibody production rate, decay rate, and transmission coefficient;

[0101] Verify the model's predictive ability through simulation experiments or comparison with actual epidemic data;

[0102] When training the model, the model is optimized based on:

[0103]

[0104] y i is the true label, p i is the predicted probability, p i is the sample size;

[0105] Finally, a dynamic model of antibody levels was trained to predict the risk of disease transmission and determine the optimal vaccination time, dose, and recipients;

[0106] The immune response analysis unit simulates different disease transmission scenarios in the model to predict the disease's spread rate in the chicken flock, the number of infected people, and possible epidemic trends;

[0107] According to the prediction results of the model, formulate or adjust the vaccination plan, recommend the best time for vaccination, and vaccinate chickens with low antibody levels;

[0108] When the model predicts that immunity levels are insufficient to protect against disease threats, the early warning system is automatically triggered.

[0109] The environmental adaptability assessment unit analyzes the correlation between environmental changes and physiological indicators based on the chicken house environmental data and physiological data, combined with environmental monitoring data. In high temperature environments, chickens drink more water and eat less, and the chickens' physiological adjustment ability to environmental changes is assessed.

[0110] Determine the key indicators for evaluating adaptability, including survival rate, egg production, weight growth rate, and disease incidence, and quantify them;

[0111] Assign weights to each indicator based on its importance and design an adaptability scoring model;

[0112] The total adaptability score is calculated for each chicken or each flock based on the performance of each indicator. The higher the score, the better the adaptability to the current environment.

[0113] The breeding decision support unit collects genetic information of chickens, including breed, parental information, and genetic markers;

[0114] Based on the environmental adaptability assessment unit, the adaptability score is associated with genetic information to identify genetic markers or gene loci related to environmental adaptability;

[0115] Predicting an individual's genetic value based on consideration of inter-family genetic correlations and environmental effects to assess their genetic potential, based on:

[0116] Where GEBV is the expected breeding value of the genotype;

[0117] is an estimate based on additive genetic effects;

[0118] It is an estimate based on non-additive genetic effects, obtained by iteratively solving a system of mixed model equations;

[0119] Based on the prediction results, identify genetic markers or gene loci that are significantly associated with environmental adaptability, as well as the genetic values ​​of individuals;

[0120] Based on the results of genetic evaluation, individuals with strong adaptability are selected as breeders.

[0121] Green breeding management module monitors feed consumption, water use, and optimizes feeding plans and water cycles based on breeding results;

[0122] Analyze chicken manure production, guide organic fertilizer production and waste resource utilization;

[0123] According to the environmental adaptability assessment results, the environmental parameters, temperature and ventilation in the chicken house are automatically adjusted to reduce energy consumption;

[0124] Finally, a green breeding report is generated to evaluate the environmental impact of the breeding process.

[0125] In this embodiment, the genetic marker data of laying hens, including SNP (single nucleotide polymorphism), etc., are collected, and the breed and parent information of each chicken are recorded to construct the pedigree structure of the flock;

[0126] By recording the egg production, egg quality, disease occurrence performance indicators of each chicken, and environmental adaptability assessment unit The adaptability rating provided;

[0127] Based on the calculated GEBV values, individuals with high genetic potential in terms of egg-laying performance and disease resistance are identified. The system automatically selects the top 10% of the GEBV ranking for egg production as candidate hens, while also taking into account the GEBV for disease resistance, ensuring that the selected breeders are not only high-yielding but also healthy.

[0128] Finally, genetic algorithms or rule-based breeding software are used to design breeding plans based on GEBV values ​​and pedigree information to ensure that the next generation of laying hens maintains or improves production performance while enhancing genetic diversity and avoiding inbreeding depression.

[0129] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic data collection and analysis system for laying hen breeding, characterized in that: The acquisition and analysis modules include: Information collection unit: real-time collection of laying hens’ physiological parameters, behavioral data, environmental parameters and health indicators; Disease monitoring unit: analysis of collected data to identify physiological abnormalities and behavioral changes before disease occurs; Antibody Level Monitoring Unit: Evaluate the immune response of laying hens to specific pathogens; Environmental adaptability assessment unit: assesses the adaptability of laying hens to extreme environments based on changes in environmental parameters and their physiological responses; Breeding decision support unit: Integrate all data to analyze the production performance, disease resistance and environmental adaptability of laying hens; recommend the best feeding plan, reduce drug use and optimize resource allocation; The disease monitoring unit includes an early warning unit, and the antibody level detection unit is provided with an immune response analysis unit.

2. The automatic data collection and analysis system for laying hen breeding according to claim 1, characterized in that: The information collection unit is equipped with image sensors, temperature and humidity sensors, light sensors, and ammonia concentration sensors to collect environmental variable information in the chicken house; The information collection unit captures the activity images of laying hens based on image sensors, uses image processing technology to identify pecking, resting, and abnormal behaviors, and analyzes and determines the health status of the chickens; Environmental data is collected regularly or continuously and sent to the disease monitoring unit via wired or wireless transmission. The collection frequency and accuracy are set according to breeding needs, once every minute or every hour.

3. The automatic data collection and analysis system for laying hen breeding according to claim 1, characterized in that: Disease monitoring units are based on flexible temperature patches, heart rate monitoring chest straps or micro-biosensors, which transmit physiological parameters of body temperature, heart rate and respiratory rate in real time; Through AI image recognition technology, the behavioral characteristics of laying hens are extracted from the video stream, including gait analysis, standing time ratio, and pecking behavior frequency; For physiological data, apply machine learning models to identify physiological indicators that deviate from the normal range; Train behavior classifiers based on deep learning models to distinguish normal and abnormal behavior patterns; Combining physiological and behavioral characteristics, feature learning and classification are performed through a multi-layer neural network: h l =σ(W l h l-1 +b l ) σ is the activation function, W l is the weight matrix, b l is the bias vector; Establish a comprehensive disease prediction model for laying hens based on deep models.

4. The automatic data collection and analysis system for laying hen breeding according to claim 1, characterized in that: Based on the prediction model of the antibody level monitoring unit, the early warning unit inputs the integrated feature vector into the deployed comprehensive disease prediction model, and the model outputs the disease risk score or disease probability of laying hens; According to the risk score output by the model, set the risk level standards as low risk, medium risk, and high risk; According to the actual needs of disease prevention and the distribution of model output, set the warning threshold and initiate the warning when the predicted risk reaches medium risk or above; When the disease risk score of laying hens exceeds the preset threshold, the early warning mechanism is automatically triggered, and an early warning message is sent to designated breeders through a pre-set notification channel. The message includes the laying hen number, current risk level, recommended inspection or intervention measures, and activates the antibody level monitoring unit and environmental adaptability assessment unit.

5. The automatic data collection and analysis system for laying hen breeding according to claim 1, characterized in that: The antibody level monitoring unit draws blood samples from the chickens; Using enzyme-linked immunosorbent assay technology, specific antibodies are coated on a solid phase carrier to react with the antigen in the sample, and then enzyme-labeled secondary antibodies are added to quantitatively analyze the antibody level through a color reaction; Mark samples with barcode or RFID technology, use scanners to automatically read information, use statistical software to organize data, calculate average antibody levels, compare with historical data, and test and evaluate antibody level trends and herd immunity status; Construct a dynamic model of antibody levels, taking into account the dynamic process of antibody production, attenuation, and disease transmission; Using historical antibody testing data, estimate model parameters, antibody production rate, decay rate, and transmission coefficient; Verify the model's predictive ability through simulation experiments or comparison with actual epidemic data; When training the model, the model is optimized based on: y i is the true label, p i is the predicted probability, p i is the sample size; Finally, a dynamic model of antibody levels was trained to predict the risk of disease transmission and determine the optimal vaccination time, dose, and recipients; The immune response analysis unit simulates different disease transmission scenarios in the model to predict the disease's spread rate in the chicken flock, the number of infected people, and possible epidemic trends; According to the prediction results of the model, formulate or adjust the vaccination plan, recommend the best time for vaccination, and vaccinate chickens with low antibody levels; When the model predicts that immunity levels are insufficient to protect against disease threats, the early warning system is automatically triggered.

6. The automatic data collection and analysis system for laying hen breeding according to claim 1, characterized in that: The environmental adaptability assessment unit analyzes the correlation between environmental changes and physiological indicators based on the chicken house environmental data and physiological data, combined with environmental monitoring data. In high temperature environments, chickens drink more water and eat less, and the chickens' physiological adjustment ability to environmental changes is assessed. Determine the key indicators for evaluating adaptability, including survival rate, egg production, weight growth rate, and disease incidence, and quantify them; Assign weights to each indicator based on its importance and design an adaptability scoring model; The total adaptability score is calculated for each chicken or each flock based on the performance of each indicator. The higher the score, the better the adaptability to the current environment.

7. The automatic data collection and analysis system for laying hen breeding according to claim 1, characterized in that: The breeding decision support unit collects genetic information of chickens, including breed, parental information, and genetic markers; Based on the environmental adaptability assessment unit, the adaptability score is associated with genetic information to identify genetic markers or gene loci related to environmental adaptability; Predicting an individual's genetic value based on consideration of inter-family genetic correlations and environmental effects to assess their genetic potential, based on: where GEBV is the expected breeding value of the genotype; is an estimate based on additive genetic effects; It is an estimate based on non-additive genetic effects, obtained by iteratively solving a system of mixed model equations; Based on the prediction results, identify genetic markers or gene loci that are significantly associated with environmental adaptability, as well as the genetic values ​​of individuals; Based on the results of genetic evaluation, individuals with strong adaptability are selected as breeders.

8. The automatic data collection and analysis system for laying hen breeding according to claim 1, characterized in that: Green breeding management module monitors feed consumption, water use, and optimizes feeding plans and water cycles based on breeding results; Analyze chicken manure production, guide organic fertilizer production and waste resource utilization; According to the environmental adaptability assessment results, the environmental parameters, temperature and ventilation in the chicken house are automatically adjusted to reduce energy consumption; Finally, a green breeding report is generated to evaluate the environmental impact of the breeding process.

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