Ewe oestrus identification system and method based on body temperature data

Abstract

The invention discloses an ewe oestrus identification system and method based on body temperature data, and belongs to the technical field of breeding. The ewe oestrus identification system comprises an ear tag identification module, an infrared imaging module, an image processing module, an intelligent analysis module and a user interface module; the ear tag identification module identifies ear tag information of the ewe and transmits the ear tag information to the intelligent analysis module; the infrared imaging module collects a thermal image of the ewe and transmits data to the image processing module; the image processing module performs preprocessing operation on the acquired thermal image, extracts required key features and transmits data to the intelligent analysis module; the intelligent analysis module analyzes the received data, captures the body surface temperature change trend and judges the estrus state of the ewe; the data storage and transmission module stores the received data and transmits the data to the user interface module; after receiving the data, the user interface module displays a processing result and a report; the method solves the problems of large workload, troublesome operation and low accuracy during artificial estrus identification of ewes.

Description

Technical Field

[0001] The present invention relates to the field of breeding technology, and in particular to a system and method for identifying ewe estrus based on body temperature data. Background Art

[0002] In large-scale ewe breeding systems, accurate monitoring of the estrus cycle is a core technical link in optimizing reproductive efficiency. The estrus detection methods currently commonly used in the industry mainly include: 1) behavioral observation method, which relies on experience to judge the behavioral manifestations of ewes such as mounting and vulvar swelling, which has the defects of strong subjectivity and high labor intensity; 2) estrus ram method, which observes the ewes' response to mounting by setting up a control group, but it is easy to cause group stress and increase the risk of disease transmission; 3) progesterone concentration detection method, which requires regular collection of blood or milk samples for laboratory analysis, and has significant shortcomings of high invasiveness and poor timeliness. The above traditional methods all have technical bottlenecks such as high degree of manual intervention, low detection sensitivity (<65%), and blind spots for nighttime monitoring, resulting in a missed detection rate of more than 30% in large-scale sheep flocks, which directly causes delays in breeding time and a decrease in the number of lambs born each year.

[0003] In recent years, with the development of intelligent sensing and data analysis technologies, infrared thermal imaging technology has gradually been applied to the field of animal body temperature monitoring. However, existing technologies mostly focus on overall body surface temperature detection and lack precise analysis of the ewe's ear, a key physiological characteristic area. In addition, most systems have not achieved closed-loop automation for data collection, processing, and decision-making, and still rely on manual intervention. Therefore, there is an urgent need for a method and system for identifying the estrus stage of ewes. By integrating data from multiple sensors and advanced deep learning technology, it can timely and accurately identify the estrus stage of ewes, providing a scientific basis and intelligent decision-making support. This method can not only improve breeding efficiency and economic benefits, but also help promote the development of the breeding industry towards digitalization and intelligence, thereby meeting the needs of modern breeding industry for efficient management and production. Summary of the Invention

[0004] The present invention is intended to provide a system and method for identifying ewe estrus based on body temperature data to solve the problems raised in the above background technology.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A ewe estrus identification system based on body temperature data, comprising an ear tag recognition module, an infrared imaging module, an image processing module, an intelligent analysis module and a user interface module;

[0007] The ear tag recognition module includes ear tag scanning, data association and real-time updating;

[0008] Ear tag scanning is to use an electronic ear tag reader / writer scanning device placed at the entrance of the sheep pen to read the ewe's ear tag information;

[0009] Data association is the process of transmitting ear tag information to the intelligent analysis module;

[0010] Real-time update is to dynamically maintain ear tag information to ensure efficient synchronization and consistency of multi-terminal data;

[0011] The infrared imaging module includes image acquisition and image output;

[0012] Image acquisition is to use an infrared thermal imager to take thermal images of the ewe, focusing on capturing the ewe's ears;

[0013] Image output is to output the formed thermal image to the image processing module for subsequent analysis;

[0014] The image processing module includes image preprocessing, feature extraction and data output;

[0015] Image preprocessing is to remove noise and enhance contrast of thermal images to improve clarity and make temperature differences more obvious;

[0016] Feature extraction is to identify and extract the ear temperature data from the processed thermal image and generate a temperature analysis graph;

[0017] Data output is to transmit relevant data after preprocessing and feature extraction to the intelligent analysis module;

[0018] The intelligent analysis module includes temperature trend analysis, estrus status judgment and result output;

[0019] Temperature trend analysis conducts time series analysis on the ewe's ear temperature data to capture the temperature variation pattern;

[0020] Estrus status is determined by temperature change trends combined with the typical characteristics of estrus, i.e., increased ear temperature, to determine whether the ewe is in estrus;

[0021] The result output is to use machine learning or deep learning algorithms to improve the accuracy of judgment and transmit the analysis results to the data storage and transmission module;

[0022] The data storage and transmission module includes data storage and data transmission;

[0023] Data storage involves categorizing and storing thermal images, temperature data, and analysis results on a local server, and using encryption technology to ensure data security.

[0024] Data transmission is the transmission of data to user terminals via wireless or wired networks;

[0025] The data storage and transmission module can also perform data management, provide data backup and recovery functions to prevent data loss; and support data export and sharing;

[0026] The user interface module includes data display and early warning functions;

[0027] Data display is to display the temperature distribution and change trend in the form of charts, heat maps, etc. on the platform, and generate estrus status reports with clear marking of estrus period, non-estrus period and other information;

[0028] The platform is mainly for mobile phones; through the mobile phone operation interface, users can manually adjust relevant parameters and provide historical data query and export functions;

[0029] The early warning function is to send out an early warning via SMS or email when abnormal temperature or estrus status is detected and analyzed, and the corresponding ear tag will flash red to warn.

[0030] A method for identifying ewe estrus based on body temperature data, comprising the following steps:

[0031] S1. Ear tag recognition module automatically identifies the ear tag information of ewes to ensure the unique identity of each ewe, and transmits the data to the intelligent analysis module for correlation with subsequent thermal images and analysis results;

[0032] S2. Infrared imaging module, using a high-precision infrared thermal imager to capture thermal images of ewes, accurately reflecting the temperature distribution of the body surface and ears of different ewes, and transmitting the data to the image processing module;

[0033] S3. Image processing module, which performs a series of preprocessing operations on the collected thermal images to improve image quality, further extract key features related to ewe estrus, and transmit the processed data to the intelligent analysis module;

[0034] S4. Intelligent analysis module uses advanced algorithm models to deeply analyze pre-processed images, keenly capture the trend of body surface temperature changes, and accurately determine the estrus status of ewes;

[0035] S5. Data storage and transmission module, used to safely and efficiently store all collected thermal images and analysis results, and transmit the data to the user interface module, so that relevant personnel can obtain the required data and information at any time;

[0036] S6. The user interface module provides users with an intuitive operation interface, receives data from the data storage and transmission module, and displays visual processing results and reports, clearly reflecting the estrus status of ewes and issuing early warnings when necessary to facilitate timely management.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention has the advantages of non-invasiveness, anti-pollution, scalability and compatibility in temperature measurement by using infrared thermal imagers. It can accurately, real-time and dynamically monitor individual information of ewes without the need for contact with the object, which greatly reduces the advantage of disease transmission. Secondly, the accurate data obtained by analyzing the thermal image can quickly identify the estrus of ewes, improve the accuracy of estrus identification, and improve breeding efficiency; the data display makes it convenient for the ranch to grasp the precise location and physical condition of each animal in real time, and perform estrus identification, disease monitoring and other treatments on the ewes, so that problems can be discovered in time, involved in advance, and each ewe can be efficiently managed, which greatly improves work efficiency and standardized animal breeding, saving a lot of breeding costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a module diagram of a system for identifying ewe estrus based on body temperature data;

[0040] Figure 2 The present invention is a flow chart of a method for identifying ewe estrus based on body temperature data. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0042] The specific implementation process is as follows:

[0043] like Figure 1 As shown, a system for identifying ewe estrus based on body temperature data includes an ear tag recognition module, an infrared imaging module, an image processing module, an intelligent analysis module, and a user interface module;

[0044] Ear tag recognition module: This includes ear tag scanning, data association, and real-time updates. Ear tag scanning uses an electronic ear tag reader / writer placed at the entrance to the sheep pen to read the ewe's ear tag information. Data association transmits the ear tag information to the intelligent analysis module, where it is combined with the ewe's thermal image, temperature data, and estrus status analysis results. Real-time updates dynamically maintain ear tag information to ensure efficient synchronization and consistency of data across multiple terminals.

[0045] Infrared imaging module: includes image acquisition and image output. Image acquisition involves the infrared thermal imager capturing thermal images of the ewe, focusing on the ewe's ears. Image output involves sending the generated thermal images to the image processing module for subsequent analysis.

[0046] Image processing module: includes image preprocessing, feature extraction, and data output. Image preprocessing removes noise from thermal images and enhances contrast, improving clarity and making temperature differences more apparent. Feature extraction identifies and extracts ear temperature data from processed thermal images and generates a temperature analysis graph. Data output transmits the relevant data after preprocessing and feature extraction to the intelligent analysis module.

[0047] Intelligent Analysis Module: This module includes temperature trend analysis, estrus status determination, and result output. Temperature trend analysis analyzes the ewe's ear temperature data through time series analysis to capture temperature variation patterns. Estrus status determination determines whether the ewe is in estrus based on temperature trends and typical characteristics of the estrus period (i.e., elevated ear temperature). Output uses machine learning or deep learning algorithms to improve judgment accuracy and transmits the analysis results to the data storage and transmission module.

[0048] Data Storage and Transmission Module: This module includes both data storage and data transmission. Data storage involves categorizing and storing thermal images, temperature data, and analysis results on a local server, using encryption to ensure data security. Data transmission involves transmitting data to user terminals via wireless or wired networks. This module also manages data, provides backup and recovery capabilities to prevent data loss, and supports data export and sharing for further research.

[0049] User interface module: includes data display and early warning functions. Data display is to display the temperature distribution and change trends in the form of charts, heat maps, etc. on the platform, and generate an estrus status report with clear markings of information such as estrus period and non-estrus period. The platform is mainly a mobile terminal, which is convenient for staff to view at any time; and it is designed with a simple and intuitive operation interface. This interface supports users to manually adjust relevant parameters, such as setting temperature thresholds, selecting analysis areas, etc., and provides historical data query and export functions. The early warning function is that when abnormal temperature or estrus status is detected and analyzed, an early warning is issued via SMS or email, and the corresponding ear tag flashes red light as a warning.

[0050] like Figure 2 As shown, a method for identifying ewe estrus based on body temperature data comprises the following steps:

[0051] S1. Ear tag recognition module, which automatically identifies the ear tag information of ewes, ensures the unique identity of each ewe, and associates it with the subsequently collected thermal images and analysis results;

[0052] S2. Infrared imaging module, using a high-precision infrared thermal imager to capture thermal images of ewes, accurately reflecting the temperature distribution of the body surface and ears of different ewes, and transmitting the data to the image processing module;

[0053] S3. Image processing module, which performs a series of preprocessing operations on the collected thermal images to improve image quality, further extract key features related to ewe estrus, and transmit the processed data to the intelligent analysis module;

[0054] S4. Intelligent analysis module uses advanced algorithm models to deeply analyze pre-processed images, keenly capture the trend of body surface temperature changes, and accurately determine the estrus status of ewes;

[0055] S5. Data storage and transmission module, used to safely and efficiently store all collected thermal images and analysis results, and transmit the data to the user interface module, so that relevant personnel can obtain the required data and information at any time;

[0056] S6. The user interface module provides users with an intuitive operation interface, receives data from the data storage and transmission module, and displays visual processing results and reports, clearly reflecting the estrus status of ewes and issuing early warnings when necessary to facilitate timely management.

[0057] The above is only an embodiment of the present invention, and the common knowledge such as the specific technical solutions and / or characteristics in the solution are not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the description can be used to interpret the content of the claims.

Claims

1. A system for identifying ewe estrus based on body temperature data, characterized by: The system includes an ear tag recognition module, an infrared imaging module, an image processing module, an intelligent analysis module and a user interface module; The ear tag recognition module includes ear tag scanning, data association and real-time updating; Ear tag scanning is to use an electronic ear tag reader / writer scanning device placed at the entrance of the sheep pen to read the ewe's ear tag information; Data association is the process of transmitting ear tag information to the intelligent analysis module; Real-time update is to dynamically maintain ear tag information to ensure efficient synchronization and consistency of multi-terminal data; The infrared imaging module includes image acquisition and image output; Image acquisition is to use an infrared thermal imager to take thermal images of the ewe, focusing on capturing the ewe's ears; Image output is to output the formed thermal image to the image processing module for subsequent analysis; The image processing module includes image preprocessing, feature extraction and data output; Image preprocessing is to remove noise and enhance contrast of thermal images to improve clarity and make temperature differences more obvious; Feature extraction is to identify and extract the ear temperature data from the processed thermal image and generate a temperature analysis graph; Data output is to transmit relevant data after preprocessing and feature extraction to the intelligent analysis module; The intelligent analysis module includes temperature trend analysis, estrus status judgment and result output; Temperature trend analysis conducts time series analysis on the ewe's ear temperature data to capture the temperature variation pattern; Estrus status is determined by temperature change trends combined with the typical characteristics of estrus, i.e., increased ear temperature, to determine whether the ewe is in estrus; The result output is to use machine learning or deep learning algorithms to improve the accuracy of judgment and transmit the analysis results to the data storage and transmission module; The data storage and transmission module includes data storage and data transmission; Data storage involves categorizing and storing thermal images, temperature data, and analysis results on a local server, and using encryption technology to ensure data security. Data transmission is the transmission of data to user terminals via wireless or wired networks; The data storage and transmission module can also perform data management, provide data backup and recovery functions to prevent data loss; and support data export and sharing; The user interface module includes data display and early warning functions; Data display is to display the temperature distribution and change trend in the form of charts, heat maps, etc. on the platform, and generate estrus status reports with clear marking of estrus period, non-estrus period and other information; The platform is mainly for mobile phones; through the mobile phone operation interface, users can manually adjust relevant parameters and provide historical data query and export functions; The early warning function is to send out an early warning via SMS or email when abnormal temperature or estrus status is detected and analyzed, and the corresponding ear tag will flash red to warn.

2. A method for identifying ewe estrus based on body temperature data, characterized in that: The specific steps include: S1. Ear tag recognition module automatically identifies the ear tag information of ewes to ensure the unique identity of each ewe, and transmits the data to the intelligent analysis module for correlation with subsequent thermal images and analysis results; S2. Infrared imaging module, using a high-precision infrared thermal imager to capture thermal images of ewes, accurately reflecting the temperature distribution of the body surface and ears of different ewes, and transmitting the data to the image processing module; S3. Image processing module, which performs a series of preprocessing operations on the collected thermal images to improve image quality, further extract key features related to ewe estrus, and transmit the processed data to the intelligent analysis module; S4. Intelligent analysis module uses advanced algorithm models to deeply analyze pre-processed images, keenly capture the trend of body surface temperature changes, and accurately determine the estrus status of ewes; S5. Data storage and transmission module, used to safely and efficiently store all collected thermal images and analysis results, and transmit the data to the user interface module, so that relevant personnel can obtain the required data and information at any time; S6. The user interface module provides users with an intuitive operation interface, receives data from the data storage and transmission module, and displays visual processing results and reports, clearly reflecting the estrus status of ewes and issuing early warnings when necessary to facilitate timely management.

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