ADVANCED UDDER HEALTH MONITORING AND INTERVENTION SYSTEM AND METHOD THEREOF

Abstract

Disclosed herein is an advanced udder Health monitoring and intervention system and method thereof, designed to provide comprehensive and real-time monitoring of cow udder health. The system integrates a plurality of temperature sensors (102), swelling sensors (104), milk conductivity sensor (106), and activity sensors (108) to collect data related to udder temperature, size, milk quality, and cow behavior. A microcontroller (110) processes this data using machine learning algorithms, while a central processing unit (112) aggregates the information to predict potential udder health issues. An automated treatment recommendation module (114) provides real-time treatment suggestions based on sensor data analysis. The system further includes a communication network (116) for wireless data transmission and a user interface (118) for delivering actionable insights and alerts to farmers and veterinarians, ensuring proactive intervention. The system is powered by a power supply unit (120) for continuous operation.

Specification

Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to agricultural technology, more specifically, relates to advanced udder health monitoring and intervention system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] The present system continuously improves early detection of udder health issues, allowing farmers to take timely action, which leads to better overall cow health and higher milk production efficiency.
[0003] The integration of multiple sensors in this system provides comprehensive and accurate real-time data, reducing the reliance on traditional, labour-intensive methods of health monitoring. This enhances both the reliability and convenience of farm management.
[0004] The use of predictive analytics and machine learning significantly optimizes farm operations by forecasting potential health problems before they become severe, helping reduce treatment costs and improving the overall productivity of the farm.
[0005] Existing systems for monitoring cow health often rely on a single sensor or parameter, which limits their ability to provide comprehensive data about the cow's udder health, leading to delayed detection and intervention in many cases.
[0006] Many current inventions involve reactive health monitoring approaches, which focus on detecting problems after they occur rather than predicting potential issues. This results in increased veterinary costs and greater risk of decreased milk production.
[0007] Many current inventions involve reactive health monitoring approaches, which focus on detecting problems after they occur rather than predicting potential issues. This results in increased veterinary costs and greater risk of decreased milk production.
[0008] Thus, in light of the above-stated discussion, there exists a need for an advanced udder health monitoring and intervention system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an advanced udder health monitoring and intervention system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to enhance the overall efficiency of dairy farm management by automating the monitoring of udder health.
[0012] Another objective of the present disclosure is to integrate multiple sensors that provide real-time data related to temperature, swelling, milk conductivity, and cow activity levels.
[0013] Another objective of the present disclosure is to improve the accuracy of early detection of udder health issues, enabling timely intervention before the onset of serious health conditions.
[0014] Another objective of the present disclosure is to reduce the manual labour and time spent by farmers in regularly inspecting cow health by providing automated and continuous monitoring systems.
[0015] Another objective of the present disclosure is to apply predictive analytics and machine learning algorithms to forecast potential health problems based on historical and real-time sensor data.
[0016] Yet another objective of the present disclosure is to provide automated treatment recommendations based on the analysis of data collected from the sensors, guiding farmers on timely and appropriate interventions.
[0017] Yet another objective of the present disclosure is to offer a user-friendly interface for farmers and veterinarians, allowing them to access real-time health insights and recommendations with ease.
[0018] Yet another objective of the present disclosure is to facilitate remote monitoring and control of the system through a communication network, enabling farmers to access data and alerts from anywhere.
[0019] Yet another objective of the present disclosure is to enhance long-term farm productivity by minimizing veterinary costs and improving overall cow health through data-driven management.
[0020] Yet another objective of the present disclosure is to promote energy-efficient farming practices by integrating low-power sensors and sustainable technology for continuous health monitoring.
[0021] In light of the above, in one aspect of the present disclosure, an advanced udder health monitoring and intervention system is disclosed herein. The system comprises a plurality of temperature sensors strategically placed on a cow's udder to measure real-time temperature. The system includes a swelling sensor attached to the cow's udder to detect changes in udder size. The system also includes a milk conductivity sensor integrated into the milking apparatus to measure milk conductivity. The system also includes a activity sensors attached to the cow to monitor movement, rumination patterns, and lying behaviour. The system also includes a microcontroller operatively connected to said the temperature sensors, the swelling sensors, and milk conductivity sensor, and activity sensors, said the microcontroller configured to process real-time data from said sensors and perform predictive analytics using embedded machine learning algorithms. The system also includes a central processing unit operatively connected to said microcontroller, said central processing unit configured to aggregate, analyse, and process sensor data to generate predictions of udder health conditions based on historical data and real-time measurement. The system also includes an automated treatment recommendation module operatively connected to said central processing unit, said automated treatment recommendation module configured to generate real-time treatment recommendations based on data analysis, and transmit the recommendations to farm personnel. The system also includes a communication network configured to enable wireless transmission of sensor data between the microcontroller, central processing unit, and an external server for additional data processing or remote monitoring. The system also includes a user interface operatively connected to the the central processing unit, said user interface configured to provide real-time alerts, health status, and actionable recommendations for farmers and veterinarians, allowing for efficient monitoring of cow health and proactive interventions. The system also includes a power supply unit operatively connected to said sensors, microcontroller, and central processing unit, said power supply unit providing continuous energy to ensure uninterrupted operation of the system.
[0022] In one embodiment, the temperature sensors are configured to continuously monitor temperature variations and generate alerts when abnormal temperature fluctuations indicative of infection or inflammation are detected.
[0023] In one embodiment, the swelling sensors are flex sensors that provide real-time measurements of udder size changes, enabling early detection of swelling associated with mastitis or other udder health issues.
[0024] In one embodiment, the activity sensors monitor cow behaviour patterns, including movement and lying times, and are configured to generate predictive alerts when significant deviations from normal activity levels, indicative of health issues, are detected.
[0025] In one embodiment, the communication network is configured to transmit real-time sensor data and health alerts to an external server via internet of things (IOT) protocols, enabling remote monitoring and additional data processing for enhanced analysis and intervention.
[0026] In light of the above, in one aspect of the present disclosure, a method for an advanced udder health monitoring and intervention is disclosed herein. The method comprises receiving real-time temperature data from a plurality of temperature sensors strategically placed on a cow's udder to monitor udder temperature, receiving udder swelling data from swelling sensors attached to the cow's udder to detect changes in udder size, receiving milk conductivity data from a milk conductivity sensor integrated into the milking apparatus to monitor milk properties, and receiving activity data from activity sensors attached to the cow to track movement, rumination patterns, and lying behaviour. The method includes processing the real-time data received from said temperature sensors, swelling sensors, milk conductivity sensor, and activity sensors using a microcontroller operatively connected to the sensors, wherein the microcontroller applies machine learning algorithms to predict potential udder health issues based on patterns in the sensor data. The method also includes aggregating and analysing the processed sensor data using a central processing unit operatively connected to the microcontroller, wherein said central processing unit compares real-time data with historical health records to predict udder health conditions such as mastitis or infections. The method also includes generating automated treatment recommendations using an automated treatment recommendation module operatively connected to said central processing unit, said module configured to transmit real-time treatment recommendations to farm personnel based on the analysis of sensor data and historical health records. The method also includes transmitting real-time alerts, health status, and treatment transmitting said sensor data and health predictions through a communication network operatively connected to said microcontroller and central processing unit to an external server for further analysis or remote monitoring by veterinarians or farm management. The method also includes displaying real-time health alerts, status updates, and treatment recommendations to users through a user interface operatively connected to the central processing unit, allowing farm personnel to take proactive actions based on the health status of the cow.
[0027] In one embodiment, the machine learning algorithms applied in the microcontroller are trained on a historical dataset of udder health conditions, enabling the system to improve predictive accuracy by continuously learning from new data collected through said temperature sensors, swelling sensors, milk conductivity sensor, and activity sensors.
[0028] In one embodiment, the communication network facilitates real-time transmission of alerts and treatment recommendations to a mobile device, allowing remote monitoring and management of cow health by farm personnel and veterinarians.
[0029] In one embodiment, the automated treatment recommendation module adjusts the treatment recommendations based on customizable thresholds for each cow, allowing farmers to set specific health benchmarks tailored to individual animal needs.
[0030] In one embodiment, the user interface dynamically updates the health status of the cow and displays historical trends, providing a visual representation of changes in udder health over time, enabling more informed decision-making for proactive interventions.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of an advanced udder health monitoring and intervention system, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flow chart of an advanced udder health monitoring and intervention system, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a method block diagram of an advanced udder health monitoring and intervention in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 4 illustrates a perspective view of step by step procedure to find udder health, in accordance with an exemplary embodiment of the present disclosure.
[0040] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0041] The advanced udder health monitoring and intervention system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0042] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0043] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0044] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0045] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0046] The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
[0047] Referring now to FIG. 1 to FIG. 4 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an advanced udder health monitoring and intervention system, in accordance with an exemplary embodiment of the present disclosure.
[0048] The system 100 may include a plurality of temperature sensors 102 strategically placed on a cow's udder to measure real-time temperature, a swelling sensor 104 attached to the cow's udder to detect changes in udder size, a milk conductivity sensor 106 integrated into the milking apparatus to measure milk conductivity, a activity sensors 108 attached to the cow to monitor movement, rumination patterns, and lying behaviour, a microcontroller 110 operatively connected to said the plurality of temperature sensors 102, the swelling sensor 104s, and milk conductivity sensor 106, and activity sensors 108, said the microcontroller 110 configured to process real-time data from said sensors and perform predictive analytics using embedded machine learning algorithms, a central processing unit 112 operatively connected to said microcontroller 110, said central processing unit 112 configured to aggregate, analyse, and process sensor data to generate predictions of udder health conditions based on historical data and real-time measurements, an automated treatment recommendation module 114 operatively connected to said central processing unit 112, said automated treatment recommendation module 114 configured to generate real-time treatment recommendations based on data analysis, and transmit the recommendations to farm personnel, a communication network 116 configured to enable wireless transmission of sensor data between said microcontroller 110, central processing unit 112, and an external server for additional data processing or remote monitoring, a user interface 118 operatively connected to said central processing unit 112, said user interface 118 configured to provide real-time alerts, health status, and actionable recommendations for farmers and veterinarians, allowing for efficient monitoring of cow health and proactive interventions, a power supply unit 120 operatively connected to said sensors, microcontroller 110, and central processing unit 112, said power supply unit 120 providing continuous energy to ensure uninterrupted operation of the system.
[0049] The plurality of temperature sensors 102 are configured to continuously monitor temperature variations and generate alerts when abnormal temperature fluctuations indicative of infection or inflammation are detected.
[0050] The swelling sensor 104 are flex sensors that provide real-time measurements of udder size changes, enabling early detection of swelling associated with mastitis or other udder health issues.
[0051] The activity sensors 108 monitor cow behaviour patterns, including movement and lying times, and are configured to generate predictive alerts when significant deviations from normal activity levels, indicative of health issues, are detected.
[0052] The communication network 116 is configured to transmit real-time sensor data and health alerts to an external server via internet of things (IOT) protocols, enabling remote monitoring and additional data processing for enhanced analysis and intervention.
[0053] The method 100 may include receiving real-time temperature data from a plurality of temperature sensors 102 strategically placed on a cow's udder to monitor udder temperature, receiving udder swelling data from swelling sensor 104 attached to the cow's udder to detect changes in udder size, receiving milk conductivity data from a milk conductivity sensor 106 integrated into the milking apparatus to monitor milk properties, and receiving activity data from activity sensors 108 attached to the cow to track movement, rumination patterns, and lying behaviour, processing the real-time data received from said the plurality of temperature sensors 102, swelling sensor 104, milk conductivity sensor 106, and activity sensors 108 using a microcontroller 110 operatively connected to the sensors, wherein the microcontroller 110 applies machine learning algorithms to predict potential udder health issues based on patterns in the sensor data, aggregating and analysing the processed sensor data using a central processing unit 112 operatively connected to said microcontroller 110, wherein said central processing unit 112 compares real-time data with historical health records to predict udder health conditions such as mastitis or infections, generating automated treatment recommendations using an automated treatment recommendation module 114 operatively connected to said central processing unit 112, said module configured to transmit real-time treatment recommendations to farm personnel based on the analysis of sensor data and historical health records, transmitting real-time alerts, health status, and treatment transmitting said sensor data and health predictions through a communication network 116 operatively connected to said microcontroller 110 and central processing unit 112 to an external server for further analysis or remote monitoring by veterinarians or farm management, displaying real-time health alerts, status updates, and treatment recommendations to users through a user interface 118 operatively connected to said central processing unit 112, allowing farm personnel to take proactive actions based on the health status of the cow.
[0054] The machine learning algorithms applied in the microcontroller 110 are trained on a historical dataset of udder health conditions, enabling the system to improve predictive accuracy by continuously learning from new data collected through said the plurality of temperature sensors 102, swelling sensor 104s, milk conductivity sensor 106, and activity sensors 108.
[0055] The communication network 116 facilitates real-time transmission of alerts and treatment recommendations to a mobile device, allowing remote monitoring and management of cow health by farm personnel and veterinarians.
[0056] The automated treatment recommendation module 114 adjusts the treatment recommendations based on customizable thresholds for each cow, allowing farmers to set specific health benchmarks tailored to individual animal needs.
[0057] The user interface 118 dynamically updates the health status of the cow and displays historical trends, providing a visual representation of changes in udder health over time, enabling more informed decision-making for proactive interventions.
[0058] The plurality of temperature sensors 102 work in unison, transmitting real-time temperature data to the microcontroller 110, which processes the data for further analysis. The microcontroller 110 applies machine learning algorithms to identify abnormal temperature variations, allowing the system to predict potential health risks before they become clinically visible. By processing data from multiple sensors simultaneously, the system 100 ensures that any localized increases in temperature, indicative of infection or inflammation, are detected promptly.
[0059] The temperature sensor 102 is designed to be non-invasive and comfortable for the cow, ensuring that the monitoring process does not interfere with the cow's normal behaviours. The data gathered from the temperature sensors 102 is continuously transmitted to the central processing unit 112, where it is aggregated with data from other sensors, such as the swelling sensors 104 and the milk conductivity sensor 106, to form a holistic picture of the cow's udder health.
[0060] The real-time data from the plurality of temperature sensors 102 plays a critical role in generating predictive analytics, which help in forecasting health conditions based on both historical data and current readings. This data is used not only to detect current health conditions but also to track long-term trends in the cow's udder health. The integration of these sensors into the system 100 enables proactive interventions by providing early warnings and reducing the need for manual health checks.
[0061] Through the automated treatment recommendation module 114, the processed data from the temperature sensors 102 contributes to the generation of real-time treatment suggestions for farm personnel. The system 100 operates seamlessly, ensuring the continuous collection of temperature data without human intervention, and plays a pivotal role in the overall health management of the cows.
[0062] The swelling sensors 104 attached to the cow's udder continuously monitor changes in the udder size, providing crucial data for early detection of health issues like mastitis. The swelling sensors 104 work by measuring subtle variations in the udder's circumference or volume, which can indicate inflammation or fluid accumulation, both of which are key indicators of infection. By constantly gathering data, these sensors help in identifying changes before they become visibly noticeable, allowing for timely intervention.
[0063] The swelling sensor 102 is strategically placed around the cow's udder to ensure comprehensive coverage. The real-time data from the swelling sensors 104 is transmitted to the microcontroller 110, which processes and analyses the information alongside temperature data from the plurality of temperature sensors 102. This integration of multiple data sources enables the system 100 to generate a more accurate assessment of the cow's udder health. The swelling sensors 104 are essential for capturing localized swelling that may occur due to infection or injury, ensuring that even minor changes are recorded and acted upon.
[0064] The processed data from the swelling sensors 104 is sent to the central processing unit 112, where it is further analysed in conjunction with historical data and inputs from other sensors, such as the milk conductivity sensor 106. This multi-dimensional analysis allows the system 100 to predict potential health problems more accurately.
[0065] In addition to early detection, the swelling sensors 104 also contribute to long-term health monitoring, helping to track the effectiveness of treatments and interventions. By continuously providing real-time data on udder swelling, the system 100 ensures that farm personnel and veterinarians receive actionable insights through the user interface 118, allowing for more informed and timely decisions regarding the health of the cows.
[0066] The milk conductivity sensor 106 integrated into the milking apparatus plays a pivotal role in monitoring the cow's milk for signs of udder health issues. The milk conductivity sensor 106 measures the electrical conductivity of the milk, which changes in response to infections like mastitis. An increase in milk conductivity often signals inflammation in the udder, as it indicates a higher concentration of salts and ions due to infection.
[0067] This sensor 106 continuously collects data during each milking session, ensuring that every change in milk composition is tracked and analysed. The milk conductivity sensor 106 works in tandem with other sensors, such as the plurality of temperature sensors 102 and swelling sensors 104, to provide a comprehensive understanding of the cow's udder health. The data from the milk conductivity sensor 106 is transmitted to the microcontroller 110, where it is processed in real-time and combined with data from the other sensors to enhance the accuracy of health predictions.
[0068] The real-time monitoring enabled by the milk conductivity sensor 106 allows for immediate detection of abnormal conductivity levels, prompting the system 100 to generate alerts and treatment recommendations. This data is then analysed further by the central processing unit 112, which uses machine learning algorithms to predict potential udder infections or other health issues.
[0069] The milk conductivity sensor 106 significantly enhances the system 100 ability to detect udder health problems early, preventing the escalation of minor issues into severe infections. This proactive approach, combined with data from other sensors, ensures that farm personnel receive real-time alerts and actionable insights through the user interface 118, helping to maintain cow health and milk quality while reducing veterinary costs.
[0070] The activity sensors 108 are essential components for monitoring the cow's overall behaviours and movement patterns. The activity sensors 108 are attached to the cow, continuously gathering data on various parameters such as movement, rumination patterns, and lying behaviours. This data is critical for assessing the cow's well-being, as changes in activity levels often serve as early indicators of health problems, including udder infections and overall discomfort.
[0071] Each activity sensor 108 uses accelerometers and gyroscopes to detect specific types of movements, helping to analysed the cow's daily activity and rest cycles. For instance, the amount of time a cow spends lying down or chewing cud can indicate whether the cow is stressed or in pain, which may correlate with udder health issues. The activity sensor 108 collects and transmits this behavioural data in real-time to the microcontroller 110 for immediate processing.
[0072] Once processed, the data from the activity sensors 108 is sent to the central processing unit 112, where it is analysed alongside information from other sensors like the plurality of temperature sensors 102, swelling sensors 104, and milk conductivity sensor 106. This integrated approach enables the system 100 to provide a more comprehensive health assessment by correlating behavioural changes with physical health data.
[0073] The activity sensor 108 plays a crucial role in detecting subtle shifts in the cow's daily patterns, offering insights that are not readily apparent through traditional observation. When irregular activity is detected, such as reduced movement or excessive lying down, the system 100 generates alerts and recommendations through the user interface 118, ensuring prompt action from farm personnel. The activity sensor 108, in collaboration with the other components, contributes to a proactive and efficient udder health monitoring system
[0074] The microcontroller 110 serves as the central processing hub for the entire udder health monitoring system 100, playing a crucial role in real-time data collection, processing, and analysis. The microcontroller 110 is responsible for integrating and managing the data received from the plurality of temperature sensors 102, swelling sensors 104, milk conductivity sensor 106, and activity sensors 108. This processing occurs continuously to ensure timely monitoring and accurate assessment of the cow's udder health.
[0075] The microcontroller 110 is programmed with embedded machine learning algorithms that allow it to analyses the incoming data streams from the sensors. By applying predictive analytics, the microcontroller 110 identifies patterns and trends that indicate potential health risks, such as mastitis or other udder-related infections. This capability enables the system 100 to provide early warnings before these issues become severe, ensuring that preventive measures can be taken promptly.
[0076] Once the microcontroller 110 processes the sensor data, it forwards the information to the central processing unit 112 for more comprehensive analysis and decision-making. The microcontroller 110 plays a vital role in streamlining this data flow, ensuring that the system 100 operates efficiently by transmitting the processed data quickly and accurately. The microcontroller 110 also manages the communication between the sensors and the other system components, allowing seamless integration within the system 100.
[0077] In addition to processing real-time data, the microcontroller 110 is responsible for executing commands sent from the central processing unit 112, such as activating alerts through the user interface 118 or adjusting system parameters based on the data analysis. Through its direct control over the sensor network, the microcontroller 110 ensures that the system 100 functions effectively, providing accurate health assessments and enabling proactive interventions for better udder health management.
[0078] The central processing unit 112 serves as the core of the data analysis and decision-making processes within the udder health monitoring system 100. It is responsible for aggregating, analysing, and processing the data transmitted from the microcontroller 110, which collects real-time inputs from the plurality of temperature sensors 102, swelling sensors 104, milk conductivity sensor 106, and activity sensors 108. The central processing unit 112 is designed to handle large volumes of data, enabling it to manage the continuous stream of information coming from multiple sources within the system 100.
[0079] The central processing unit 112 performs advanced data analysis by applying machine learning algorithms and predictive analytics to the aggregated data. It evaluates the patterns and correlations between the various health parameters, such as temperature fluctuations, udder swelling, changes in milk conductivity, and cow activity levels. This analysis enables the central processing unit 112 to predict potential udder health issues before they become critical, providing actionable insights that help farmers take preventive measures.
[0080] In addition to data analysis, the central processing unit 112 is responsible for communicating with the automated treatment recommendation module 114. Once the central processing unit 112 identifies potential health risks or abnormal patterns, it generates precise predictions and sends this data to the automated treatment recommendation module 114. This module, in turn, uses the information to formulate treatment suggestions, which are then transmitted to farm personnel via the user interface 118.
[0081] The central processing unit 112 also plays a critical role in maintaining system efficiency by coordinating the transmission of data through the communication network 116. It ensures that data is securely transmitted between the sensors, the microcontroller 110, and any external servers for remote monitoring or additional processing. The central processing unit 112 optimizes the overall functionality of the system 100, enhancing the accuracy and timeliness of health predictions and interventions.
[0082] The automated treatment recommendation module 114 plays a critical role in guiding farm personnel with precise, data-driven interventions based on real-time health assessments generated by the central processing unit 112. This module 114 is operatively connected to the central processing unit 112, which provides detailed health analysis, identifying potential risks such as mastitis or other udder health issues. The automated treatment recommendation module 114 processes this information and determines the most appropriate course of action to ensure timely and effective intervention.
[0083] The automated treatment recommendation module 114 functions by using embedded algorithms designed to analysed health data patterns. It takes into account factors such as temperature fluctuations recorded by the plurality of temperature sensors 102, udder swelling detected by the swelling sensors 104, and milk conductivity variations measured by the milk conductivity sensor 106. Additionally, the module considers behavioural data from the activity sensors 108 to form a comprehensive understanding of the cow's health. These multiple data points allow the automated treatment recommendation module 114 to recommend specific treatments, whether they involve medical intervention, isolation of affected cows, or adjustment of feeding and care routines.
[0084] One of the significant advantages of the automated treatment recommendation module 114 is its ability to reduce the need for manual decision-making by providing scientifically validated treatment suggestions. It continuously updates recommendations based on real-time data and historical health records, thus ensuring that farm personnel receive up-to-date guidance. The module 114 also transmits these recommendations to the user interface 118, enabling farm staff and veterinarians to receive clear, actionable insights directly, allowing them to respond swiftly to emerging health issues.
[0085] The integration of the automated treatment recommendation module 114 within the system 100 ensures a proactive approach to udder health management, significantly enhancing the effectiveness of the overall monitoring system.
[0086] The communication network 116 serves as the backbone for data transmission within the system 100, ensuring seamless connectivity between the various components. This communication network 116 is operatively connected to the microcontroller 110, the central processing unit 112, and the user interface 118, facilitating the wireless transmission of sensor data, predictive health insights, and treatment recommendations. This real-time data flow enables farm personnel to monitor udder health continuously, ensuring timely interventions and maintaining the overall health of the herd.
[0087] The communication network 116 operates using internet of things (I0T) protocols, allowing it to connect with external servers for additional data processing and remote monitoring. This connection enables data to be stored securely in cloud-based systems or processed by external health monitoring platforms, providing an extra layer of analysis. The communication network 116 also supports bidirectional data transfer, allowing farm staff to remotely input commands, adjust system settings, or respond to alerts generated by the automated treatment recommendation module 114.
[0088] The communication network 116 plays a crucial role in ensuring uninterrupted operation, even when personnel are offsite. By transmitting real-time data wirelessly between the microcontroller 110, central processing unit 112, and the user interface 118, it allows farm staff to access critical health data and treatment recommendations from any location. This enhances flexibility in farm management and provides peace of mind to farm operators, knowing they are continuously informed about the health of their herd.
[0089] Furthermore, the communication network 116 ensures that all data points, from sensor readings to health analytics, are synchronized across the system 100, preventing data loss or delays. This reliable network infrastructure is essential for the effective functioning of the system 100, empowering farmers and veterinarians with accurate and timely data to make informed decisions about udder health management.
[0090] The user interface 118 serves as a critical component of the system 100, providing real-time access to the data generated and analysed by the sensors and the central processing unit 112. This user interface 118 is operatively connected to the central processing unit 112, ensuring that all health data, predictive analytics, and treatment recommendations are displayed clearly and in real-time for farm personnel. This interface ensures seamless interaction between the user and the system, facilitating efficient monitoring of the health conditions of the cows.
[0091] Through the user interface 118, farmers and veterinarians can access essential health metrics such as temperature readings from the temperature sensors 102, swelling measurements from the swelling sensors 104, and milk conductivity data from the milk conductivity sensor 106. The user interface 118 continuously updates these metrics, allowing farm personnel to track changes in cow health and act on them promptly. The system 100 ensures that health insights and predictions derived from the microcontroller 110 are clearly presented, enabling farm staff to respond to potential udder health issues effectively.
[0092] In addition to real-time monitoring, the user interface 118 provides actionable recommendations generated by the automated treatment recommendation module 114. These recommendations are based on the predictive analytics performed by the central processing unit 112, ensuring that all treatment suggestions are data-driven and tailored to the specific health conditions detected. Farm staff receives alerts and notifications through the user interface 118, allowing them to intervene proactively, whether remotely or on-site, depending on the circumstances.
[0093] The user interface 118 is also designed to be intuitive and easy to use, minimizing training requirements and ensuring that farmers and veterinarians can engage with the system 100 effectively. By offering a user-friendly interface, the system empowers users to access critical information monitor the herd's health in real-time, and make informed decisions about treatment, ultimately improving overall herd management.
[0094] The power supply unit 120 serves as a fundamental component of the system 100, providing continuous energy to ensure uninterrupted operation of all connected devices. This power supply unit 120 is operatively connected to the sensors, including the temperature sensors 102, swelling sensors 104, milk conductivity sensor 106, and activity sensors 108. The power supply unit 120 guarantees that these sensors operate efficiently, continuously gathering real-time data on cow health without any interruptions due to power shortages.
[0095] Additionally, the power supply unit 120 is connected to the microcontroller 110 and the central processing unit 112, which are responsible for processing and analyzing the data from the sensors. The microcontrollers 110 uses this data to run predictive analytics and machine learning algorithms, while the central processing unit 112 aggregates the data and generates actionable insights. The constant power provided by the power supply unit 120 ensures that these crucial operations run smoothly and without delay, allowing for real-time analysis and decision-making.
[0096] The power supply unit 120 is also configured to power the automated treatment recommendation module 114, which is responsible for generating real-time treatment suggestions based on the health data. This module 114 plays a vital role in helping farmers and veterinarians address potential health issues proactively, and the power supply unit 120 ensures that the module remains operational at all times, allowing it to deliver timely recommendations.
[0097] In conjunction with the communication network 116, the power supply unit 120 supports the wireless transmission of sensor data to external servers or cloud-based systems for further processing. The communication network 116 relies on a steady power source to transmit data seamlessly between different components and the external systems, and the power supply unit 120 facilitates this by maintaining the necessary energy flow. This consistent power availability enhances the overall reliability and efficiency of the system 100, supporting its continuous operation.
[0098] FIG. 2 illustrates a flow chart of an advanced udder health monitoring and intervention system, in accordance with an exemplary embodiment of the present disclosure.
[0099] At 202, temperature sensors, swelling sensors, milk conductivity sensor, and activity sensors continuously collect real-time data from the cow's udder, milk, and behaviour.
[0100] At 204, the collected data is transmitted to the microcontroller, which receives input from all the sensors.
[0101] At 206, the microcontroller processes the real-time data and applies machine learning algorithms for predictive analytics, identifying patterns and potential health issues.
[0102] At 208, the processed data is sent to the central processing unit, which aggregates sensor data and analyzes it further based on real-time and historical records.
[0103] At 210, the central processing unit generates predictions regarding the cow's udder health condition, including potential risks such as mastitis or inflammation.
[0104] At 212, the central processing unit sends the analyzed data to the automated treatment recommendation module, which formulates real-time treatment suggestions based on the predictions.
[0105] At 214, the automated treatment recommendations and health status are transmitted to the user interface. Farmers and veterinarians receive real-time alerts and actionable recommendations.
[0106] At 216, the data and recommendations are transmitted wirelessly through the communication network, enabling remote monitoring or additional processing through an external server.
[0107] At 218, the power supply unit ensures continuous operation of the entire system, providing energy to all sensors, the microcontroller, and the central processing unit.
[0108] FIG. 3 illustrates a method block diagram of an advanced udder health monitoring and intervention in accordance with an exemplary embodiment of the present disclosure.
[0109] At 302, receiving real-time temperature data from a plurality of temperature sensors strategically placed on a cow's udder to monitor udder temperature, receiving udder swelling data from swelling sensors attached to the cow's udder to detect changes in udder size, receiving milk conductivity data from a milk conductivity sensor integrated into the milking apparatus to monitor milk properties, and receiving activity data from activity sensors attached to the cow to track movement, rumination patterns, and lying behaviour.
[0110] At 304, processing the real-time data received from said temperature sensors, swelling sensors, milk conductivity sensor, and activity sensors using a microcontroller operatively connected to the sensors, wherein the microcontroller applies machine learning algorithms to predict potential udder health issues based on patterns in the sensor data.
[0111] At 306, aggregating and analysing the processed sensor data using a central processing unit operatively connected to the microcontroller, wherein said central processing unit compares real-time data with historical health records to predict udder health conditions such as mastitis or infections.
[0112] At 308, generating automated treatment recommendations using an automated treatment recommendation module operatively connected to the central processing unit, said module configured to transmit real-time treatment recommendations to farm personnel based on the analysis of sensor data and historical health records.
[0113] At 310, transmitting real-time alerts, health status, and treatment transmitting said sensor data and health predictions through a communication network operatively connected to said microcontroller and central processing unit to an external server for further analysis or remote monitoring by veterinarians or farm management.
[0114] At 312, displaying real-time health alerts, status updates, and treatment recommendations to users through a user interface operatively connected to said the central processing unit allowing farm personnel to take proactive actions based on the health status of the cow.
[0115] FIG. 4 illustrates a perspective view of step by step procedure to find udder health, in accordance with an exemplary embodiment of the present disclosure.
[0116] Data collection within the system 100 begins with the plurality of temperature sensors 102 strategically placed on the cow's udder to continuously monitor real-time temperature. These temperature readings are crucial for detecting early signs of infection or inflammation, providing valuable data points that the system processes. Alongside this, the swelling sensors 104 continuously detect changes in the size of the udder, offering real-time insights into any physical abnormalities, such as swelling, which can be indicative of health issues like mastitis.
[0117] Milk quality is measured through the milk conductivity sensor 106, which is integrated into the milking apparatus to capture electrical conductivity data during each milking session. Changes in milk conductivity are directly related to udder health, offering another layer of data for comprehensive analysis. Additionally, the activity sensors 108 attached to the cow constantly monitor movement patterns, rumination, and lying behaviour, which reflect overall cow wellness and possible health concerns.
[0118] All this data is transmitted to the microcontroller 110, which is responsible for processing and integrating the real-time inputs from the temperature sensors 102, swelling sensors 104, milk conductivity sensor 106, and activity sensors 108. By collecting and analysing data from multiple points simultaneously, the system ensures a continuous and holistic understanding of udder health conditions. This real-time collection of various data points enables timely and accurate predictions of udder health, which are further processed by the central processing unit 112 to forecast potential issues before they become critical. This integrated data collection process supports proactive health management, contributing significantly to improved cow welfare and overall farm productivity.
[0119] Data transmission in the system 100 begins with the seamless transfer of collected sensor data from the microcontroller 110 to the central processing unit 112. As real-time data from the plurality of temperature sensors 102, swelling sensors 104, milk conductivity sensor 106, and activity sensors 108 is processed, the microcontroller 110 ensures all relevant information is gathered and immediately transmitted for further analysis.
[0120] The communication network 116 plays a critical role in this process, facilitating the wireless transmission of data between the microcontroller 110 and central processing unit 112. This network ensures smooth and uninterrupted communication, allowing data to flow efficiently from the sensors to the processing unit. The communication network 116 also enables remote transmission of sensor data to external servers for additional data processing or long-term storage.
[0121] Real-time data transmission ensures the system 100 provides up-to-date insights on udder health conditions. Once processed, the results are sent through the communication network 116 to the user interface 118, where farmers and veterinarians can access actionable insights, alerts, and health recommendations, ensuring prompt decision-making and intervention based on real-time data transmission.
[0122] Data processing within the system 100 begins as sensor data from the temperature sensors 102, swelling sensors 104, milk conductivity sensor 106, and activity sensors 108 is transmitted to the central processing unit 112. The central processing unit 112 aggregates all the incoming data and immediately begins analysing it to identify patterns that indicate potential udder health conditions.
[0123] The microcontroller 110 plays a crucial role in pre-processing the data before sending it to the central processing unit 112. It runs embedded machine learning algorithms that perform predictive analytics on the real-time data, offering initial insights into udder health.
[0124] Once the data reaches the central processing unit 112, it undergoes further processing using advanced predictive analytics. This analysis involves comparing real-time sensor measurements with historical data to detect abnormal patterns. The system 100 processes this data to identify any early signs of mastitis, infection, or other udder-related health issues. Based on this processing, the automated treatment recommendation module 114 generates actionable treatment recommendations ensuring farmers receive timely and accurate insights into the health of their livestock.
[0125] Health analysis within the system 100 takes place as the central processing unit 112 begins analysing real-time data received from multiple sensors. The temperature sensors 102 continuously monitor udder temperature, while the swelling sensors 104 detect changes in udder size. At the same time, the milk conductivity sensor 106 measures variations in milk conductivity, and the activity sensors 108 track the cow's movement and behaviours patterns.
[0126] As this data is processed, the central processing unit 112 compares the current sensor readings with historical data stored in the system 100. This comparison allows the system 100 to identify trends, detect deviations from normal patterns, and assess potential risks to udder health. Using embedded machine learning algorithms in the microcontroller 110, predictive analytics are applied to forecast possible udder health issues before clinical symptoms become apparent.
[0127] The health analysis identifies any signs of mastitis or other udder infections, alerting farm personnel to potential health risks. The automated treatment recommendation module 114 uses the analysed data to generate tailored treatment recommendations, guiding the farm staff in implementing timely interventions to maintain optimal udder health.
[0128] The recommendation process in the system 100 takes place through the automated treatment recommendation module 114, which is operatively connected to the central processing unit 112. After health analysis identifies potential udder health risks using data from temperature sensors 102, swelling sensors 104, milk conductivity sensors 106, and activity sensors 108, the system 100 generates precise treatment recommendations.
[0129] The automated treatment recommendation module 114 processes the analyzed data, comparing it with historical health records and best-practice treatment protocols. By leveraging the predictive analytics executed by the microcontroller 110, the module 114 determines the most effective interventions for preventing or addressing the identified health issues. These recommendations are tailored to the cow's specific condition, taking into account the severity of detected symptoms and the potential risks.
[0130] These recommendations are then transmitted to farm personnel through the user interface 118. The user interface 118 displays real-time alerts, treatment suggestions, and actionable steps that farmers and veterinarians can follow. This process allows for timely and accurate interventions, ensuring proactive udder health management and minimizing the risk of worsening conditions or costly treatments. The recommendations serve to guide effective and efficient treatment, improving overall farm productivity and animal well-being.
[0131] The recommendation process in the system 100 takes place through the automated treatment recommendation module 114, which is operatively connected to the central processing unit 112. After health analysis identifies potential udder health risks using data from temperature sensors 102, swelling sensors 104, milk conductivity sensors 106, and activity sensors 108, the system 100 generates precise treatment recommendations.
[0132] The automated treatment recommendation module 114 processes the analyzed data, comparing it with historical health records and best-practice treatment protocols. By leveraging the predictive analytics executed by the microcontroller 110, the module 114 determines the most effective interventions for preventing or addressing the identified health issues. These recommendations are tailored to the cow's specific condition, taking into account the severity of detected symptoms and the potential risks.
[0133] These recommendations are then transmitted to farm personnel through the user interface 118. The user interface 118 displays real-time alerts, treatment suggestions, and actionable steps that farmers and veterinarians can follow. This process allows for timely and accurate interventions, ensuring proactive udder health management and minimizing the risk of worsening conditions or costly treatments. The recommendations serve to guide effective and efficient treatment, improving overall farm productivity and animal well-being.
[0134] User interaction with the system 100 revolves around the user interface 118, which provides seamless access to real-time data and actionable insights. Farmers and veterinarians engage with the system by monitoring health information from sensors, including temperature sensors 102, swelling sensors 104, and milk conductivity sensors 106, and activity sensors 108. The user interface 118 ensures that users interact with the system efficiently, allowing them to view critical data such as temperature readings, swelling levels, and other health parameters.
[0135] Through the user interface 118, users receive health alerts and recommendations generated by the automated treatment recommendation module 114. These alerts notify users when certain thresholds are exceeded, prompting immediate intervention. The ability to set and adjust data thresholds further enhances user interaction, allowing farmers to customize the system based on specific health management needs.
[0136] Additionally, the user interface 118 supports long-term health monitoring, enabling users to track historical data trends. Users interact with this feature by reviewing past health data and making informed decisions on preventative care measures. The intuitive design of the interface encourages frequent interaction ensuring users remain informed about the health status of the cows and can respond proactively to emerging health concerns.
[0137] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
, Claims:I/We Claim:
1. An advanced udder health monitoring and intervention system (100) comprising:
a plurality of temperature sensors (102) strategically placed on a cow's udder to measure real-time temperature;
a swelling sensors (104) attached to the cow's udder to detect changes in udder size;
a milk conductivity sensor (106) integrated into the milking apparatus to measure milk conductivity;
a activity sensors (108) attached to the cow to monitor movement, rumination patterns, and lying behaviour;
a microcontroller (110) operatively connected to said an temperature sensors (102), an swelling sensors (104), and milk conductivity sensor (106), and activity sensors (108), said an microcontroller (110) configured to process real-time data from said sensors and perform predictive analytics using embedded machine learning algorithms;
a central processing unit (112) operatively connected to said microcontroller (110), said central processing unit (112) configured to aggregate, analyse, and process sensor data to generate predictions of udder health conditions based on historical data and real-time measurements;
an automated treatment recommendation module (114) operatively connected to said central processing unit (112), said automated treatment recommendation module (114) configured to generate real-time treatment recommendations based on data analysis, and transmit the recommendations to farm personnel;
a communication network (116) configured to enable wireless transmission of sensor data between said microcontroller (110), central processing unit (112), and an external server for additional data processing or remote monitoring;
a user interface (118) operatively connected to said central processing unit (112), said user interface (118) configured to provide real-time alerts, health status, and actionable recommendations for farmers and veterinarians, allowing for efficient monitoring of cow health and proactive interventions;
a power supply unit (120) operatively connected to said sensors, microcontroller (110), and central processing unit (112), said power supply unit (120) providing continuous energy to ensure uninterrupted operation of the system (100).
2. The system (100) as claimed in claim 1, wherein the temperature sensors (102) are configured to continuously monitor temperature variations and generate alerts when abnormal temperature fluctuations indicative of infection or inflammation are detected.
3. The system (100) as claimed in claim 1, wherein the swelling sensors (104) are flex sensors that provide real-time measurements of udder size changes, enabling early detection of swelling associated with mastitis or other udder health issues.
4. The system (100) as claimed in claim 1, wherein the activity sensors (108) monitor cow behaviour patterns, including movement and lying times, and are configured to generate predictive alerts when significant deviations from normal activity levels, indicative of health issues, are detected.
5. The system (100) as claimed in claim 1, wherein the communication network (116) is configured to transmit real-time sensor data and health alerts to an external server via internet of things (IOT) protocols, enabling remote monitoring and additional data processing for enhanced analysis and intervention.
6. A method (100) for advanced udder health monitoring and intervention comprising:
receiving real-time temperature data from a plurality of temperature sensors (102) strategically placed on a cow's udder to monitor udder temperature, receiving udder swelling data from swelling sensors (104) attached to the cow's udder to detect changes in udder size, receiving milk conductivity data from a milk conductivity sensor (106) integrated into the milking apparatus to monitor milk properties, and receiving activity data from activity sensors (108) attached to the cow to track movement, rumination patterns, and lying behaviour;
processing the real-time data received from said temperature sensors (102), swelling sensors (104), milk conductivity sensor (106), and activity sensors (108) using a microcontroller (110) operatively connected to the sensors, wherein the microcontroller (110) applies machine learning algorithms to predict potential udder health issues based on patterns in the sensor data;
aggregating and analysing the processed sensor data using a central processing unit (112) operatively connected to said microcontroller (110), wherein said central processing unit (112) compares real-time data with historical health records to predict udder health conditions such as mastitis or infections;
generating automated treatment recommendations using an automated treatment recommendation module (114) operatively connected to said central processing unit (112), said module (114) configured to transmit real-time treatment recommendations to farm personnel based on the analysis of sensor data and historical health records;
transmitting real-time alerts, health status, and treatment transmitting said sensor data and health predictions through a communication network (116) operatively connected to said microcontroller (110) and central processing unit (112) to an external server for further analysis or remote monitoring by veterinarians or farm management;
displaying real-time health alerts, status updates, and treatment recommendations to users through a user interface (118) operatively connected to said central processing unit (112), allowing farm personnel to take proactive actions based on the health status of the cow.
7. The method (100) as claimed in claim 1, wherein the machine learning algorithms applied in the microcontroller (110) are trained on a historical dataset of udder health conditions, enabling the system to improve predictive accuracy by continuously learning from new data collected through said temperature sensors (102), swelling sensors (104), milk conductivity sensor (106), and activity sensors (108).
8. The method (100) as claimed in claim 1, wherein the communication network (116) facilitates real-time transmission of alerts and treatment recommendations to a mobile device, allowing remote monitoring and management of cow health by farm personnel and veterinarians.
9. The method (100) as claimed in claim 1, wherein the automated treatment recommendation module (114) adjusts the treatment recommendations based on customizable thresholds for each cow, allowing farmers to set specific health benchmarks tailored to individual animal needs.
10. The method (100) as claimed in claim 1, wherein the user interface (118) dynamically updates the health status of the cow and displays historical trends, providing a visual representation of changes in udder health over time, enabling more informed decision-making for proactive interventions.

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