Description:FIELD OF THE INVENTION
This invention relates to AI-Powered Sensor-Vision Platform for Remote and Real-Time Bird Health Prediction with GPS-Linked Alerts.
BACKGROUND OF THE INVENTION
The methods of bird health monitoring in aviaries and farms used traditionally are largely manual as well as time-consuming, labour-intensive and human oversight prone. Early signs of illness in birds like changes in posture, vocalization or behaviour often go undetected which results in increased morality rates and delayed diagnosis. The existing systems are not capable of providing automated, real-time and intelligent health assessments. Moreover, there is no system present with combine multimodal sensing like a system which can work on environmental, visual and physiological data at a single time with AI-driven analysis for accurate prediction of bird health conditions. In many of the cases energy is a hurdle in continuous operation of electronic monitoring systems in remote or rural areas. Therefore, there comes a need for a AI-based health monitoring solution which is self-sustained and solar powered that can automatically detect abnormalities and provide timely alerts for early intervention.
Recently, bird farming and poultry industries have grown substantially to meet the increasing global demand for eggs and meet. However, the welfare and health of birds is a critical concern as the diseases can spread in a fast manner in flocks and lead to reduced productivity, significant losses and ethical issues which are related to animal welfare. The bird health monitoring methods used traditionally primarily relies upon manual inspection by veterinarians or farm staff who observes physical symptoms, behavioural changes and morality patterns. These practices are labour-intensive as well as time consuming and also lacks off objectivity and consistency. Most importantly the early signs of illness such as altered vocalization, reduced activity or minor changes in feeding behaviour often go unnoticed which allows the diseases to progress undetected.
Furthermore, existing digital solutions in the market are either overly simplistic or require expensive infrastructure that is not sustainable or scalable, especially in resource-constrained and rural environments. Most of the available systems do not integrate the real-time physiological sensing with environmental and behavioural monitoring nor do they use advanced AI algorithms for providing predictive analytics. The lack of multimodal data fusion leads to poor diagnostic accuracy and delays in timely intervention. In addition, this system depends upon frequent manual maintenance or constant power sources making them unsuitable for continuous deployment in field conditions.
Energy is another major limitation. Farms which are located at remote areas with unstable electricity supply which makes it difficult to run continuous monitoring systems using the conventional power sources. Existing sensor-based systems are not designed for low-power operations and hence can’t function autonomously for extended periods without power replacement or manual recharging. Moreover, many of the systems do not support geolocation capabilities which are essential for tracking and managing bird populations across distributed or large farms.
Due to these challenges, there is a necessary need of an autonomous and comprehensive bird health monitoring system which can identify signs of disease early, operate in real-time and function reliable in field environments. This system should be capable of collecting the multimodal data such as physiological readings and numerical environmental readings as well as audio and visual inputs using dedicated edge devices. It should leverage artificial intelligence for deliver timely alerts and health prediction to caretakers including precise GPS location data. Additionally, the system must be self-sufficient in terms of energy by integrating battery with solar power which enables 24/7 operation without any external power dependency. Addressing these gaps would significantly enhance early detection of disease, improve welfare of bird and support sustainable poultry management practices.
US20230389525A1 The present disclosure relates to a system for measuring changes in the vital signs of poultry and a method for predicting whether poultry is infected with pathogens using the same. The present disclosure can be helpfully used to determine an overall health condition including the outbreak of various poultry diseases by measuring quantitative changes in vital signs of poultry in real time. In particular, the present disclosure provides reliable real-time information about changes in vital signs through comparison of body temperature and noise measurement values of poultry for each time period reflecting circadian changes of the poultry, rather than simple comparison of the body temperature and noise measurement values with simple reference values, and thus can be used for early diagnosis of highly infectious pathogens such as avian influenza virus and early isolation of confirmed cases, and ultimately for effective prevention of the spread of diseases.
RESEARCH GAP: Unlike the referenced system which focuses on vital signs (temperature and noise) and circadian analysis for pathogen prediction, the proposed system integrates multi-sensor IoT and edge-AI vision modules to monitor posture, behavior, GPS, and environmental data for a broader and more intelligent real-time poultry health assessment.
CN116934088A The invention discloses an intelligent pigeon breeding management method and system based on an analysis model, and relates to the technical field of management systems, wherein the pigeon breeding method comprises the following steps: the pigeon house environment analysis result is combined with the disease risk early warning mechanism by periodically acquiring characteristic data related to pigeon house environment, comprehensively analyzing the characteristic data, analyzing whether the pigeon house environment supports pigeon feeding, formulating a pigeon house optimization scheme according to the analysis result, periodically acquiring the characteristic data related to pigeon growth, establishing the disease risk early warning mechanism through the characteristic data, carrying out disease risk early warning on the pigeon through the disease risk early warning mechanism and generating a corresponding management strategy. The pigeon breeding system and the pigeon breeding method can evaluate the pigeon cage environment and early warn the disease risk of the pigeons, so that the breeding management is effectively optimized, the overall growth trend analysis is carried out on the pigeons bred in the same batch, the breeding effect is effectively evaluated, and the effectiveness of the breeding strategy is determined.
RESEARCH GAP: While the referenced system focuses on environmental analysis and disease risk early warning for pigeons using periodic data collection, the proposed system combines real-time multi-sensor data, edge AI-based posture and vocal analysis, and GPS tracking to enable continuous, individualized health monitoring and predictive diagnostics in poultry.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to AI-Powered Sensor-Vision Platform for Remote and Real-Time Bird Health Prediction with GPS-Linked Alerts.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed system discloses an intelligent and automated bird health monitoring system which integrates Internet of Things, artificial intelligence and renewable energy to provide continuous, real-time and remote surveillance of bird health conditions. The system is designed such that to address the limitations of traditional manual monitoring by using a dual-device architecture comprising an Edge Vision Data Collector Device (EVDCD) and a Smart Sensory Numeric Data Collector Device (SSNDCD). These two edge devices work independently using separate computing units, each of them dedicated to collecting and transmitting different types of data related to birds. This system is engineered such that it can be deployed in poultry farms, breeding facilities, aviaries, wildlife centuries and other environments, where examining and monitoring the health of individual or groups of birds is crucial in preventing disease outbreaks and ensuring animal welfare. The SSNDCD is consists of wide range of physiological as well as environmental sensors which includes humidity, gas (ammonia), temperature sensors, sound/vibration detectors and weight sensors integrated into perching surfaces to capture variations of body weight. Additionally, there is a GPS module for recording the geographical location of each data point, a real-time clock for timestamping and an SD card for offline data logging. The primary work of the SSNDCD is to collect real-time numeric data relevant to the birds’ physiological and surroundings status. This data is continuously sent to the centralized cloud-server using Wi-Fi for further analysis. The EVDCD Uses HD camera for visual and auditory data capture. The camera monitors bird movement, posture, feeding behaviour and any visible signs of distress, also, the vocal patterns are analysed using a microphone associated with the device which may indicate illness, pain or abnormal behaviour. This device uses lightweight edge AI algorithms for preprocessing the data which also includes image enhancement, object tracking and basic classification behaviour patterns which are executed locally on microcontrollers. Both EVDCD and SSNDCD are powered by a solar panel which is connected with a rechargeable battery system which makes the setup energy-autonomous which makes the system ideally usable in remote or power-constrained areas. These devices work together for sending synchronised multimodal data streams i.e. visual/audio data and numeric sensor data to the cloud server data. The preprocessing is done at cloud infrastructure on incoming data to normalise the data values, remove noise, align timestamps and prepare the datasets for AI-based health analysis. A pre-trained AI model is developed by using historical data of diseased and healthy birds is used to classify the health status of birds. The model applies a combination of supervised and unsupervised machine learning techniques for predicting whether a bird is stressed, healthy, sick or critical state. If the system detects any early disease symptoms or health abnormality the AI module immediately triggers an alert. This alert is sent to the caretaker’s mobile device or management dashboard which includes time of detection, the health condition, GPS location and recommended actions. The alert system helps in quick decision-making, such as adjusting environmental parameters, isolating the affected birds or initiating veterinary intervention. Its low energy consumption, modular design and intelligent data handling offer a reliable, novel and practical solution for modern bird health management. This system significantly improves reduces dependency on manual labour, early disease detection, minimizes mortality rates and supports ethical and sustainable bird farming and conservation practices.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: OVERALL ARCHITECTURE
FIGURE 2: SMART SENSORY NUMERIC DATA COLLECTOR DEVICE
FIGURE 3 EDGE VISION DATA COLLECTOR DEVICE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed system discloses an intelligent and automated bird health monitoring system which integrates Internet of Things, artificial intelligence and renewable energy to provide continuous, real-time and remote surveillance of bird health conditions. The system is designed such that to address the limitations of traditional manual monitoring by using a dual-device architecture comprising an Edge Vision Data Collector Device (EVDCD) and a Smart Sensory Numeric Data Collector Device (SSNDCD). These two edge devices work independently using separate computing units, each of them dedicated to collecting and transmitting different types of data related to birds. This system is engineered such that it can be deployed in poultry farms, breeding facilities, aviaries, wildlife centuries and other environments, where examining and monitoring the health of individual or groups of birds is crucial in preventing disease outbreaks and ensuring animal welfare. The SSNDCD is consists of wide range of physiological as well as environmental sensors which includes humidity, gas (ammonia), temperature sensors, sound/vibration detectors and weight sensors integrated into perching surfaces to capture variations of body weight. Additionally, there is a GPS module for recording the geographical location of each data point, a real-time clock for timestamping and an SD card for offline data logging. The primary work of the SSNDCD is to collect real-time numeric data relevant to the birds’ physiological and surroundings status. This data is continuously sent to the centralized cloud-server using Wi-Fi for further analysis. The EVDCD Uses HD camera for visual and auditory data capture. The camera monitors bird movement, posture, feeding behaviour and any visible signs of distress, also, the vocal patterns are analysed using a microphone associated with the device which may indicate illness, pain or abnormal behaviour. This device uses lightweight edge AI algorithms for preprocessing the data which also includes image enhancement, object tracking and basic classification behaviour patterns which are executed locally on microcontrollers. Both EVDCD and SSNDCD are powered by a solar panel which is connected with a rechargeable battery system which makes the setup energy-autonomous which makes the system ideally usable in remote or power-constrained areas. These devices work together for sending synchronised multimodal data streams i.e. visual/audio data and numeric sensor data to the cloud server data. The preprocessing is done at cloud infrastructure on incoming data to normalise the data values, remove noise, align timestamps and prepare the datasets for AI-based health analysis. A pre-trained AI model is developed by using historical data of diseased and healthy birds is used to classify the health status of birds. The model applies a combination of supervised and unsupervised machine learning techniques for predicting whether a bird is stressed, healthy, sick or critical state. If the system detects any early disease symptoms or health abnormality the AI module immediately triggers an alert. This alert is sent to the caretaker’s mobile device or management dashboard which includes time of detection, the health condition, GPS location and recommended actions. The alert system helps in quick decision-making, such as adjusting environmental parameters, isolating the affected birds or initiating veterinary intervention. Its low energy consumption, modular design and intelligent data handling offer a reliable, novel and practical solution for modern bird health management. This system significantly improves reduces dependency on manual labour, early disease detection, minimizes mortality rates and supports ethical and sustainable bird farming and conservation practices.
The system starts with initializing two independent microcontroller-based devices i.e. SSNDCD which collects physiological and environmental data while the EVDCD captures images and audio of the birds. Both devices operate continuously which is powered by solar-charged batteries and transmit their data using Wi-Fi to a cloud server, the cloud server preprocesses the data, then sends it to pretrained AI model for analysing the health of the birds. If any abnormality is detected then an alert is generated and sent to a mobile device along with the GPS location recommended actions. This system ensures automated bird health monitoring using energy-efficient, real-time, intelligent sensing and analysis. The algorithm used is as follows:
Algorithm Used
BEGIN SYSTEM
// ---------- DEVICE 1: SSNDCD (Numeric Sensor Data Collector) ----------
INITIALIZE SSNDCD
CONNECT sensors: temperature, humidity, ammonia, weight, vibration, GPS
CONNECT RTC, SD card, Wi-Fi module, solar-powered battery
LOOP every fixed interval (e.g., 5 minutes)
READ temperature, humidity, ammonia, vibration, weight
GET GPS location
GET current timestamp from RTC
STORE all readings in SD card with timestamp
IF Wi-Fi is connected THEN
PACKAGE sensor data with timestamp and GPS
SEND data to CLOUD_SERVER
ENDIF
END LOOP
// ---------- DEVICE 2: EVDCD (Edge Vision Data Collector) ----------
INITIALIZE EVDCD
CONNECT camera, microphone
LOAD lightweight AI model for behavior and vocal analysis
CONNECT Wi-Fi module and solar battery
LOOP continuously or motion/audio-triggered
CAPTURE image and audio
PROCESS image with AI_model to detect:
- abnormal posture
- signs of stress
- movement anomalies
PROCESS audio to detect:
- abnormal vocalization
- distress patterns
IF abnormal behavior OR vocalization detected THEN
FLAG data as potentially unhealthy
ENDIF
PACKAGE visual/audio results with timestamp
SEND processed data to CLOUD_SERVER
END LOOP
// ---------- CLOUD SERVER AND AI ANALYSIS MODULE ----------
INITIALIZE cloud environment
RECEIVE data from SSNDCD and EVDCD
PREPROCESS data:
- Normalize sensor values
- Align timestamp across data types
- Remove noise from images/audio
LOAD pretrained AI_HEALTH_MODEL
FOR each incoming bird record DO
EXTRACT features from sensor + vision data
PREDICT health status using AI_HEALTH_MODEL
OUTPUT: health_score, health_category (Healthy, Stressed, Sick, Critical)
IF health_category != "Healthy" THEN
GENERATE alert_message with:
- Bird ID (or GPS location)
- Health status
- Timestamp
- Recommended action
SEND alert to MOBILE_APP or caretaker dashboard
ENDIF
END FOR
// ---------- MOBILE APPLICATION / USER INTERFACE ----------
INITIALIZE mobile dashboard
RECEIVE and DISPLAY alert messages
DISPLAY:
- Health trends over time
- GPS locations of critical alerts
- Bird-specific profiles with historical data
END SYSTEM
ADVANTAGES OF THE INVENTION:
The key advantages that make it a valuable tool for bird health prediction are:
• The proposed system enables automated and continuous monitoring of bird health without requiring any manual observation. By capturing data at regular intervals from both audio/visual analysis and sensor inputs the system detects abnormal patterns in real time. This allows for the early identification of stress or diseases indicators which helps the caretakers or farm managers to respond before conditions get worsen, thereby reduces morality rates and improves flock health outcomes.
• Unlike the old traditional single-mode monitoring this proposed system utilizes multimodal data collections from two devices i.e. EVDCD and SSNDCD. The SSNDCD gathers environmental as well as numerical data such as temperature, humidity, body weight, ammonia levels and GPS location. The EVDCD captures audio and images for analysing distress sounds and behavioural cues. This fusion of environmental, physical and behavioural data results in a more holistic and accurate understanding of each bird’s health condition.
• This whole system works upon rechargeable batteries which is powered by solar panels which allows the system to work 24/7 in remote or rural location whether there is poor or lack of grid electricity. This makes the system energy efficient as well as sustainable.
• At the core of the system there present an artificial intelligence model which is pretrained by the existing data. This model uses machine learning algorithms for recognizing the patterns associated with sick, stressed, healthy or critically ill birds. It evaluates real-time as well as historical data to predict the likelihood of disease progression, enabling preventive action. This reduces the need for constant human oversights and improves decision-making through data-driven insights.
, Claims:1. An AI-based bird health monitoring system comprising a Smart Sensory Numeric Data Collector Device (SSNDCD) configured to capture environmental and physiological sensor data, an Edge Vision Data Collector Device (EVDCD) configured to capture audio and visual data, both devices being solar powered and connected through Wi-Fi to a cloud server, wherein a pretrained artificial intelligence model processes the multimodal data to predict bird health status and generates real-time alerts with GPS location.
2. The system as claimed in claim 1, wherein the EVDCD comprises a microcontroller, a camera, a microphone, a Wi-Fi module, a display unit, a storage unit, and a real-time clock, powered by a rechargeable solar battery.
3. The system as claimed in claim 1, wherein the EVDCD is configured to capture bird posture, movement, feeding behaviour, and vocalization, and preprocess the data locally using lightweight edge AI algorithms for behaviour and vocal analysis.
4. The system as claimed in claim 1, wherein the SSNDCD comprises sensors for temperature, humidity, ammonia, vibration, weight, and GPS, a microcontroller, a real-time clock, a display unit, and a storage unit for timestamped data logging.
5. The system as claimed in claim 1, wherein the SSNDCD autonomously operates using a solar-powered rechargeable battery to transmit real-time numeric data to the cloud server.
6. The system as claimed in claim 1, wherein the cloud server preprocesses the incoming data by normalizing values, removing noise, and aligning timestamps before analysis.
7. The system as claimed in claim 1, wherein the artificial intelligence algorithm classifies bird health into categories including healthy, stressed, sick, and critical based on multimodal data.
8. The system as claimed in claim 1, wherein the generated alert includes details of the health status, timestamp, GPS location, and recommended actions for intervention.
9. The system as claimed in claim 1, wherein the alerts are transmitted to a mobile device or caretaker dashboard for enabling immediate response.
10. The system as claimed in claim 1, wherein historical and real-time data are processed by the pretrained model to evaluate disease progression and support preventive measures.