Description:FIELD OF THE INVENTION
This invention relates to AI-Enabled Vision Device for Analyzing Bird Behavior and Environmental Dynamics through Machine Learning.
BACKGROUND OF THE INVENTION
Understanding bird behavior and how it interacts with the environment is essential in the age of ecological study. To study and interpret bird behavior and its relationship to the environment, our ground-breaking innovation combines cutting-edge technology, artificial intelligence, and sophisticated sensors. Real-time data collecting, intelligent analysis, and cutting-edge ecological research techniques are all provided by this combination. Our understanding of avian ecology is improved by the combination of hardware, AI, machine learning, and cloud technologies, which also influences the direction of more general ecological investigations.
One of the most important areas of ecological study is the investigation of the relationship between bird behavior and environmental dynamics. Birds provide information about relationships, species health, and habitat quality as important ecosystem components. Traditional data gathering techniques, however, have drawbacks in terms of timely capture, manual intervention, and thorough analysis. The complex relationship between bird behavior and elements including temperature, humidity, light, gases, vibrations, and noises makes it difficult to comprehend using traditional methods. This limit understanding of how birds and their surroundings interact. Data sharing struggles as a result of problems with display, storage, and accessibility. It takes technology, data science, and sensor fusion to provide a complete treatment. It is crucial to have a system that combines real-time collecting, AI-driven analysis, and secure cloud storage for open visualization. This approach must enable scientists, promoting greater understanding of bird behavior and ecological considerations.
US8092790B2 Various exemplary compounds, compositions, methods and devices are disclosed. An exemplary composition or formulation includes methyl anthranilate, fatty acid and an amine such as, but not limited to, monoethanolamine or triethanolamine. Such an exemplary composition is optionally an emulsion. An exemplary method applies an exemplary compound to an insect nest. Such an exemplary compound may be in a composition or formulation. Exemplary compounds optionally include semiochemicals of insects, plants and/or animals. Other exemplary compounds, compositions, methods and devices are also disclosed.
RESEARCH GAP: AI & Vision enabled solution is the novelty of the system.
US10920748B2 An automated system for mitigating risk from a wind farm. The automated system may include an array of a plurality of image capturing devices independently mounted in a wind farm. The array may include a plurality of low resolution cameras and at least one high resolution camera. The plurality of low resolution cameras may be interconnected and may detect a spherical field surrounding the wind farm. A server is in communication with the array of image capturing devices. The server may automatically analyze images to classify an airborne object captured by the array of image capturing devices in response to receiving the images.
RESEARCH GAP: AI & Vision enabled solution is the novelty of the system.
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-Enabled Vision Device for Analyzing Bird Behavior and Environmental Dynamics through Machine Learning.
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.
Data gathering, preprocessing, machine learning analysis, cloud-based storage, and data presentation are all integrated into the suggested device's methodical approach. This thorough procedure attempts to increase our comprehension of avian behavior and its complicated relationship with environmental factors. Data is obtained from a variety of hardware sensors to start the process. These sensors, which include an HD camera, a DHT sensor, a light sensor, a gas sensor, a vibration sensor, and a microphone sensor, record a variety of data. The HD camera captures real-time video footage of birds, recording their actions and interactions in their natural environments. These sensors simultaneously precisely record environmental elements such as temperature, humidity, light intensity, gases, vibrations, and background noise. Preprocessing is then applied to the raw data to improve its quality and usefulness. To guarantee accuracy in analysis, noise and inconsistent data from the sensor are removed. While environmental sensor data is calibrated and synced to precisely coincide with corresponding video frames, video data may be compressed for effective storage. Extraction of relevant characteristics from preprocessed data is the next stage. Key characteristics of bird behavior may be identified using computer vision algorithms from video data. Environmental sensor data is simultaneously translated into useful elements, including hourly averages or event-triggered data points. The basis for further investigation is these retrieved characteristics.
Data fusion, in which elements from video and sensor data are combined to generate a single dataset, is essential. The combined information makes it possible to correlate bird behavior with the surrounding environment. Timestamps align video frames with the relevant environmental data, assuring analytical correctness. Here, machine learning models are used, trained on the unified dataset. These models show patterns and connections between environmental factors and avian behavior. Regression algorithms forecast behavior metrics based on environmental factors, whereas classification techniques identify behaviors like eating or flying. The program uses trained models to evaluate real-time video feeds, identifying evolving bird behaviors and giving quick insights into how they interact with their surroundings. Predictive models shed light on the potential effects of shifting environmental variables on future behavior. A cloud server receives processed behavioral data that has been associated with environmental data for storing and analysis. Researchers may easily store data for the long term and retrieve it quickly thanks to this cloud architecture. Researchers use the cloud platform to display and interact with data, employing graphs, charts, and heatmaps to comprehend changes in the interactions between birds and their environments across time. Researchers learn important things about how birds react to changing environmental dynamics in this setting. By incorporating input into the process iteratively, models and data processing pipelines are improved, assuring responsiveness to changing research demands.
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: SYSTEM ARCHITECTURE
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.
Data gathering, preprocessing, machine learning analysis, cloud-based storage, and data presentation are all integrated into the suggested device's methodical approach. This thorough procedure attempts to increase our comprehension of avian behavior and its complicated relationship with environmental factors. Data is obtained from a variety of hardware sensors to start the process. These sensors, which include an HD camera, a DHT sensor, a light sensor, a gas sensor, a vibration sensor, and a microphone sensor, record a variety of data. The HD camera captures real-time video footage of birds, recording their actions and interactions in their natural environments. These sensors simultaneously precisely record environmental elements such as temperature, humidity, light intensity, gases, vibrations, and background noise. Preprocessing is then applied to the raw data to improve its quality and usefulness. To guarantee accuracy in analysis, noise and inconsistent data from the sensor are removed. While environmental sensor data is calibrated and synced to precisely coincide with corresponding video frames, video data may be compressed for effective storage. Extraction of relevant characteristics from preprocessed data is the next stage. Key characteristics of bird behavior may be identified using computer vision algorithms from video data. Environmental sensor data is simultaneously translated into useful elements, including hourly averages or event-triggered data points. The basis for further investigation is these retrieved characteristics.
Data fusion, in which elements from video and sensor data are combined to generate a single dataset, is essential. The combined information makes it possible to correlate bird behavior with the surrounding environment. Timestamps align video frames with the relevant environmental data, assuring analytical correctness. Here, machine learning models are used, trained on the unified dataset. These models show patterns and connections between environmental factors and avian behavior. Regression algorithms forecast behavior metrics based on environmental factors, whereas classification techniques identify behaviors like eating or flying. The program uses trained models to evaluate real-time video feeds, identifying evolving bird behaviors and giving quick insights into how they interact with their surroundings. Predictive models shed light on the potential effects of shifting environmental variables on future behavior. A cloud server receives processed behavioral data that has been associated with environmental data for storing and analysis. Researchers may easily store data for the long term and retrieve it quickly thanks to this cloud architecture. Researchers use the cloud platform to display and interact with data, employing graphs, charts, and heatmaps to comprehend changes in the interactions between birds and their environments across time. Researchers learn important things about how birds react to changing environmental dynamics in this setting. By incorporating input into the process iteratively, models and data processing pipelines are improved, assuring responsiveness to changing research demands.
ADVANTAGES OF THE INVENTION
1. Using a variety of hardware sensors allows for a thorough understanding of both bird behaviors and environmental factors. This comprehensive dataset enables researchers to investigate bird behavior in relation to many environmental factors, providing more thorough and accurate findings.
2. Researchers can quickly understand changing bird behaviour thanks to real-time analysis of video feeds using machine learning algorithms. This agility is particularly useful for recording unplanned and urgent activities.
3. The project's visualization tools make it possible to show relationships and patterns between bird behaviour and environmental factors. These discoveries have an impact on bird ecology as well as possible conservation implications.
4. By giving students and researchers firsthand exposure to cutting-edge technology and ecological research approaches, the program has the potential to be informative.
5. The project skillfully combines cutting-edge elements including sensors, machine learning algorithms, and cloud computing to provide a complex research tool that connects technological understanding and ecological awareness.
6. The hardware sensor configuration's adaptability, which can be modified to certain study questions and bird species, gives researchers a personalized method of inquiry.
7. Using a cloud-based storage architecture ensures that processed data is securely retained and easily accessible for researchers. This effectiveness eliminates the need for substantial local storage and makes it possible for researchers to collaborate remotely.
, Claims:1. An AI-Enabled Vision Device for Analyzing Bird Behavior and Environmental Dynamics through Machine Learning comprises of AI-Enabled Vision Device (10), Micro Phone Sensor (11), Vibration Sensor (12), Gas Sensor (13), Light Sensor (14), DHT Senor (15), GSM Modem (16), HD Camera (17), Raspberry Pi CM4 Board and Processing (18) and Battery Power Supply (19).
2. The system as claimed in claim 1, wherein a Raspberry Pi CM4 Board, equipped with an HD camera, GSM modem, DHT sensor, light sensor, gas sensor, vibration sensor, microphone sensor, and battery power supply, powers the AI-Enabled Vision Device; and it makes use of this arrangement to analyze bird behavior and environmental dynamics using machine learning.
3. The system as claimed in claim 1, wherein the key to the invention is the integration of a wide range of different hardware sensors, including an HD camera, a DHT sensor (which measures temperature and humidity), a light sensor, a gas sensor, a vibration sensor, and a microphone sensor; and this collection records a wide range of environmental factors and information on bird behavior.
4. The system as claimed in claim 1, wherein the high-definition HD camera captures real-time video of birds in their natural environments; and complex observations of the behaviors and interactions among numerous bird species are made possible by this ability.
5. The system as claimed in claim 1, wherein operating in real-time, the hardware sensors ensure uninterrupted data collection; and this consistent flow of information offers insights into both bird behaviors and prevailing environmental circumstances.
6. The system as claimed in claim 1, wherein the hardware setup is capable of transferring processed behavioral and environmental data to a cloud server for archival; and the scalability and accessibility of this cloud-based repository are ensured, meeting the needs of scholars.
7. The system as claimed in claim 1, wherein this invention enables the sensor network to be customized, allowing it to be tailored to the particular needs of the study site and the targeted bird species; and this adaptability guarantees effective data collection.
8. The system as claimed in claim 1, wherein the design enables researchers to remotely oversee data collecting, make modifications, and address problems, enhancing the efficiency of the study process.