Description:Field of the Invention
This invention relates to Computer vision and AI based Cattle Health Monitoring System.
Background of the Invention
In order to ensure that the animals in a farm remain productive and healthy, it is important to keep track of their health throughout. The conventional ways of monitoring cattle health involve manual observation and periodic veterinary calls that are time-consuming, tedious and could easily go wrong due to human error. The incorporation of technology into animal husbandry has opened up an avenue for better monitoring of cattle health. That is why we propose creating a machine learning model for cattle welfare which would enable real-time automated tracks on different states of animal health and behaviour. Under this model, there are eight categories of livestock’s wellness levels and activities: grazing; grazing sittingly; healthy; sitting; standing; standing grazingly; standing sittingly, and unhealthy. With the Raspberry Pi – a portable computing system which is low cost – this model can be employed by farmers as it can be expanded inexpensively without needing too much infrastructure with them offering constant surveillance throughout. It employs computer vision and machine learning techniques in analyzing images or videos involving cows to enable accurate detection of their well-being as well as physical disposition at any given time.
Livestock management practices can be improved significantly through such a model, as it will detect any potential health problems at an early stage, optimize pasture patterns and reduce less manual monitoring. When unhealthy animals are identified early, they can be assisted immediately which minimizes the spread of diseases and improves the whole herd’s state. Besides, understanding cattle activity patterns helps in evaluating their welfare and ensuring that they graze and rest appropriately. For this reason, besides enhancing animal welfare, this technology-driven approach enhances economic sustainability in livestock farming by maximizing productivity while minimizing losses.
US9538729B2 Disclosed are a system, device and process for monitoring physical and physiological features of livestock through a unique monitoring system and device. Basic and Smart tags are placed on livestock to monitor, among other things, temperature, movement, location, posture, pulse rate, and other physical and physiological features. Information is relayed from Basic tags, in one embodiment, to Smart tags that requests the information and receives the information from the basic tags. Smart tags send information to a mobile unit controller and/or home base so that requested information is sent to an end user that monitors the livestock for signs of illness. Potentially ill animals are segregated from the herd for further evaluation and minimization of exposure risk to the rest of the herd. This early detection system saves livestock and ensures a healthier herd for livestock farmers.
RESEARCH GAP:
? Real-time Monitoring: In addition, this model allows continuous and real-time monitoring of cattle’s health status and activities thereby facilitating the immediate detection and response to any emerging issues. The need for regular checks is therefore eliminated making sure that illnesses are detected early enough.
? Automation and Efficiency: This means that there is lesser work force as well as reduced time to be spent on carrying out manual observations of cattle for their health. Consequently, the resources are freed up hence enabling these workers to concentrate more on other important duties with regards to farm management efficiency generally seen in automation procedures on livestock farms.
KR20190047396A The present invention relates to a device and a method for analyzing the activity of livestock by collecting motion data from a sensor device inserted into ruminant stomachs of the livestock and monitoring livestock health and disease management information by using the activity information. According to the present invention, the device for monitoring the activity of livestock by using a sensor device inserted into ruminant stomachs of the livestock by means of oral administration comprises: a data sensing unit for receiving sensing data including the motion data of ruminant stomachs from the sensor device; an activity analysis unit for analyzing the intake activity, the rumination activity, and the rest activity by using the received sensing data; and a monitoring unit for storing analyzed activity information and providing information on the health and disease of the livestock for which activity information is not analyzed so as to monitor the same.
RESEARCH GAP:
? Cost-Effective Solution: A cheap way to reach a lot of farmers is to build the model with a Raspberry Pi. This low-priced technology allows for its adoption on a large scale especially in poor resource regions.
? Early Disease Detection: Using this model, unhealthy cows can be detected earlier thus preventing diseases from spreading within the herd. Early detection of health problems can help reduce veterinary expenses and improve animal welfare.
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 Computer vision and AI based Cattle Health Monitoring System.
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 diagram shows in great detail how an elaborate cattle management system that uses advanced technologies and power management systems for efficient livestock monitoring and maintenance. The central component of such a system is the Raspberry Pi (11) which acts as the central control unit synchronizing various parts of the structure towards smooth functioning. The system utilizes 4K camera (2) that has resolution up to 3840x2160p enabling explicit visual checkup of animals. In addition, there is an infrared camera (9) that operates on night vision principles hence allowing uninterrupted surveillance even under dim light conditions. These cameras send their information straight into the Raspberry Pi so it can use that data. The information from the cameras and other sensors is fed through a Wi-Fi module (6), which makes it possible for the system to transfer data to cloud storage (7) for backup and historical data analysis. And so, this wireless connectivity also supports remote monitoring (8), thus allowing the farm managers to monitor their farms from any point, thereby improving their decision-making and response time. For real-time processing and analysis of data, a neural stick (10) is used as an add-on with fast computing power included. Henceforth, the system is capable of quickly pinpointing certain patterns like cattle behavior or health related indicators that are out of ordinary. Farm personnel can control manually inputting data into the system using input devices such as a mouse (13) and keyboard(14). This processed data can be viewed on an LCD screen (5) that is operated by a microcontroller (12). The purpose of the microcontroller is to ensure that visual outputs are correctly displayed in order to facilitate understanding by farm staff. A heat sink (15) manages thermal management for maintaining optimal operating temperatures for Raspberry Pi and other heat-sensitive components ensuring long-term reliability and performance. Powering the entire setup is a 12V 3amp lithium polymer battery (17), which is charged by a solar panel (16). This sustainable power source ensures that the system can operate independently of external power grids, making it ideal for remote farming locations. The power supply management module further consists of a charger (18), which connects to an AC outlet (19) to convert changing currents (20), ensuring consistent power delivery and battery health.
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: General Architecture of the system
Figure 2: Detailed Structure of the system with power management
Figure 3: Algorithmic view of the system
Figure 4: unhealthy.
Figure 5: Healthy Result.
Figure 6: Cow movement and action result.
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 diagram shows in great detail how an elaborate cattle management system that uses advanced technologies and power management systems for efficient livestock monitoring and maintenance. The central component of such a system is the Raspberry Pi (11) which acts as the central control unit synchronizing various parts of the structure towards smooth functioning. The system utilizes 4K camera (2) that has resolution up to 3840x2160p enabling explicit visual checkup of animals. In addition, there is an infrared camera (9) that operates on night vision principles hence allowing uninterrupted surveillance even under dim light conditions. These cameras send their information straight into the Raspberry Pi so it can use that data. The information from the cameras and other sensors is fed through a Wi-Fi module (6), which makes it possible for the system to transfer data to cloud storage (7) for backup and historical data analysis. And so, this wireless connectivity also supports remote monitoring (8), thus allowing the farm managers to monitor their farms from any point, thereby improving their decision-making and response time. For real-time processing and analysis of data, a neural stick (10) is used as an add-on with fast computing power included. Henceforth, the system is capable of quickly pinpointing certain patterns like cattle behavior or health related indicators that are out of ordinary. Farm personnel can control manually inputting data into the system using input devices such as a mouse (13) and keyboard(14). This processed data can be viewed on an LCD screen (5) that is operated by a microcontroller (12). The purpose of the microcontroller is to ensure that visual outputs are correctly displayed in order to facilitate understanding by farm staff. A heat sink (15) manages thermal management for maintaining optimal operating temperatures for Raspberry Pi and other heat-sensitive components ensuring long-term reliability and performance. Powering the entire setup is a 12V 3amp lithium polymer battery (17), which is charged by a solar panel (16). This sustainable power source ensures that the system can operate independently of external power grids, making it ideal for remote farming locations. The power supply management module further consists of a charger (18), which connects to an AC outlet (19) to convert changing currents (20), ensuring consistent power delivery and battery health.
Cattle health detection begins with data acquisition through cameras strategically placed across livestock areas, ensuring comprehensive coverage to avoid blind spots. Images and videos captured undergo meticulous preprocessing steps. This includes resizing to maintain uniform input dimensions, noise reduction to enhance clarity, and normalization of pixel values for optimal machine learning model performance. These preparatory measures ensure that subsequent analysis is based on standardized and high-quality data, crucial for accurate health assessment. In order to detect complex patterns in images, feature extraction uses convolutional neural networks (CNNs). Numerous layers in CNN identify important features that are related to the health and activity of cows like shapes, edges and textures. Those features are then transferred through a fully connected neural network for classification. The final layer of this network applies the SoftMax activation function which is responsible for giving probabilities to each of eight classes namely; grazing, grazing and sitting, healthy, sitting, standing, standing and grazing, standing and sitting as well as unhealthy. This categorization process is important in establishing the current status of cattle which of course includes their health conditions and activities. After classification, actionable insights are generated by the system. Such actions include sending immediate alerts to farmers whos’ cattle have been classified as being sick. These classifications findings also get stored on a database that can be accessed later when there is need for more comprehensive processing. As such, it becomes possible to understand trends over time by analysing stored data in this post-processing phase. The knowledge obtained helps farmers decide how they should run their herds properly. The less urgent cases like those where cows are found to be grazing or sitting too often generate regular reports supporting continuous monitoring strategies that encourage proactive farming actions. To sum up, the coupling between cutting edge imaging technology and machine learning algorithms is a powerful framework for detecting cattle’s health. Starting from meticulous collection of relevant information to complex extraction of features and classification, all steps contribute into accurate estimation of the animal’s health. The system can notify farmers about ill cows with real-time alerts and also keep track of the ongoing monitoring by providing extensive reports hence it is among the most useful tools in modern animal husbandry. Farmers are able to improve animal well-being, enhance productivity and conserve natural resources by taking advantage of these capabilities.
Present invention comprises a plurality of FarmVigilance (1.1, 1.2, 1.N), 4K camera (3840 x 2160p) (2), Cloud Server (4), LCD screen (5), Wi-Fi module (6), cloud storage (7), remote monitoring system (8), an infrared camera (9), neural stick (10), Raspberry Pi (11), Microcontroller (12), Mouse (13), Keyboard (14), Heat sink (15), solar panel (16), 12v 3amp Lithium Polymer battery (17), charger (18), an AC outlet (19), convert changing currents (20), convolutional neural network, wherein cameras has resolution up to 3840x2160p enabling explicit visual checkup of animals, there is an infrared camera that operates on night vision principles hence allowing uninterrupted surveillance even under dim light conditions.
In another embodiment provide the microprocessor-based machine learning model is proposed for real-time, automated tracking of cattle wellness, enhancing monitoring and surveillance systems to improve animal welfare.
In another embodiment sensors are used to collect the real time information and transfer to the cloud storage through the Wifi.
In another embodiment the wireless connectivity also supports remote monitoring (8), thus allowing the farm managers to monitor their farms from any point, thereby improving their decision-making and response time.
In another embodiment the neural stick (10) is used as an add-on with fast computing power included.
In another embodiment the processed data can be viewed on an LCD screen (5) that is operated by microcontroller.
In another embodiment the heat sink (15) manages thermal management for maintaining optimal operating temperatures for microprocessor and other heat-sensitive components ensuring long-term reliability and performance.
In another embodiment powering the entire setup is 12V 3amp lithium polymer battery (17), which is charged by solar panel (16).
In another embodiment the power supply management module further consists of charger (18), which connects to an AC outlet (19) to convert changing currents (20), ensuring consistent power delivery and battery health.
ADVANTAGES OF THE INVENTION
1. Better Animal Welfare: Continuous tracking of cows’ movements ensures that they are feeding, sleeping, and behaving normally. This enhances general health as well as animal welfare according to regulations governing them. Data-Driven Insights: Livestock behaviour data are accumulated and analysed by this system so as to give useful hints regarding their behaviour patterns changes over time. Consequently, herd owners get guided decisions for better operational tactics on farm management issues based on such pieces of advice.
2. Scalability is something that the model can easily be scaled to a large number of herds by setting up multiple Raspberry Pi units. As the farm develops, this adaptability allows for growth while ensuring that all cattle are well-monitored. Reduced Human Error is another advantage of automated health monitoring which eliminates the inconsistencies and errors associated with manual observation. This enables more accurate and reliable data on cattle health leading to better decision-making process.
3. The Model can also be embedded with environmental sensors for instance, in order to correlate cattle behaviour with weather conditions as well as other environmental factors. Understanding how environment affects cows’ health is holistic in nature.
4. Enhanced Productivity: By ensuring healthy management of cattle, this model contributes towards increased milk production, meat quality, and overall farm output. Healthy cattle are more productive meaning farmers will make more money.
, C , Claims:1. A computer vision and AI based cattle health monitoring system comprises a plurality of FarmVigilance (1.1, 1.2, 1.N), 4K camera (3840 x 2160p) (2), Cloud Server (4), LCD screen (5), Wi-Fi module (6), cloud storage (7), remote monitoring system (8), an infrared camera (9), neural stick (10), Raspberry Pi (11), Microcontroller (12), Mouse (13), Keyboard (14), Heat sink (15), solar panel (16), 12v 3amp Lithium Polymer battery (17), charger (18), an AC outlet (19), convert changing currents (20), convolutional neural network, wherein cameras has resolution up to 3840x2160p enabling explicit visual checkup of animals, there is an infrared camera that operates on night vision principles hence allowing uninterrupted surveillance even under dim light conditions.
2. The system as claimed in claim 1, wherein, provide the microprocessor-based machine learning model is proposed for real-time, automated tracking of cattle wellness, enhancing monitoring and surveillance systems to improve animal welfare.
3. The system as claimed in claim 1, wherein sensors are used to collect the real time information and transfer to the cloud storage through the Wifi.
4. The system as claimed in claim 1, wherein the wireless connectivity also supports remote monitoring (8), thus allowing the farm managers to monitor their farms from any point, thereby improving their decision-making and response time.
5. The system as claimed in claim 1, wherein the neural stick (10) is used as an add-on with fast computing power included.
6. The system as claimed in claim 1, wherein the processed data can be viewed on an LCD screen (5) that is operated by microcontroller.
7. The system as claimed in claim 1, wherein the heat sink (15) manages thermal management for maintaining optimal operating temperatures for microprocessor and other heat-sensitive components ensuring long-term reliability and performance.
8. The system as claimed in claim 1, wherein powering the entire setup is 12V 3amp lithium polymer battery (17), which is charged by solar panel (16).
9. The system as claimed in claim 1, wherein the power supply management module further consists of charger (18), which connects to an AC outlet (19) to convert changing currents (20), ensuring consistent power delivery and battery health.