DESC:FIELD
The present disclosure relates to detection to mastitic milk.
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.
In the dairy industry, mastitis is a known term. Mastitis is the persistent, inflammatory reaction of the udder tissue due to physical trauma or microorganism’s infections. Mastitis, a potentially fatal mammary gland infection, is the most common disease in dairy cattle worldwide. It is also the costliest disease to the dairy industry. Milk from cows suffering from mastitis has an increased somatic cell count. Prevention and Control of mastitis requires consistency in sanitizing the cow barn facilities, proper milking procedure and segregation of infected animals.
In order to detect mastitis, there are various methods like somatic cell count (SCC) measurement, California Mastitis Test (CMT) and measurement of electrical conductivity (EC) of the milk that can be prone to human errors. Moreover, detecting and curing the cattle from mastitis should be timely as it could lead to losses of the dairy farm or could be potentially fatal to the cattle.
Therefore, there is felt a need to provide an IoT enabled mastitic milk detection system and a method thereof which alleviates the above mentioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide an IoT enabled mastitic milk detection system.
Another object of the present disclosure is to provide a system that provides dairy cattle’s health diagnosis and mastitis detection by AI/ML techniques.
Yet another object of the present disclosure is to provide a system that increases accuracy of prediction and detection of mastitis.
Still another object of the present disclosure is to provide a system that can be well fitted with any milking machine.
Another object of the present disclosure is to provide a system that performs easy monitoring of the dairy cattle health.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a method for mastitic milk detection. The method includes the following steps:
• receiving inputs, parameters of the cow from which milk is to be drawn, and environmental conditions;
• selecting a reference table from a plurality of reference indexed tables stored in a repository wherein each of the reference table is indexed according to a specific breed of a cow, its age environmental conditions and contains threshold values corresponding to Electrical conductivity (EC), PH and temperature which designate mastitic milk;
• extracting corresponding threshold values from the selected reference table;
• sensing EC value of the milk in a steady state;
• sensing PH value of the milk in a steady state;
• sensing temperature of the milk in a steady state;
• comparing, the sensed EC value with the threshold value in the selected reference table to check if the sensed EC value is greater than the threshold value; and
• if the sensed EC value exceeds the referenced value, comparing the PH and the temperature sensed values with the respective threshold values to check if the sensed PH and temperature value is within the range of the PH and temperature threshold values to confirm that the milk is mastitic.
In an embodiment, the step of receiving inputs, parameters of cow from which milk is been drawn, and environmental conditions includes:
• receiving a cow identifier; and
• breed of the cow.
The present disclosure envisages an IoT enabled mastitic milk detection system comprising an IoT enabled device detachably attached to a milking machine, a server communicatively coupled to the IoT enabled device, and an application loaded in a user device.
The IoT enabled device is configured to receive a unique identifier (ID) associated with a cow being milked, and is further configured to sense a plurality of parameters associated with the milk of the cow collected in the milking machine to generate corresponding sensed values.
In an embodiment, an RFID tag is provided on each of the cows, each of the RFID tags configured to store the ID associated with the corresponding cow. The IoT enabled device includes an RFID reader for reading the RFID tag provided on the cow being milked to receive the associated ID.
The user interface is configured to receive the ID associated with the cow being milked from a user.
In an embodiment, the IoT enabled device includes a sensing unit and a first communication unit. The sensing unit is configured to sense the plurality of parameters of the milk collected during milking of the cow by the milking machine and is further configured to generate the sensed values corresponding to the sensed parameters.
The first communication unit is configured to transmit the sensed values and the ID to the server.
The server is configured to receive the sensed values from the IoT enabled device and is further configured to detect mastitic milk based on the received sensed values.
In an embodiment, the server includes a mastitis identifier, a notification generating unit and a second communication unit.
The mastitis identifier is configured to detect level of mastitis in the milk based on the received sensed values and is configured to generate an identification signal based on the detection.
In an embodiment, the mastitis identifier includes a repository, a first crawler and extractor, a second crawler and extractor, a first comparator, a second comparator and an analyser.
The repository is configured to store:
• a first reference table having a list of the parameters and a threshold value corresponding to each of the parameters; and
• a second reference table having a list of the unique IDs, a breed of the cow corresponding each of the IDs, and a breed threshold value corresponding to each of the breeds corresponding to each of the IDs.
The first crawler and extractor is configured to cooperate with the repository configured to cooperate with the repository to crawl through the first reference table to extract threshold values of the sensed parameters corresponding to the unique ID of the cow.
The second crawler and extractor is configured to cooperate with the repository to crawl through the second reference table to extract the breed threshold value corresponding to the ID.
The first comparator is configured to cooperate with the first crawler and extractor to compare each of the sensed values with the corresponding extracted threshold values to generate a first comparison result.
The second comparator is configured to cooperate with the second crawler and extractor to compare each of the sensed values with the corresponding extracted breed threshold values to generate a second comparison result.
The analyser is configured to cooperate with the first comparator and the second comparator to analyse the first comparison result and the second comparison result to generate the identification signal.
In an embodiment, the analyser includes a signal generator, wherein the signal generator is configured to:
• generate the identification signal corresponding to a highest level mastitis risk, when the first comparison result and the second comparison result both are beyond the respective threshold values;
• generate the identification signal corresponding to a first level mastitis risk, when the first comparison result is within the corresponding threshold value and the second comparison result is not within the corresponding breed threshold value;
• generate the identification signal corresponding to a second level mastitis risk, when the first comparison result is beyond the corresponding threshold value but the second comparison result is within the corresponding breed threshold value; and
• no identification signal is generated, when the first comparison result and the second comparison result are within the corresponding threshold values.
The server further comprises a learning unit configured to receive the sensed values to:
• monitor and record changes in the sensed values and generate a monitoring result;
• provide different solutions to different breed of the cows based on the monitoring result using Artificial Intelligence and machine learning techniques; and
• perform self-learning and adaptive learning based on any inputs provided by the user and AI/ML techniques.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
An IoT enabled mastitic milk detection system and a method thereof, of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a flow diagram depicting steps involved in a method for IoT enabled mastitic milk detection;
Figure 2 illustrates a block diagram of the IoT enabled mastitic milk detection system;
Figure 3 illustrates a front view of IoT enabled device;
Figure 4 illustrates a sectional view of IoT enabled device;
Figure 5 illustrates an isometric view of IoT enabled device;
Figure 6a and 6b illustrate a front view of the arrangement of the IoT enabled device detachably attached to a milking machine; and
Figure 7a, 7b, 7c and 7d illustrate graphs for the results depicting a cow producing mastitic milk.
LIST OF REFERENCE NUMERALS
200 system
202 IoT enabled device
204 milking machine
206 server
208 Application loaded in a user device
210 RFID reader
212 sensing unit
213 De-noising unit
214 first communication unit
216 mastitis identifier
218 notification generating unit
220 second communication unit
222 repository
224 first crawler and extractor
226 second crawler and extractor
228 first comparator
230 second comparator
232 analyser
234 signal generator
236 Display screen
238 user interface
240 visual indicator
242 milk chamber
244 learning unit
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed elements.
The terms first, second, third, etc., should not be construed to limit the scope of the present disclosure as the aforementioned terms may be only used to distinguish one element, component, region, or section from another element, component, region, or section. Terms such as first, second, third etc., when used herein do not imply a specific sequence or order unless clearly suggested by the present disclosure.
An IoT enabled mastitic milk detection system and a method thereof of the present disclosure, is described with reference to Figure 1 through Figure 7d.
Figure 1 illustrate a flow diagram of a method for IoT enabled mastitic milk detection.
The method includes the following steps:
• Step 102: receiving inputs, parameters of the cow from which milk is to be drawn, and environmental conditions;
• Step 104: selecting a reference table from a plurality of reference indexed tables stored in a repository wherein each of the reference table is indexed according to a specific breed of a cow, its age environmental conditions and contains threshold values corresponding to Electrical conductivity (EC), PH and temperature which designate mastitic milk;
• Step 106: extracting corresponding threshold values from the selected reference table;
• Step 108: sensing EC value of the milk in a steady state;
• Step 110: sensing PH value of the milk in a steady state;
• Step 112: sensing temperature of the milk in a steady state;
• Step 114: comparing, the sensed EC value with the threshold value in the selected reference table to check if the sensed EC value is greater than the threshold value; and
• Step 116: if the sensed EC value exceeds the referenced value, comparing the PH and the temperature sensed values with the respective threshold values to check if the sensed PH and temperature value is greater than the PH and temperature threshold value to confirm that the milk is mastitic.
In an embodiment, before sensing the EC, PH and temperature of the milk, the sensors are checked to be in a steady state for providing better values of the milk during milking process using milking machine.

In an embodiment, the step of receiving (102) inputs, parameters of cow from which milk is been drawn, and environmental conditions includes:
• receiving a cow identifier; and
• breed of the cow.
Referring to Figure 2, the IoT enabled mastitic milk detection system (hereinafter referred as “system”) (200) comprises an IoT enabled device (202) detachably attached to a milking machine (204), a server (206) communicatively coupled to the IoT enabled device (202), and an application loaded in a user device (208).
In an embodiment, as mentioned in Figure 6a and 6b, the IoT enabled device (202) is detachably attached to the milking machine (204).
The IoT enabled device (202), as referred in Figure 3, is configured to receive a unique identifier (ID) associated with a cow being milked, and is further configured to sense a plurality of parameters associated with the milk of the cow collected in the milking machine (204) to generate corresponding sensed values.
In an embodiment, an RFID tag is provided on each of the cows, each of the RFID tags configured to store the ID associated with the corresponding cow. The IoT enabled device (202) includes an RFID reader (210) for reading the RFID tag provided on the cow being milked to receive the associated ID.
Referring to Figure 5, the IoT enabled device (202) further includes one or more visual indicators (240) and a user interface (238). The visual indicator (240) is configured to indicate:
• the status of the internet connectivity to the user;
• milking of the cow by the milking machine (204);
• charging of the indicators; and
• power on/off.
The visual indicator (240) can be a LED.
The user interface (238) is configured to receive the ID associated with the cow being milked from a user. The user interface (238) is selected from the group consisting of, but is not limited to, a touch pad and a touch screen. The user can provide manual inputs associated with the cow via the user interface (238).
In an embodiment, the IoT enabled device (202) includes a sensing unit (212) and a first communication unit (214). The sensing unit (212), as shown in Figure 4, is configured to sense the plurality of parameters of the milk collected during milking of the cow by the milking machine (204), and is further configured to generate the sensed values corresponding to the sensed parameters.
In an embodiment, the sensing unit (212) senses the following parameters:
• real time pH;
• temperature; and
• Electrical conductivity (EC) of the milk.
Accordingly, the sensing unit (212) comprises a plurality of sensors selected from the group, but not limited to, a pH sensor, a temperature sensor and an electrical conductivity (EC) sensor.
The sensed values are passed through a de-noising unit (213) for the calibration and noise cancellation of the sensed values. Further, the de-noised values are transmitted to the server using the first communication unit (214)
The first communication unit (214) is configured to transmit the sensed values and the ID to the server (206).
The server (206) is configured to receive the sensed values from the IoT enabled device (202), and is further configured to detect mastitic milk based on the received sensed values.
In an embodiment, the server (206) includes a mastitis identifier (216), a notification generating unit (218) and a second communication unit (220).
The mastitis identifier (216) is configured to detect level of mastitis in the milk based on the received sensed values and is configured to generate an identification signal based on the detection.
In an embodiment, the mastitis identifier (216) includes a repository (222), a first crawler and extractor (224), a second crawler and extractor (226), a first comparator (228), a second comparator (230) and an analyser (232).
The repository (222) is configured to store:
• a first reference table having a list of the parameters and a threshold value corresponding to each of the parameters; and
• a second reference table having a list of the unique IDs, a breed of the cow corresponding each of the IDs, and a breed threshold value corresponding to each of the breeds corresponding to each of the IDs.
The first crawler and extractor (224) is configured to cooperate with the repository (222) configured to cooperate with the repository (222) to crawl through the first reference table to extract threshold values of the sensed parameters corresponding to the unique ID of the cow.
The second crawler and extractor (226) is configured to cooperate with the repository (222) to crawl through the second reference table to extract the breed threshold value corresponding to the ID.
The first comparator (228) is configured to cooperate with the first crawler and extractor (224) to compare each of the sensed values with the corresponding extracted threshold values to generate a first comparison result.
The second comparator (230) is configured to cooperate with the second crawler and extractor (226) to compare each of the sensed values with the corresponding extracted breed threshold values to generate a second comparison result.
The analyser (232) is configured to cooperate with the first comparator (228) and the second comparator (230) to analyse the first comparison result and the second comparison result to generate the identification signal.
In an embodiment, the analyser (232) includes a signal generator (234), wherein the signal generator (234) is configured to:
• generate the identification signal corresponding to a highest level mastitis risk, when the first comparison result and the second comparison result both are beyond the respective threshold values;
• generate the identification signal corresponding to a first level mastitis risk, when the first comparison result is within the corresponding threshold value and the second comparison result is not within the corresponding breed threshold value;
• generate the identification signal corresponding to a second level mastitis risk, when the first comparison result is beyond the corresponding threshold value but the second comparison result is within the corresponding breed threshold value; and
• no identification signal is generated, when the first comparison result and the second comparison result are within the corresponding threshold values.
The notification generating unit (218) is configured to cooperate with the mastitis identifier (216) to receive the identification signal, and is further configured to generate the notification based on the received identification signal. The notification generating unit (218) is configured to generate the notifications based on below conditions:
• on receiving the identification signal corresponding to the highest level mastitis risk, a high alert is generated;
• on receiving the identification signal corresponding to the first level mastitis risk, a first level alert is generated; and
• on receiving the identification signal corresponding to the second level mastitis risk, a second level alert is generated.
The second communication unit (220) is configured to cooperate with the notification generating unit (218) to receive and transmit the notification to the user device (208).
The application loaded in the user device (208) cooperates with the server (206) to receive at least one notification based on the detection of mastitic milk.
In an embodiment, the server (206) further comprises a learning unit (244) configured to receive the sensed values to:
• monitor and record changes in the sensed values and generate a monitoring result;
• provide different solutions to different breed of the cows based on the monitoring result using Artificial Intelligence and machine learning techniques; and
• perform self-learning and adaptive learning based on any inputs provided by the user and AI/ML techniques.
The mastitis identifier (216), the notification generating unit (218) and the learning unit (244) are implemented using one or more processor(s).
The processors may be general-purpose processors, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), and/or the like. The processors may be configured to retrieve data from and/or write data to a memory/repository. The memory/repository can be for example, a random access memory (RAM), a memory buffer, a hard drive, a database, an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), a read only memory (ROM), a flash memory, a hard disk, a floppy disk, cloud storage, and/or so forth.
Referring to Figure 5, the system (200) can collect samples of milk when offline, and detect mastitis once the vehicle is IoT enabled or having internet connectivity, which is displayed on a display screen (236). The IoT enabled device (202) further includes one or more visual indicators (240). The visual indicator (240) is configured to indicate:
• the status of the internet connectivity to the user;
• milking of the cow by the milking machine (204);
• charging of the indicators; and
• power on/off.
The visual indicator (240) can be a LED.

The different colors of the visual indicator indicate:
Green-Good milk
Yellow-Suspecting cattle might have mastitis
Red-Cattle affected by mastitis
Grey-Not enough samples size to predict.
The IoT enabled device (202) is equipped with a buzzer for various alarms to notify the customer.
• Long Beep: When the IoT enabled device (202) starts (or is restarted)
• Short Beep: Keypad tone
• Intermittent Beeping (every 5-10 Seconds): the IoT enabled device (202) is not connected to internet/wifi along with a blinking yellow led

In an embodiment, the IoT enabled device (202) is powered by a rechargeable battery (3.7 V). The IoT enabled device (202) can operate for 8 hours on a fully charged battery. The IoT enabled device (202) must be cleaned after every milking session daily which can be cleaned fitted along with the milking machine (204) by running water through a milk chamber (242). Care must be taken to not use hot water with temperature more than 60 degrees Celsius to prevent sensor damage.

Referring to Figures 7a, 7b, 7c and 7d, the graphs are plotted with experimental results. The graphs represent that the sample 3401 (Figure 7a) is of the cow producing mastitic milk as the majority of the sensed EC values are higher than the pre-determined threshold value of 6.5. Once the mastitic milk is determined, the sample of the milk is sent lab for confirmation.

The IoT enabled device (202) is portable and is driven from site to site and Food grade material – 316 stainless steel used for the milk chamber (242).

The foregoing description of the embodiments has been provided for purposes of illustration and not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.

TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of, an IoT enabled mastitic milk detection system and a method thereof which:
• provides dairy cattle’s health diagnosis and mastitis detection by AI/ML techniques;
• increases accuracy to predict and detect mastitis;
• can be well fitted with any milking machine; and
• performs easy monitoring of the dairy cattle health.
The foregoing disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, or group of elements, but not the exclusion of any other element, or group of elements.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
,CLAIMS:WE CLAIM:
1. A method (100) for detecting mastitic milk real-time during milking of a cow, said method (100) comprising the following steps:
• receiving (102) inputs, parameters of the cow from which milk is to be drawn, and environmental conditions;
• selecting (104) a reference table from a plurality of reference indexed tables stored in a repository wherein each of said reference table is indexed according to a specific breed of a cow, its age environmental conditions and contains threshold values corresponding to Electrical conductivity (EC), PH and temperature which designate mastitic milk;
• extracting (106) corresponding threshold values from said selected reference table;
• sensing (108) EC value of said milk in a steady state;
• sensing (110) PH value of said milk in a steady state;
• sensing (112) temperature of said milk in a steady state;
• comparing (114) said sensed EC value with said threshold value in said selected reference table to check if the sensed EC value is greater than said threshold value; and
• if said sensed EC value exceeds said referenced value, comparing (116) said PH and the temperature sensed values with said respective threshold values to check if said sensed PH and temperature value is greater than said PH and temperature threshold value to confirm that said milk is mastitic.

2. The method (100) as claimed in claim 1, wherein the step of receiving (102) inputs, parameters of cow from which milk is been drawn, and environmental conditions includes:
• receiving a cow identifier; and
• breed of said cow.

3. A system (200) for detecting mastitic milk real-time during milking of a cow, said system (200) comprising:
• an IoT enabled device (202), detachably attached to a milking machine (204), said device (202) configured to receive a unique identifier (ID) associated with said cow being milked, and further configured to sense a plurality of parameters associated with said milk of said cow collected in said milking machine (204) and generate corresponding sensed values;
• a server (206) communicatively coupled to said IoT enabled device (202) to receive said sensed values from said IoT enabled device (202), and further configured to detect mastitic milk based on said received sensed values; and
• an application loaded in a user device (208), said application configured to cooperate with said server (206) to receive at least one notification based on said detection of mastitic milk.

4. The system (200) as claimed in claim 3, wherein an RFID tag is provided on each of said cows, each of said RFID tags configured to store said unique ID associated with said corresponding cow.
5. The system (200) as claimed in claim 3, wherein said IoT enabled device (202) includes an RFID reader (210) for reading said RFID tag provided on said cow being milked for receiving said ID associated with said cow.
6. The system (200) as claimed in claim 3, wherein said IoT enabled device (202) includes a user interface (238) for receiving said cow identifier.
7. The system (200) as claimed in claim 3, wherein said IoT enabled device (202) includes:
• a sensing unit (212) configured to sense said plurality of parameters of said milk collected during milking by said milking machine (204), and further configured to generate said sensed values corresponding to said parameters; and
• a first communication unit (214) configured to transmit said sensed signals and said ID to said server (206).
8. The system (200) as claimed in claim 3, wherein said server (206) includes:
• a mastitis identifier (216) configured to detect level of mastitis in said milk based on said received sensed values, said mastitis identifier (216) configured to generate an identification signal based on said detection;
• a notification generating unit (218) configured to cooperate with said mastitis identifier (216) to receive said identification signal, and further configured to generate said notification based on said received identification signal; and
• a second communication unit (220) configured to cooperate with said notification generating unit (218) to receive and transmit said notification to said user device (208),
wherein said mastitis identifier (216) and said notification generating unit (218) are implemented using one or more processor(s).

9. The system (200) as claimed in claim 8, wherein said mastitis identifier (216) includes:
• a repository (222) configured to store a first reference table having a list of said parameters and a threshold value corresponding to each of said parameters, and a second reference table having a list of said unique IDs, a breed of said cow corresponding each of said IDs, and a breed threshold value corresponding to each of said breeds corresponding to each of said IDs;
• a first crawler and extractor (224) configured to cooperate with said repository (222) to crawl through said first reference table to extract threshold values of said sensed parameters corresponding to said unique ID of said cow;
• a second crawler and extractor (226) configured to cooperate with said repository (222) to crawl through said reference table to extract said breed threshold value corresponding to said unique ID;
• a first comparator (228) configured to cooperate with said first crawler and extractor (224) to compare each of said sensed values with said corresponding extracted threshold values to generate a first comparison result;
• a second comparator (230) configured to cooperate with said second crawler and extractor (226) to compare each of said sensed values with said extracted corresponding breed threshold values to generate a second comparison result; and
• an analyser (232) configured to cooperate with said first comparator (228) and said second comparator (230) to analyse said first comparison result and said second comparison result to generate said identification signal.

10. The system (200) as claimed in claim 9, wherein said analyser (232) includes a signal generator (234), wherein said signal generator (234) is configured to generate said identification signal under below conditions:
• when said first comparison result and said second comparison result both are beyond said respective threshold values, said identification signal corresponding to a highest level mastitis risk is generated;
• when said first comparison result is within said corresponding threshold value and said second comparison result is not within said corresponding breed threshold value, said identification signal corresponding to a first level mastitis risk is generated;
• when said first comparison result is beyond said corresponding threshold value but said second comparison result is within said corresponding breed threshold value, said identification signal corresponding to a second level mastitis risk is generated; and
• when said first comparison result and said second comparison result are within said corresponding threshold values, no signal is generated.
11. The system (200) as claimed in claim 3, wherein said server (206) further includes a learning unit (244) configured to receive said sensed values to:
• monitor and record changes in said sensed values and generate a monitoring result;
• provide different solutions to different breed of the cows based on said monitoring result using Artificial Intelligence and machine learning techniques; and
• perform self-learning and adaptive learning based on any inputs provided by the user and AI/ML techniques.