DESC:TECHNICAL FIELD
The present disclosure relates to a system and method for monitoring weight and growth of a chick in the poultry farm, and more particularly, the present invention relates to an IoT-based system and method for collecting and processing time-series data of the chick weights in a poultry farm.

BACKGROUND
In existing methods for estimating the weight of chickens encounter accuracy issues due to improper segmentation during weighing on platform scales. The inadequacies of these approaches, including inaccuracies stemming from flawed segmentation techniques and potential limitations of platform scales, collectively result in poor weight estimation. This deficiency adversely impacts the accuracy and reliability of weight measurements, subsequently affecting the quality of poultry management decisions.
Hence, there is a need to provide a system and method for monitoring daily weight gain of the chick which overcomes abovementioned drawbacks.
For the reasons stated above, which will become apparent to those skilled in the art upon reading and understanding the specification, there is a need in the art for a system and method remote sensing data, aiming to provide an accurate and efficient solution for monitoring chicks daily weight gain while addressing the shortcomings of conventional methods.

OBJECTS OF THE INVENTION
An objective of the present invention is to monitor daily weight gain of the chick using remote sensing data.
Another object of the present invention is to observe actual weight of weight gain of the chick as per standard weight chart.
Yet another object of the present invention is to adapt feeding routine for the chicks according to average weight gain/loss of the chick.

SUMMARY
This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in limiting the scope of the claimed subject matter.
In an implementation of the present disclosure a system 100 configured to daily monitor in real-time the weight gain by a chick or hatchling. The system further comprises at least one weighing scale 102 configured to measure weight of each of the chicks in the poultry farm, wherein the at least one weighing scale 102, is configured to generate a weight data 104 for each of the chick in real-time, and/or at defined intervals. Further a central server 103 configured to receive and process the weight data 104 at defined time intervals. The system further comprises a wireless transceiver unit 106 integrated with the weighing scale 102, enabling remote data transmission from the weighing scale 102 to the central server 103 via the data transfer module 105.
In another implementation a method 200 configured to daily monitor in real-time the weight gain by a chick or hatchling is disclosed. The method comprises processing the received weight data 104 at defined time intervals by the central server 103. Further cleaning the received weight data 104 by removing unwanted noise and outliers and addressing external factors affecting the weighing scale 102. Removing missing data first and applying a simple pre-processing method (SPM) to generate filtered weight time series data. Organizing the raw weight data and identifying a predefined minimum threshold weight value based on a standard weight chart. Further the method comprises achieving symmetrical weight distribution using a weight data symmetric distribution method (WDSDM) technique at predefined bins, accounting for potential variations due to multiple chicks being weighed simultaneously.
In another implementation a method 300 configured to daily monitor in real-time the weight gain by a chick or hatchling is disclosed. Receiving 302 a plurality of data from at least one weighing scale 102. Arranging 304, the received weight data 104 in time series, i.e., in time sequence to obtained time series data. Applying 310, a first threshold value to the clean time series data, to obtain segregated data. Further computing 312, the segregated data arranged in time series. The method further comprises reading 402 the computed segregated data arranged in time series.

BRIEF DESCRIPTION OF DRAWINGS
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
Figure 1, illustrates a system in accordance with an exemplary embodiment of the present disclosure;
Figure 2, is a flowchart of a method for computing the average weight of chicks generated for a day, in accordance with an exemplary embodiment of the present disclosure;
Figure 3, illustrates a flowchart of a method for removing garbage data using SPM in accordance with the exemplary embodiment of the present disclosure;
Figure 4, illustrates a flowchart of a method for weight data symmetric distribution (WDSDM) in accordance with the exemplary embodiment of the present disclosure.
It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present invention. Similarly, it will be appreciated that any flowcharts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION
Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
References in the specification to “one embodiment” or “an embodiment” mean that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
Hereinafter, embodiments will be described in detail. For clarity of the description, known constructions and functions will be omitted.
Parts of the description may be presented in terms of operations performed by at least one processor, electrical / electronic circuit, a computer system, using terms such as data, state, link, fault, packet, and the like, consistent with the manner commonly employed by those skilled in the art to convey the substance of their work to others skilled in the art. As is well understood by those skilled in the art, these quantities take the form of data stored/transferred in the form of non-transitory, computer-readable electrical, magnetic, or optical signals capable of being stored, transferred, combined, and otherwise manipulated through mechanical and electrical components of the computer system; and the term computer system includes general purpose as well as special purpose data processing machines, switches, and the like, that are standalone, adjunct or embedded. For instance, some embodiments may be implemented by a processing system that executes program instructions so as to cause the processing system to perform operations involved in one or more of the methods described herein. The program instructions may be computer-readable code, such as compiled or non-compiled program logic and/or machine code, stored in a data storage that takes the form of a non-transitory computer-readable medium, such as a magnetic, optical, and/or flash data storage medium. Moreover, such processing system and/or data storage may be implemented using a single computer system or may be distributed across multiple computer systems (e.g., servers) that are communicatively linked through a network to allow the computer systems to operate in a coordinated manner.
In accordance with an embodiment of the present invention, a method and system for daily monitoring the weight gain of a chick is disclosed. The method and the system as disclosed is further configured change the feeding routine for the chicks to achieve weight gain as per standard weight chart.
According to an embodiment, the present invention provides a system and method for monitoring average weight of a chick in poultry farm per standard weight chart using remote sensing data.
In an implementation according to one of the embodiments of the present invention, the system monitors weight gain of the chicks in poultry farm on daily basis. The system comprises at least one wireless transceiver unit integrated with at least weighing scale. Specifically, the wireless transceiver unit enables remote data transmission from the weighing scale to a central server. The central server processes the received weight data at defined time intervals.
The system furthermore comprises a module for data cleaning. The module cleans the data received from the weighing scales. The data cleaning module ensures data accuracy by removing unwanted noise and outliers. The module for data cleaning also address a situation where the weighing scale might be affected by external factors such as dust or objects.
Specifically, the module for data cleaning removes the missing data first and apply the Simple Preprocessing Method (SPM) to generate the filtered weight time series data. This organizes the raw weight time series data and identifies a predefined minimum threshold weight value based on a Standard weight chart. Data points exceeding this threshold are validated using predetermined criteria. This meticulous approach leads to the generation of a filtered weight time series dataset, which eliminates garbage and outlier data.
The system further comprises a module for achieving symmetrical weight distribution using Weight Data Symmetric Distribution Method (WDSDM) technique at predefined bins. The module address scenarios where multiple chicks or birds might be placed on the same weighing scale simultaneously. The module uses WDSDM which is particularly designed to accommodate such situations, ensuring accurate weight assessment while accounting for potential variations due to the combined weight of multiple subjects.
In an implementation according to one of the embodiments of the present invention, a Weight Data Symmetric Distribution Method (WDSDM) commences by establishing a lower bound and an upper bound based on the Standard Weight Data for the first day. Weight measurements falling within the range of the lower and upper bounds are assigned to the first bin. This bin configuration caters to scenarios involving a single chick on the scale. The weight range for first bin is calculated as follows:
Bin first = Wi = (Lower Bound) and Wi = (Upper Bound) ? 1 = i= n
Similarly, second bin data is generated using
Bin second = Wi = (Upper Bound * i ) + 1 and Wi = (Upper Bound * (i+1)) ? 1 = i= n
In another aspect, the present invention provides a method for monitoring average weight of a chick in poultry farm per standard weight chart using remote sensing data.
At first step, the method comprises remote data transmission from the weighing scale within a poultry premises to a central server using a data transfer module. Specifically, the central server processes the received weight data at defined time intervals.
At second step, the method comprises cleaning the data receiving from the weighing scales of the poultry farm. Specifically, the data cleaning step ensures data accuracy by removing unwanted noise and outliers. The data cleaning step also address a situation where the weighing scale might be affected by external factors such as dust or objects.
Specifically, the data cleaning step removes missing data first and apply the Simple Preprocessing Method (SPM) to generate the filtered weight time series data. This organizes the raw weight time series data and identifies a predefined minimum threshold weight value based on a Standard weight chart. Data points exceeding this threshold are validated using predetermined criteria. This meticulous approach leads to the generation of a filtered weight time series dataset, which eliminates garbage and outlier data.
At third step, the method comprises achieving symmetrical weight distribution using Weight Data Symmetric Distribution Method (WDSDM) technique at predefined bins. This step address scenarios where multiple chicks or birds might be placed on the same weighing scale simultaneously. The module uses WDSDM which is particularly designed to accommodate such situations, ensuring accurate weight assessment while accounting for potential variations due to the combined weight of multiple subjects.
In an implementation according to one of the embodiments of the present invention, a Weight Data Symmetric Distribution Method (WDSDM) commences by establishing a lower bound and an upper bound based on the Standard Weight Data for the first day. Weight measurements falling within the range of the lower and upper bounds are assigned to the first bin. This bin configuration caters to scenarios involving a single chick on the scale.
Now referring to Figure 1 to Figure 4, illustrate a method and a system configured to daily monitor in real-time the weight gain by a chick or hatchling in a poultry farm. Further the method, is configured to change or adopt the feeding routine via a feeding system to enable optimal weight gain by the chick in accordance with a pre-defined set.
In an exemplary embodiment, the system 100 may include at least one weighing scale 102 configured to measure weight of each of the chicks in the poultry farm. The at least one weighing scale 102, is configured to generate a weight data 104 for each of the chick in real-time, and/or at defined intervals. A central server 103 may be provided in the system 100, and configured to receive and process the weight data 104 at defined time intervals. In accordance with an aspect the weight data 104, is remotely transmitted from the weighing scale 102 to the central server 103 using a data transfer module 105. The system 100 may also include a wireless transceiver unit 106 integrated with the weighing scale 102, enabling remote data transmission from the weighing scale 102 to the central server 103 via the data transfer module 105. Further the central server 103 may process the weight data 104 received at regular time intervals to monitor the weight gain of the chicks.
Further in accordance with the exemplary embodiment, the system 100 may also comprise a data cleaning module 107 configured to clean the received weight data by removing unwanted noise and outliers, and to address external factors affecting the weighing scale 102. The system 100 may further comprise a first module 108 for achieving symmetrical weight distribution using a Weight Data Symmetric Distribution Method (WDSDM) technique at predefined bins, accounting for potential variations due to multiple chicks being weighed simultaneously.
In an aspect the first module 108 is configured to establish a lower bound and an upper bound based on the Standard Weight Data for the first day. Further assigning weight measurements falling within the range of the lower and upper bounds to a first bin. Generating by the first module 108 a second bin by assigning weight measurements falling within a calculated range based on the upper bound.
In some embodiments, the data cleaning module 107 may remove missing data first and apply a Simple Preprocessing Method (SPM) to generate a filtered weight time series data. Further organizing the raw weight data and identifying a predefined minimum threshold weight value based on a Standard Weight Chart. The WDSDM technique establishes lower and upper bounds based on Standard Weight Data, assigning weight measurements within these bounds to respective bins. The data cleaning module 107 may be configured to validate data points exceeding a predefined minimum threshold using predetermined criteria.
In another exemplary embodiment, the method 200 may comprise processing the received weight data 104 at defined time intervals by the central server 103. Cleaning the received weight data 104 by removing unwanted noise and outliers and addressing external factors affecting the weighing scale 102. Removing missing data first and applying a simple pre-processing method (SPM) to generate filtered weight time series data, organizing the raw weight data and identifying a predefined minimum threshold weight value based on a standard weight chart.
Further achieving symmetrical weight distribution using a weight data symmetric distribution method (WDSDM) technique at predefined bins, accounting for potential variations due to multiple chicks being weighed simultaneously. The step of cleaning the received weight data further comprises validating data points exceeding the predefined minimum threshold using predetermined criteria.
The method 200 further comprises establishing a lower bound, and an upper bound based on the standard weight data for the first day or the first instance. Further assigning weight measurements falling within the range of the lower and upper bounds to a first bin. Generating a second bin by assigning weight measurements falling within a calculated range based on the upper bound. The data cleaning step is configured to address situations where the weighing scale may be affected by external factors such as dust or objects.
In accordance with another exemplary embodiment, a method 300 may comprise at step 302 receiving a plurality of data from at least one weighing scale 102. The plurality of data received from the weighing scale 102, includes the weight data 104. The weight data 104 comprises weight raw data associated with each of the chick in the poultry farm. Further at step 304, arranging the received weight data 104 in time series, i.e., in time sequence to obtained time series data. At step 306, cleaning the time series data to obtain a clean time series data, by removing unwanted noise and outliers and addressing external factors affecting the weighing scale 102 and further pre-processing using a data cleaning module 107 configured to deploy the simple preprocessing method.
In accordance with the exemplary aspect, at step 308, defining a first threshold value based on the clean time series data. At step 310, applying the first threshold value to the clean time series data, to obtain segregated data. The segregated data, comprises clean time series data having value greater than the threshold value. Further at step 312, computing the segregated data arranged in time series.
The method 300 as disclosed in the exemplary embodiment, further comprises, at step 402 reading the computed segregated data arranged in time series. At step 404, defining a number of days, for at least one cycle. The method as disclosed is configured to run for multiple cycles, and each cycle comprises a defined number of days as captured in step 404. Further at the step 404, mapping each data within the segregated data arranged in time series, and further arranging to obtain a standard weight data series. Further at step 406, defining a first threshold value, a lower bound value, and an upper bound value. The first threshold value may be associated with weight of the chick for a specific date selected from the standard weight data series. Further the lower bound value may be defined as a value about 20% less than the first threshold value, similarly the upper bound value may be defined as a value about 20% more than the first threshold value.
At step 408, applying a weight data symmetric distribution method (WDSDM) to the standard weight data series so as to distribute the weight data from the threshold weight symmetry. Further at step 410, defining a bin, wherein each bin is defined as the data set comprises a first value greater than lower bound and less than upper bound. Further fist value is weight data at a defined location within distributed weight data. At step 412, distributing the weight data distributed using WDSDM, within a plurality of bins. Further at step 414, generating a weight bin series data.
The foregoing objects of the invention are accomplished and the problems and shortcomings associated with prior art techniques and approaches are overcome by the present invention described in the present embodiment. Detailed descriptions of the preferred embodiment are provided herein; however, it is to be understood that the present invention may be embodied in various forms. Therefore, specific details disclosed herein are not to be interpreted as limiting, but rather as a basis for the claims and as a representative basis for teaching one skilled in the art to employ the present invention in virtually any appropriately detailed system, structure, or matter. The embodiments of the invention as described above and the methods disclosed herein will suggest further modification and alterations to those skilled in the art. Such further modifications and alterations may be made without departing from the scope of the invention.

CLAIMS:I/We claim:

1. A system 100 configured to daily monitor in real-time the weight gain by a chick or hatchling comprises:
at least one weighing scale 102 configured to measure weight of each of the chicks in the poultry farm, wherein the at least one weighing scale 102, is configured to generate a weight data 104 for each of the chick in real-time, and/or at defined intervals;
a central server 103 configured to receive and process the weight data 104 at defined time intervals; and
a wireless transceiver unit 106 integrated with the weighing scale 102, enabling remote data transmission from the weighing scale 102 to the central server 103 via the data transfer module 105.

2. The system as claimed in claim 1, comprises a data cleaning module 107 configured to clean the received weight data 104 by removing unwanted noise and outliers, and to address external factors affecting the weighing scale 102.

3. The system as claimed in claim 1 comprises a first module 108 for achieving symmetrical weight distribution using a Weight Data Symmetric Distribution Method (WDSDM) technique at predefined bins, accounting for potential variations due to multiple chicks being weighed simultaneously.

4. A method 200 configured to daily monitor in real-time the weight gain by a chick or hatchling comprising:
processing the received weight data 104 at defined time intervals by the central server 103;
cleaning the received weight data 104 by removing unwanted noise and outliers and addressing external factors affecting the weighing scale 102;
removing missing data first and applying a simple pre-processing method (SPM) to generate filtered weight time series data;
organizing the raw weight data and identifying a predefined minimum threshold weight value based on a standard weight chart; and
achieving symmetrical weight distribution using a weight data symmetric distribution method (WDSDM) technique at predefined bins, accounting for potential variations due to multiple chicks being weighed simultaneously.

5. The method as claimed in claim 4 wherein the cleaning the received weight data further comprises validating data points exceeding the predefined minimum threshold using predetermined criteria.

6. The method as claimed in claim 4 comprises establishing a lower bound, and an upper bound based on the standard weight data for the first day or the first instance.

7. The method as claimed in claim 4 comprises assigning weight measurements falling within the range of the lower and upper bounds to a first bin.

8. The method as claimed in claim 4 comprises generating a second bin by assigning weight measurements falling within a calculated range based on the upper bound.

9. A method 300 configured to daily monitor in real-time the weight gain by a chick or hatchling comprising:
receiving 302 a plurality of data from at least one weighing scale 102;
arranging 304, the received weight data 104 in time series, i.e., in time sequence to obtained time series data;
applying 310, a first threshold value to the clean time series data, to obtain segregated data;
computing 312, the segregated data arranged in time series; and
reading 402 the computed segregated data arranged in time series.

10. The method as claimed in claim 9, comprises cleaning 306, the time series data to obtain a clean time series data, by removing unwanted noise and outliers and addressing external factors affecting the weighing scale 102 and further pre-processing using a data cleaning module 107.

11. The method as claimed in claim 9, comprises defining 308, a first threshold value based on the clean time series data.

12. The method as claimed in claim 9, comprises defining 404, a number of days, for at least one cycle, further mapping each data within the segregated data arranged in time series, and further arranging to obtain a standard weight data series.

13. The method as claimed in claim 9, comprises defining 406, a first threshold value, a lower bound value, and an upper bound value.

14. The method as claimed in claim 9, comprises applying 408, a weight data symmetric distribution method (WDSDM) to the standard weight data series so as to distribute the weight data from the threshold weight symmetry.

15. The method as claimed in claim 9, comprises defining 410, a bin, wherein each bin is defined as the data set comprises a first value greater than lower bound and less than upper bound.

16. The method as claimed in claim 9, comprises distributing 412, the weight data distributed using WDSDM, within a plurality of bins.

17. The method as claimed in claim 9, comprises generating 414, a weight bin series data.

Dated this on 20th Day of AUGEST 2024