WO2023166474A1

WO2023166474A1 - System and method for identifying deficiencies in nutrition value of feed mix for target animal

Info

Publication number: WO2023166474A1
Application number: PCT/IB2023/051976
Authority: WO
Inventors: Kumar Ranjan
Original assignee: Agrigators Enterprises Private Limited
Priority date: 2022-03-04
Filing date: 2023-03-03
Publication date: 2023-09-07

Abstract

A system for identifying deficiencies in a nutrition value of a feed mix for an animal includes a memory including a nutrition profile database of nutritional requirements for the animal and the localised nutrition database of the localised nutritional values of each ingredient grown in locations.

Description

SYSTEM AND METHOD FOR IDENTIFYING DEFICIENCIES IN NUTRITION VALUE OF FEED MIX FOR TARGET ANIMAL

TECHNICAL FIELD

[0001] The present disclosure relates generally to the field of animal husbandry; and more specifically, to a localised ration balancing system for identifying deficiencies in a nutrition value of a feed mix for a target animal and a method for identifying deficiencies in a nutrition value of the given feed mix for the target animal.

BACKGROUND

[0002] Livestock production is an essential part of the food and economic security of India, as it provides a source of nutrition for humans and contributes to the livelihoods of millions of people. However, poor nutrition for livestock remains a major constraint on the animal husbandry industry, leading to low productivity and hindering the growth of the livestock sector. The demand for livestock products is increasing rapidly, particularly in developing countries like India, where economic growth and demographic changes have resulted in changing patterns of food consumption. Despite this, there is a lack of effective solutions to address the issue of nutritional scarcity in livestock.

[0003] Existing solutions have not been able to fully address the problem of poor nutrition, and there is a pressing need for a novel and innovative approach to promote animal health and productivity. Therefore, there is an exigency to devise a new solution that can provide a nutritious diet for livestock, stimulate the rural economy, promote agriculture and animal production, and mitigate the deficiencies associated with the entire value chain of the ecosystem.

[0004] Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with the conventional systems or equipment having an agricultural application, and method of operation of such conventional systems or equipment. SUMMARY

[0005] The present disclosure provides a system (i.e., a localised ration balancing system) for identifying deficiencies in a nutrition value of a given feed mix for a target animal and a method for identifying deficiencies in a nutrition value of the given feed mix for the target animal. The present disclosure provides a solution to the existing problem of identifying and addressing nutritional deficiencies in feed mixes for target animals based on the specific location of production and nutritional requirements of the animals. An aim of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in prior art and provide an improved system that can suggest one or more recommended feed mix having a correct nutritional value. This, in turn, can lead to healthier and more productive livestock. There is further provided an improved method for suggesting the one or more recommended feed mix having the correct nutritional value.

[0006] One or more objectives of the present disclosure is achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.

[0007] In one aspect, the present disclosure provides a localized ration balancing system for identifying deficiencies in a nutrition value of a given feed mix for a target animal. The system includes a memory including a nutrition profile database of nutritional requirements for the target animal and the localised nutrition database of the localised nutritional values of each ingredient grown in the different locations. The system further includes a hardware controller configured to receive data associated with each ingredient and a quantity of each ingredient in the given feed mix, and a location of production of the given feed mix from a user. The hardware controller is further configured to retrieve a nutritional value of each ingredient in the given feed mix from the localised nutrition database, based on the location of production of the given feed mix. The hardware controller is further configured to compare the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix. The hardware controller is further configured to identify one or more deficiencies in the nutritional value of the given feed mix specific to the location of production of the given feed mix and the target animal. The hardware controller is further configured to generate and control display of nutritional deficiency cum recommended feed mix output including a list of the one or more deficiencies in the nutritional value of the given feed mix and a recommended feed mix specific to the location of production of the given feed mix and the target animal.

[0008] The system of the present disclosure addresses the problem of identifying deficiencies in the nutritional value of a given feed mix for a target animal. This is important because providing a balanced diet to livestock is critical for their health and productivity. Without a balanced diet, animals may experience health issues, reduced productivity, and lower quality of products like milk or meat. The system utilizes the nutrition profile database and the localized nutrition database to provide a more accurate assessment of the nutritional value of each ingredient in the given feed mix. This means that the system takes into account not only the nutritional requirements of the target animal but also the local nutrition values of each ingredient based on the location of production. This ensures that the recommended feed mix is tailored to the specific needs of the target animal and the local production environment.

[0009] Furthermore, the disclosed system is user-friendly as it involves a hardware controller that receives data associated with each ingredient and a quantity of each ingredient in the given feed mix, and a location of production of the given feed mix from the user. The hardware controller generates a nutritional deficiency cum recommended feed mix output that includes a list of the deficiencies in the nutritional value of the given feed mix and a recommended feed mix specific to the location of production of the given feed mix and the target animal. This allows users to easily identify and address nutritional deficiencies in the feed mix and adjust the feed mix accordingly, leading to healthier and more productive livestock.

[0010] In an implementation form, the hardware controller is further configured to form the localised nutrition database of the localised nutritional values of each ingredient grown in the different locations.

[0011] By forming the localized nutrition database of the nutritional values of each ingredient grown in different locations, the system can provide more precise and accurate recommendations for the feed mix based on the location of production of the given feed mix. This can lead to a more efficient use of resources and better health and productivity for the target animal.

[0012] In a further implementation form, in order to form the localised nutrition database, the hardware controller is further configured to form one or more nested databases. Each nested database of the localised nutrition database includes a nutritional value of each ingredient grown in a specific location.

[0013] In such implementation form, the nested databases allows for a more efficient and organized way of storing and retrieving nutritional data for each specific location. By creating nested databases, the localised nutrition database may be easily expanded to include new locations and updated nutritional values without affecting the rest of the database. This makes it easier to maintain and update the system overtime.

[0014] In a further implementation form, the generated output further includes one or more supplements to be added in the given feed mix to eliminate the one or more deficiencies in the nutritional value of the given feed mix.

[0015] The system not only identifies deficiencies in the nutritional value of the given feed mix but also provides a solution in the form of one or more supplements to be added to eliminate the deficiencies. This can lead to a more precise and efficient way of addressing nutritional deficiencies in livestock, ultimately improving their health and productivity.

[0016] In a further implementation form, in order to compare the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix, the hardware controller is further configured to estimate an identification ratio (IR) for each ingredient in the given feed mix based on the nutritional value of each ingredient grown in a specific location and the quantity of the ingredient in the feed mix, and utilize the IR for each ingredient in the given feed mix to compare the nutritional content of each ingredient in the given feed mix to the nutritional values of the same ingredients in a reference location. [0017] The IR allows for a more accurate comparison of the nutritional content of each ingredient in the given feed mix in a reference location to the nutritional requirement of the target animal. This enhances the precision and reliability of the system in identifying deficiencies and recommending the appropriate supplements for the given feed mix.

[0018] In a further implementation form, the identification ratio (IR) for each ingredient is defined by a ratio of the nutritional value to quantity of each ingredient in the given feed mix. The IR provides a measure of the nutritional content of each ingredient in the given feed mix.

[0019] In a further implementation form, the hardware controller is further configured to train a machine learning model for updating the localised nutrition database and providing the output based on one or more parameters. In order to train the machine learning model, the hardware controller is further configured to incorporate machine learning algorithms that analyse historical data from the nutrition profile database and the localised nutrition database and adjust the retrieved nutritional values of each ingredient based on one or more parameters, in order to continuously improve an accuracy of the generated output.

[0020] The machine learning model enables the system to continuously improve its accuracy in identifying nutritional deficiencies and recommending the feed mix with a correct nutrition for the target animal. By incorporating machine learning algorithms, the system may analyse historical data and adjust the retrieved nutritional values based on various parameters, such as location, time, and animal breed, resulting in more accurate and personalized recommendations.

[0021] In another aspect, the present disclosure provides a method for identifying deficiencies in a nutrition value of a given feed mix for a target animal. The method includes receiving, by a hardware controller, data associated with each ingredient and a quantity of each ingredient in the given feed mix, and a location of production of the given feed mix from a user. The method further includes retrieving, by the hardware controller, a nutritional value of each ingredient in the given feed mix from a localised nutrition database, based on the location of production of the given feed mix. The localised nutrition database includes localised nutritional values of each ingredient grown in different locations. The method further includes comparing, by the hardware controller, nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix. The nutritional requirements of the target animal are stored in the nutrition profde database. The method further includes identifying, by the hardware controller, one or more deficiencies in the nutritional value of the given feed mix specific to the location of production of the given feed mix and the target animal. The method further includes generating and controlling, by the hardware controller, display of nutritional deficiency cum recommended feed mix output comprising a list of the one or more deficiencies in the nutritional value of the given feed mix and a recommended feed mix specific to the location of production of the given feed mix and the target animal.

[0022] In an implementation form, comparison of the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix includes estimating an identification ratio (IR) for each ingredient in the given feed mix based on the nutritional value of each ingredient grown in a specific location and the quantity of the ingredient in the feed mix, and utilizing the IR for each ingredient in the given feed mix to compare the nutritional content of each ingredient in the given feed mix to the nutritional values of the same ingredients in a reference location.

[0023] The method achieves all the advantages and technical effects of the system of the present disclosure.

[0024] It is to be appreciated that all the aforementioned implementation forms can be combined. All steps which are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity which performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements, or any kind of combination thereof. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims. [0025] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those skilled in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[0027] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

FIG. 1 is a block diagram illustrating a localized ration balancing system for identifying deficiencies in a nutritional value of a given feed mix for a target animal, in accordance with an embodiment of the present disclosure;

FIG. 2 is a diagram that illustrates an exemplary scenario of training of a machine learning (ML) model, in accordance with an embodiment of the present disclosure;

FIG. 3 depicts a flowchart for localised ration balancing and identifying the one or more deficiencies in the nutrition value of the given feed mix for the target animal, in accordance with an embodiment of the present disclosure; and

FIG. 4 is a flowchart of a method for identifying deficiencies in a nutrition value of a given feed mix for a target animal, in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing. DETAILED DESCRIPTION OF EMBODIMENTS

[0028] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.

[0029] As used throughout this disclosure, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to.

[0030] The phrases “at least one”, “one or more”, and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

[0031] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably.

[0032] The term “automatic” and variations thereof, as used herein, refers to any process or operation done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material”.

[0033] The present subject matter may have a variety of modifications and may be embodied in a variety of forms, and specific embodiments will be described in more detail with reference to the drawings. It should be understood, however, that the embodiments of the present subject matter are not intended to be limited to the specific forms, but include all modifications, equivalents, and alternatives falling within the spirit and scope of the present subject matter.

[0034] FIG. 1 is a block diagram illustrating a localized ration balancing system for identifying deficiencies in a nutritional value of a given feed mix for a target animal, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a block diagram that includes a localised ration balancing system 100 (herein after referred to as the system 100). The system 100 includes a hardware controller 104 and a memory 106. The hardware controller 104 is communicatively coupled with the memory 106. The memory 106 includes a nutrition profile database 108 of nutritional requirements for the target animal and a localised nutrition database 110 of the localised nutritional values of each ingredient grown in different locations. The system 100 may be used to identify one or more deficiencies in a nutritional value of the given feed mix for the target animal. In some examples, the system 100 may be used for cattle farming. However, the system 100 of the present disclosure may be used for any farming scenario, without any limitations. Some other farming scenarios may include, but limited to, poultry farming and aqua farming.

[0035] In an implementation, the hardware controller 104 and the memory 106 may be implemented on a same server, such as a server 102. In some implementations, the nutrition profile database 108 and the localised nutrition database 110 may be stored in the same server, such as the server 102, as shown in FIG. 1. In some other implementations, the nutrition profile database 108 and the localised nutrition database 110 may be stored outside the server 102. For example, the nutrition profile database 108 and the localised nutrition database 110 may be stored in an external storage device (not shown). The server 102 may be communicatively coupled to a plurality of user devices, such as a user device 114, via a communication network 112. A software or an application may be installed in the user device 114. The user device 114 includes a user interface 116. The system 100 further a machine learning (ML) model 122 stored in the memory 106.

[0036] The present disclosure provides the system 100 for localized ration balancing for livestock, where the system 100 identifies the one or more deficiencies in a nutritional value of a given feed mix for a target animal and suggest a recommended feed mix with a proper nutritional value for the target animal. The term “target animal” refers to the specific species of animal that is being raised or managed for a particular purpose, such as for meat, milk, eggs, or wool production. The term “given feed mix" refers to a specific combination of feed ingredients used to provide nutrition to the target animal population. Localized ration balancing refers to a process of formulating a feed ration for the livestock that meets nutritional requirements of the livestock based on factors specific to a local environment and production conditions. For performing the localised ration balancing, the quality and availability of feed ingredients, as well as the nutritional needs of the target animal population are considered. By tailoring the ration to the specific conditions of a specific location, the localized ration balancing may help improve livestock health and productivity while reducing costs and minimizing waste.

[0037] The server 102 includes suitable logic, circuitry, interfaces, and code that may be configured to communicate with the user device 114 via the communication network 112. In an implementation, the server 102 may be a master server or a master machine that is a part of a data center that controls an array of other cloud servers communicatively coupled to it for load balancing, running customized applications, and efficient data management. Examples of the server 102 may include, but are not limited to a cloud server, an application server, a data server, or an electronic data processing device.

[0038] The hardware controller 104 refers to a computational element that is operable to respond to and processes instructions that drive the system 100. The hardware controller 104 may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices, and elements are arranged in various architectures for responding to and processing the instructions that drive the system 100. In some implementations, the hardware controller 104 may be an independent unit and may be located outside the server 102 of the system 100. Examples of the hardware controller 104 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry.

[0039] The memory 106 refers to a volatile or persistent medium, such as an electrical circuit, magnetic disk, virtual memory, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory 106 is a non-volatile mass storage, such as a physical storage media. Furthermore, a single memory may encompass and, in a scenario, and the system 100 is distributed, the hardware controller 104, the memory 106 and/or storage capability may be distributed as well. Examples of implementation of the memory 106 may include, but are not limited to, an Electrically Erasable Programmable Read-Only Memory (EEPROM), Dynamic Random-Access Memory (DRAM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), and/or CPU cache memory.

[0040] The nutrition profile database 108 refers to a database that contains the nutritional requirements for different target animals. The nutrition profile database 108 may include information such as the required levels of protein, fat, carbohydrates, vitamins, and minerals needed by the animal to maintain good health and productivity. The nutritional requirements of different target animal species may vary widely, depending on their age, gender, reproductive status, growth rate, activity level, and other factors.

[0041] The localised nutrition database 110 refers to a database that contains information on the nutritional content and quality of feed ingredients grown in different locations. The localised nutrition database 110 may include variations in the nutritional value of the same feed ingredient due to differences in soil quality, climate, and other factors specific to the location where the ingredient is grown.

[0042] The communication network 112 includes a medium (e.g., a communication channel) through which the user device 114 communicates with the server 102. The communication network 112 may be a wired or wireless communication network. Examples of the communication network 112 may include, but are not limited to, Internet, a Local Area Network (LAN), a wireless personal area network (WPAN), a Wireless Local Area Network (WLAN), a wireless wide area network (WWAN), a cloud network, a Long- Term Evolution (LTE) network, a plain old telephone service (POTS), a Metropolitan Area Network (MAN), and/or the Internet.

[0043] The user device 114 refers to an electronic computing device operated by a user. The user device 114 may be configured to obtain a user input 118 in the software or the application rendered over the user interface 116 and communicate the user input 118 to the server 102. The server 102 may then be configured to generate a nutritional deficiency cum recommended feed mix output 120 (herein after referred to as the output 120). Examples of the user device 114 may include but not limited to a mobile device, a smartphone, a desktop computer, a laptop computer, a Chromebook, a tablet computer, a robotic device, or other user devices. Examples of the user interface 116 may include a keyboard or a touch screen.

[0044] The ML model 122 refers to a mathematical algorithm that is trained on a dataset to make predictions or decisions based on new data. The ML model 122 is used to predict the nutritional value of the feed mix based on the combination and quantity of ingredients used. The ML model 122 may also be used to predict the impact of a change in location of production of the feed mix and the one or more parameters such as weather pattern, soil quality, and farming method. The ML model 122 may also be used to predict the impact of adding or removing specific ingredients on the overall nutritional value of the given feed mix.

[0045] In operation, the hardware controller 104 is configured to receive the user input 120 associated with each ingredient and a quantity of each ingredient in the given feed mix, and a location of production of the given feed mix from a user. Specifically, the user input 120 includes a name and the quantity of each ingredient in the given feed mix, entered through the user interface 116 such as a keyboard or touch screen. The user input further includes the location of production of the given feed mix, selected from a list of available options or entered as a text input. Examples of the user input 120 that the hardware controller 104 may receive: com, soybean meal, wheat bran, and maize silage. Further, in this example, the user input 120 may include quantities of each ingredient in an example feed mix, such as 50kg com, 20kg soybean meal, 10kg wheat bran, and 100kg maize silage. In addition, the hardware controller 104 may receive location of production, for example, Punjab, Haryana, and Uttar Pradesh. In some implementations, the hardware controller 104 may also receive one or more parameters, such as weather patterns, soil quality, and farming practice. The one or more parameters may affect the nutritional value of each ingredient in the given feed mix. In some examples, the user input may include any additional animal related parameters or constraints that may affect the nutritional requirements or availability of feed ingredients, such as the age or breed of the target animal, or the season or weather conditions in the location of production. In some implementations, the user may be anyone who is involved in a process of preparing the feed mix, such as a livestock farmer, a nutritionist, or a feed mill operator. In some implementations, the user may also input problem or difficulty faced by the user while using the given feed mix.

[0046] The hardware controller 104 is further configured to retrieve a nutritional value of each ingredient in the given feed mix from the localised nutrition database 110, based on the location of production of the given feed mix. Nutritional value of each ingredient grown in one location may differ from that grown in other locations due to differences in soil composition, climate, and other factors. Therefore, the localised nutrition database 110 may have information on the nutritional values of each ingredient grown in the specific location as well as other locations. In an example, the hardware controller 104 retrieves the nutritional value of each ingredient of an example feed mix, that are com, soybean meal, wheat bran, and maize silage, from the localised nutrition database 110 based on the location of production of the ingredients of the example feed mix.

[0047] The hardware controller 104 is further configured to compare the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix. In some implementations, the nutritional requirements of the target animal may be retrieved from the nutrition profile database 108. In some implementations, in order to compare the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix, the hardware controller 104 is further configured to estimate an identification ratio (IR) for each ingredient in the given feed mix based on the nutritional value of each ingredient grown in a specific location and the quantity of the ingredient in the feed mix. In some implementations, the IR for each ingredient is defined by a ratio of the nutritional value to quantity of each ingredient in the given feed mix. In order to compare the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix, the hardware controller 104 is further configured to compare the nutritional value of each ingredient in the given feed mix in the specific location to the nutritional requirement of the target animal based on the IR for each ingredient in the given feed mix in the specific location. In an example, one of the ingredients of the given feed mix is com. For a specific location and given climate conditions, 100 grams of com contains approximately 18.7 grams of carbohydrates, 2.4 grams of protein, 2.7 grams of fiber, and 0.9 grams of fat. Assuming that the quantity of com in the feed mix is 500 grams, the nutritional value of 500 grams of com is 18.7 x 5 = 93.5 grams of carbohydrates, 2.4 x 5 = 12 grams of protein, 2.7 x 5 = 13.5 grams of fiber, and 0.9 x 5 = 4.5 grams of fat. Therefore, the IR for com in this case would be: 93.5/500 = 0.187 for carbohydrates, 12/500 = 0.024 for protein, 13.5/500 = 0.027 for fiber, and 4.5/500 = 0.009 for fat. Further, percentage composition of nutritional value of each ingredient in the given feed mix is calculated from the IR. In this example, nutritional value (in %) of com is 18.7% carbohydrates, 2.4% protein, 2.7% fiber, and 0.9% fat. In another example, the target animal may a lactating dairy cow that may have the nutritional requirement in its diet of about 50-60% energy, 15-20% protein, and 20- 30% fiber, and as well as appropriate levels of essential minerals and vitamins.

[0048] The hardware controller 104 is further configured to identify one or more deficiencies in the nutritional value of the given feed mix specific to the location of production of the given feed mix and the target animal. Based on comparison of the nutritional requirements of the target animal from the nutrition profile database 108 with the nutritional values of each ingredient in the given feed mix from the localised nutrition database 110, the hardware controller 104 identifies the one or more deficiencies in the nutritional value of the given feed mix specific to the location of production of the given feed mix and the target animal. For example, if fiber requirement for a lactating dairy cow is about 20-30% and the IR of an ingredient in the given feed mix, such as com, is 0.027 for fiber, the hardware controller 104 identifies that the lactating dairy cow requires about 20-30% fiber in the feed mix. However, only 2.7% fiber is provided in the diet of the lactating dairy cow. Therefore, there is a fiber deficiency in the given feed mix. Similarly, other deficiencies may be identified in the given feed mix. [0049] The hardware controller 104 is further configured to generate and control display of the nutritional deficiency cum recommended feed mix output 120 (interchangeably referred to as the output 120) including a list of the one or more deficiencies in the nutritional value of the given feed mix and a recommended feed mix specific to the location of production of the given feed mix and the target animal. In some examples, the recommended feed mix refers to a list of ingredients and their corresponding quantities that are recommended to address the identified nutritional deficiencies in the given feed mix, taking into account the nutritional requirements of the target animal population and the availability of locally sourced feed ingredients. In some implementations, the generated output 120 further includes one or more supplements to be added in the given feed mix to eliminate the one or more deficiencies in the nutritional value of the given feed mix. In some implementations, the generated output 120 further includes several processes and the supplements to solve the problem provided by the user.

[0050] In some examples, the hardware controller 104 may be configured to provide an option on the user device 114 to enquire about ingredients of the recommended feed mixes. In other words, the user gets an option on the user device 114 to enquire about ingredients of the recommended feed mixes. Moreover, the hardware controller 104 may be configured to provide contact details of a dedicated personnel to enquire about ingredients of the recommended feed mixes in real time. In other words, the user may also enquire about the nutritional feed with dedicated personnel available at the back end in real time.

[0051] In accordance with an embodiment, the hardware controller 104 is further configured to form the localised nutrition database 110 of the localised nutritional values of each ingredient grown in the different locations. In order to form the localised nutrition database 110, the hardware controller 104 is further configured to form one or more nested databases. Each nested database includes a nutritional value of each ingredient grown in a specific location.

[0052] In accordance with an embodiment, the hardware controller 104 is further configured to train the ML model 122 for updating the localised nutrition database 110 and providing the output 120 based on the one or more parameters. In order to train the ML model 122, the hardware controller 104 is further configured to incorporate ML algorithms that analyse historical data from the localised nutrition database 110. The hardware controller 104 is further configured to adjust the retrieved nutritional values of each ingredient based on the one or more parameters, in order to continuously improve an accuracy of the generated output 120.

[0053] FIG. 2 is a diagram that illustrates an exemplary scenario of training of a machine learning (ML) model, in accordance with an embodiment of the present disclosure. FIG. 2 is described in conjunction with elements from FIG. 1. With reference to FIG. 2, there is shown an exemplary scenario 200 that illustrates the training of the ML model 122 (of FIG. 1). With reference to FIG. 2, there is shown a diagram that includes a series of operations from 202 to 210 to train the ML model 122 (of FIG. 1). The hardware controller 104 (of FIG. 1) are configured to execute the operations shown in the diagram.

[0054] During the training phase, the ML model 122 is trained using thousands of different raw materials, including ingredients used in the feed mix from various locations, weather conditions (such as winter, summer, and rain), different soil types (such as red soil and black soil), different growth stages (such as two-day and three-day com plants), and different farming styles.

[0055] At operation 202, the hardware controller 104 is configured to access the historical data from the localised nutrition database 110. In some implementations, the hardware controller 104 may further receive raw materials that are used in the feed mix from the user. Specifically, the hardware controller 104 receives both historical data and new raw material data from various sources such as the user or the localised nutrition database 110. The historical data contains information about the nutritional content of various ingredients that have been previously used in the feed mix, while the new raw material data contains information about the nutritional content of any new ingredients that are being added.

[0056] At operation 204, the hardware controller 104 is further configured to receive the one or more parameters from the user, such as, weather conditions (such as winter, summer, and rain), different soil types (such as red soil and black soil), different growth stages (such as two-day and three-day com plants), and different farming styles. As discussed above, the hardware controller 104 is further configured to receive the location of production of the ingredients from the user. The location of production of the feed mix and the one or more parameters may impact the nutritional value of the ingredients of the feed mix.

[0057] At operation 206, the hardware controller 104 is further configured to apply machine learning algorithms to analyze the historical and new raw material data and. The machine learning algorithms may use various techniques such as regression, clustering, and classification to analyze the data and provide more accurate nutritional values for each ingredient.

[0058] At operation 208, the hardware controller 104 is further configured to update the retrieved nutritional values of each ingredient from the localised nutrition database 110 based on the one or more parameters provided by the user. Updated data i.e., the updated nutritional values of each ingredient are then stored in the localised nutrition database 110 to continuously improve the accuracy of the generated output that may be generated using the trained ML model. Finally, at operation 210, the trained ML model 122 is obtained, which is used by the hardware controller 104 to predict the updated nutritional values of the feed mix based on the combination and quantity of ingredients used, the impact of a change in location of production of the feed mix and the one or more parameters such as weather pattern, soil quality, and farming method.

[0059] FIG. 3 depicts a flowchart for localised ration balancing and identifying the one or more deficiencies in the nutrition value of the given feed mix for the target animal, in accordance with an embodiment of the present disclosure. FIG. 3 is described in conjunction with elements from FIGs. 1 and 2. With reference to FIG. 3, there is shown a flowchart 300 that includes a series of operations from 302-to-316. The hardware controller 104 (of FIG. 1) is configured to execute the flowchart 300.

[0060] At operation 302, on receiving instruction from the user, the hardware controller 104 creates an account on the software or the mobile application (hereinafter referred to as the application) using the user device 114 communicatively coupled with the hardware controller 104 and the user may log into the account for further accessibility and manoeuvring leading to the required nutritional information. After that, at operation 304, the user navigates to a nutrition page on the application using the user interface 116 and on receiving instruction from the user, the hardware controller 104 creates a profile for animal(s) i.e., the target animal. Thereafter, at operation 306, the hardware controller 104 receives type information of the target animal(s) from the user. The type information of the target animal(s) received from the user at operation 306 may include, but not limited to, a category and species/breed of the target animal(s), a number of target animal(s) of particular species, and a group name of the target animal(s). Furthermore, at operation 308, the hardware controller 104 receives specific information of an individual target animal from the user. The specific information of the individual target animal received from the user may include, but age, weight, and other parameters of the target animal based on the type information of the target animal. For example, the other parameters of a cow may include mild yield, current amount of fat in milk, and the like. Thereafter, at operation 310, the hardware controller 104 receives an expected output from the target animal based on the type information of the target animal. For example, the expected output from the target animal may include increase in milk yield of a cow when the cow is the target animal. After that, at operation 312, the hardware controller 104 compares the nutritional value of the given feed mix provided by the ML model with the nutritional requirements for the target animal in order to identify the one or more deficiencies in the given feed mix and suggests better nutritional feed for the target animal. Furthermore, at operation 314, the hardware controller 104 generates a list of different types of recommended feed mixes having an optimal nutritional value according to the expected output from the target animal. At last, at operation 316, the user gets an option on the user device 114 to enquire about ingredients of the recommended feed mixes. Further, the user may also get a buylink on the user device 114 for buying the recommended feed mixes online.

[0061] FIG. 4 is a flowchart of a method for identifying deficiencies in a nutrition value of a given feed mix for a target animal, in accordance with an embodiment of the present disclosure. FIG. 4 is explained in conjunction with elements from FIGs. 1, 2 and 3. With reference FIG. 4, there is shown a flowchart of a method 400. The method 400 is executed at the server 102 (of Fig. 1). The method 400 may include steps 402 to 410.

[0062] At step 402, the method 400 includes receiving, by the hardware controller 104, the user input 120 associated with each ingredient and the quantity of each ingredient in the given feed mix, and the location of production of the given feed mix from the user. [0063] At step 404, the method 400 further includes retrieving, by the hardware controller 104, the nutritional value of each ingredient in the given feed mix from the localised nutrition database 110, based on the location of production of the given feed mix. The localised nutrition database 110 includes the localised nutritional values of each ingredient grown in the different locations.

[0064] At step 406, the method 400 further includes comparing, by the hardware controller 104, the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix. The nutritional requirements of the target animal are stored in the nutrition profile database 108. The comparison of the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix includes estimating the identification ratio (IR) for each ingredient in the given feed mix based on the nutritional value of each ingredient grown in the specific location and the quantity of the ingredient in the feed mix, and then comparing the nutritional value of each ingredient in the given feed mix in the specific location to the nutritional requirement of the target animal based on the IR for each ingredient in the given feed mix in the specific location.

[0065] At step 408, the method 400 further includes identifying, by the hardware controller 104, the one or more deficiencies in the nutritional value of the given feed mix specific to the location of production of the given feed mix and the target animal.

[0066] At step 410, the method 400 further includes generating and controlling, by the hardware controller 104, display of the nutritional deficiency cum recommended feed mix output 120 including the list of the one or more deficiencies in the nutritional value of the given feed mix and the recommended feed mix specific to the location of production of the given feed mix and the target animal.

[0067] The method 400 allows for a personalised and localised ration balancing of the target animal. By taking into account the specific location of production of the given feed mix and the nutritional values of each ingredient grown in that location, the method 400 provides a more tailored and effective feed mix recommendation for the target animal, thereby leading to better health and performance outcomes for the animal, as well as potentially cost savings for the owner. [0068] The steps 402 to 410 are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein. Various embodiments and variants disclosed with the aforementioned system (such as the system 102) apply mutatis mutandis to the aforementioned method 400.

[0069] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure.

Claims

1. A localised ration balancing system (100) for identifying deficiencies in a nutritional value of a given feed mix for a target animal, the system (100) comprising: a memory (106) comprising a nutrition profile database (108) of nutritional requirements for the target animal and a localised nutrition database (110) of the localised nutritional values of each ingredient grown in different locations; and a hardware controller (104) configured to: receive a user input (118) associated with each ingredient and a quantity of each ingredient in the given feed mix, and a location of production of the given feed mix from a user; retrieve a nutritional value of each ingredient in the given feed mix from the localised nutrition database (110), based on the location of production of the given feed mix; compare the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix; identify one or more deficiencies in the nutritional value of the given feed mix specific to the location of production of the given feed mix and the target animal; and generate and control display of nutritional deficiency cum recommended feed mix output (120) comprising a list of the one or more deficiencies in the nutritional value of the given feed mix and a recommended feed mix specific to the location of production of the given feed mix and the target animal.

2. The system (100) as claimed in claim 1, wherein the hardware controller (104) is further configured to form the localised nutrition database (110) of the localised nutritional values of each ingredient grown in the different locations.

3. The system (100) as claimed in claim 2, wherein, in order to form the localised nutrition database (110), the hardware controller (104) is further configured to form one or more nested databases, wherein each nested database comprises a nutritional value of each ingredient grown in a specific location. The system (100) as claimed in claim 1, wherein the generated output (120) further comprises one or more supplements to be added in the given feed mix to eliminate the one or more deficiencies in the nutritional value of the given feed mix. The system (100) as claimed in claim 1, wherein, in order to compare the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix, the hardware controller (104) is further configured to: estimate an identification ratio (IR) for each ingredient in the given feed mix based on the nutritional value of each ingredient grown in a specific location and the quantity of the ingredient in the feed mix; and compare the nutritional value of each ingredient in the given feed mix in the specific location to the nutritional requirement of the target animal based on the IR for each ingredient in the given feed mix in the specific location. The system (100) as claimed in claim 5, wherein the identification ratio (IR) for each ingredient is defined by a ratio of the nutritional value to quantity of each ingredient in the given feed mix. The system (100) as claimed in claim 1, wherein the hardware controller (104) is further configured to train a machine learning model (122) for updating the localised nutrition database (110) and providing the output (120) based on one or more parameters, and wherein, in order to train the machine learning model (122), the hardware controller (104) is further configured to: incorporate machine learning algorithms that analyse historical data from the localised nutrition database (110); and adjust the retrieved nutritional values of each ingredient based on the one or more parameters, in order to continuously improve an accuracy of the generated output (120). A method (400) for identifying deficiencies in a nutrition value of a given feed mix for a target animal, the method (400) comprising: receiving, by a hardware controller (104), a user input (118) associated with each ingredient and a quantity of each ingredient in the given feed mix, and a location of production of the given feed mix from a user; retrieving, by the hardware controller (104), a nutritional value of each ingredient in the given feed mix from a localised nutrition database (110), based on the location of production of the given feed mix, wherein the localised nutrition database (110) comprises localised nutritional values of each ingredient grown in different locations; comparing, by the hardware controller (104), nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix, wherein the nutritional requirements of the target animal are stored in the nutrition profile database (108); identifying, by the hardware controller (104), one or more deficiencies in the nutritional value of the given feed mix specific to the location of production of the given feed mix and the target animal; and generating and controlling, by the hardware controller (104), display of nutritional deficiency cum recommended feed mix output (120) comprising a list of the one or more deficiencies in the nutritional value of the given feed mix and a recommended feed mix specific to the location of production of the given feed mix and the target animal. The method (400) as claimed in claim 8, wherein the comparison of the nutritional requirements of the target animal with the retrieved nutritional values of each ingredient in the given feed mix comprises: estimating an identification ratio (IR) for each ingredient in the given feed mix based on the nutritional value of each ingredient grown in a specific location and the quantity of the ingredient in the feed mix; and comparing the nutritional value of each ingredient in the given feed mix in the specific location to the nutritional requirement of the target animal based on the IR for each ingredient in the given feed mix in the specific location. The method (400) as claimed in claim 9, wherein the identification ratio (IR) for each ingredient is defined by a ratio of the nutritional value to quantity of each ingredient in the given feed mix.