FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
AND
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR GENERATING A SET OF CROP RECOMMENDATIONS”
We, JIO PLATFORMS LIMITED, an Indian National, of address Office-101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad-380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which
it is to be performed.

METHOD AND SYSTEM FOR GENERATING A SET OF CROP RECOMMENDATIONS
PRIORITY APPLICATION
This application claims the benefit of Indian Patent Application no. 202321030801 filed on 28 April 2023 titled “METHOD AND SYSTEM FOR PROVIDING AGRICULTURAL RECOMMENDATIONS”. The entire contents of the aforementioned applications are incorporated herein by reference.
TECHNICAL FIELD
Embodiments of the present disclosure generally relate to providing crops related recommendations. More particularly, embodiments of the present disclosure relate to generating and providing a set of crop recommendations for an agricultural region based on top traditional crop(s) and/or top non-traditional crop(s) grown in one or more agricultural regions that are similar to the agricultural region.
BACKGROUND
The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
Agriculture is of great importance to society because it provides food, fuel, and fiber to sustain human life. The practice of agriculture has been a fundamental aspect of human civilization, allowing humans to settle in one place and develop complex societies. Agricultural practices have evolved over time, with

advancements in technology and science improving crop yields and livestock production. Agriculture also plays a significant role in the economy, providing jobs and income for millions of people worldwide. In addition, sustainable agricultural practices can have positive impacts on the environment, such as reducing greenhouse gas emissions and promoting biodiversity. Overall, agriculture is a critical component of society and is essential for the health and well-being of people and the planet. Further, the growth of agriculture is crucial for the economy of any nation. Agricultural products serve as raw materials for various industries, such as food processing, textiles, and construction, contributing to the growth of the economy. A thriving agricultural sector also creates job opportunities for millions of people, particularly in rural areas, reducing unemployment and poverty levels. Additionally, agriculture can serve as a source of foreign exchange earnings through the export of agricultural products. A country that invests in modern agricultural techniques and infrastructure can increase crop yields, lower food prices, and reduce the reliance on imports, leading to greater economic stability. Finally, agriculture is essential for food security, ensuring that a nation's citizens have access to adequate and nutritious food, which is critical for the overall health and productivity of the population. Therefore, the growth of agriculture is essential for sustainable economic development, poverty reduction, and food security.
Further, the introduction of technology in agriculture has revolutionized the industry and contributed significantly to its growth. Precision agriculture techniques, such as soil sensors, drones, and GPS-guided equipment, allow farmers to optimize crop yields, reduce waste, and increase efficiency. Advanced machinery, including tractors, combines, and irrigation systems, has also improved productivity and reduced labor requirements. Additionally, genetic engineering and biotechnology have led to the development of new crop varieties that are more resistant to pests, diseases, and drought, ensuring greater crop yields and reducing food insecurity. The use of technology in agriculture has also improved

data collection and analysis, leading to better decision-making and increased profitability. Finally, the adoption of sustainable agriculture practices, such as conservation tillage and integrated pest management, has reduced environmental degradation and ensured long-term sustainability. In conclusion, the introduction of technology in agriculture has had numerous advantages, including increased productivity, efficiency, and profitability, while also promoting sustainable practices and ensuring food security.
Further, with the world's population expected to reach nearly 10 billion by 2050, there is a growing need for more efficient and sustainable agricultural practices. Limited agricultural land and the increasing demand for food mean that farmers must find ways to increase their yield and productivity. Furthermore, despite the advancement in the field of technology in agriculture, there are still several shortcomings that need to be addressed. One significant issue is the inability of current technology to accurately predict crop yields. This can lead to overproduction or underproduction, resulting in financial losses for farmers and food shortages for consumers. Additionally, current technology often fails to take into account the impact of climate change on agriculture, making it difficult for farmers to plan and prepare for changing weather patterns. Another challenge is the lack of customization available in existing agricultural technology. Many systems are designed for large-scale farming operations and are not easily adaptable to smaller farms or specific geographical regions. This can limit the usefulness of technology for certain farmers and prevent them from fully realizing its benefits. Therefore, the existing technologies failed to update the farmers about market prices of crops, accurate weather information or forecasts, soil information, etc. in an efficient and effective manner. As a side effect of this, the following secondary problems manifest:
1. Sub-Optimal Crop Selection: The crop selection should be based on parameters such as soil type, weather, rainfall, irrigation, etc., but farmers

generally select crops based on what their peers are growing, or they just end up choosing the traditional crops. This reduces the overall productivity.
2. Lack of Awareness Towards Crop Economics: Farmers lack solutions and data to estimate a financial outcome of their crop post-harvest. This leads to poor financial planning and frequent loses.
3. Lack of Experimentation: Farmers don’t try new crops as frequently as they would have liked because they don’t have solutions to predict the productivity or the profitability of those crops.
In conclusion, while technology has revolutionized agriculture, there are still limitations that need to be addressed, such as the accurate prediction of yields, consideration of climate change, and customization to suit the needs of farmers in specific regions. In conclusion, to meet the growing demand for food and overcome the limitations of current agricultural technologies, there is a need for continued investment in research and development, adoption of sustainable farming practices, and improved education and training for farmers.
Having said that, and in view of the aforementioned shortcomings, enigmas, and drawbacks inherent in the existing techniques, there exist unembellished needs for an improved and enhanced mechanism to provide agricultural recommendations to farmers or users. Further, there is a requirement to propose a system and method for providing agricultural recommendations that not only considers various factors related to agricultural region(s), such as soil, topography, weather, etc., but also predicts a yield and return on investment. Furthermore, there is a requirement to propose a system and method for generating a set of crop recommendations for an agricultural region based on detecting traditional crop(s) and additional crop(s) from other agricultural regions.

OBJECTS OF THE INVENTION
Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
It is an object of the present disclosure to provide a system and a method that facilitates for providing agricultural recommendations for an agricultural region based on agricultural data from its similar agricultural regions.
It is an object of the present disclosure to provide a system and a method that receives data associated with various agricultural regions from a set of data sources and provides crop forecast for an agricultural region that is similar to said various agricultural regions.
It is an object of the present disclosure to provide a system and a method that process an input data received from the set of data sources and provide return on investment (ROI) related recommendation(s) for the agricultural region.
It is an object of the present disclosure to provide a system and a method that identifies traditional crop(s) and additional crop(s) associated with an agricultural region.
It is an object of the present disclosure to provide a system and a method that generates crop recommendation(s) for an agricultural region based on the identification of traditional crop(s) and additional crop(s) from similar agricultural regions of said agricultural region.
It is also an object of the present disclosure to provide crop recommendations along with predicted values for yield, cost, and profit etc. to help farmers in taking an informed decision about what kind of crop they want to grow and how much benefit they can make from it. For instance, the farmers can compare these

predicted values related to different crops grown in different agricultural regions and decide to grow one/more crops based on their preferences.
It is also an object of the present disclosure to provide recommendations for proper selection of crops to farmers that can be the first step towards increasing the overall productivity and profitability for the farmers.
It is also an object of the present disclosure to provide agricultural recommendations to farmers such that the farmers based on such agricultural recommendations may get an estimation of a probable yield, cost, and profits they can make from growing a particular crop.
It is also an object of the present disclosure to provide a solution that encompasses collection of agriculture related data associated with a geographical region from reliable and authentic sources, such as a village level agriculture related data from public/government databases and/or a weather information of the village from a weather service provider etc.
Another object of the present disclosure is to provide a solution that may make data-driven predictions about yield, price and profits of various crops grown in various regions and may accordingly recommend to farmers crop(s) to be grown in their fields to aid the farmers take informed decisions.
Yet another object of the present disclosure is to provide crop recommendation(s) to help farmers in taking an informed decision about what kind of crop they may grow based on relevant region parameters like soil type, climate information, etc.
SUMMARY
This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

An aspect of the present disclosure may relate to a method for providing one or more agricultural recommendations. The method comprises receiving, by an input unit, an input agriculture region. The method further comprises receiving, by a receiver unit from a set of data sources, an input data associated with a set of pre-stored agriculture regions. Further, the method comprises generating, by a processing unit using one or more data processing techniques, a processed data based on the input data. The method further encompasses searching, by a searching unit using one or more similarity techniques, a set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. The method further encompasses generating, by the processing unit, a crop based data based on the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. Further, the method comprises generating, by the processing unit using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. The method further comprises generating, by the processing unit, a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data. Thereafter, the method comprises generating and providing, by the processing unit, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data.
Another aspect of the present disclosure may relate to a system for providing one or more agricultural recommendations. The system comprises an input unit, configured to receive an input agriculture region. Further, the system comprises a

receiver unit, configured to receive from a set of data sources, an input data associated with a set of pre-stored agriculture regions. The system further comprises a processing unit, configured to generate using one or more data processing techniques, a processed data based on the input data. Further, the system encompasses a searching unit configured to search using one or more similarity techniques, a set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. Thereafter, the processing unit is further configured to generate, a crop based data based on the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. The processing unit is further configured to generate, using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. The processing unit is further configured to generate, a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data. Thereafter, the processing unit is further configured to generate and provide, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data.
Another aspect of the present disclosure may relate to a user device for providing one or more agricultural recommendations. The user device comprises a system, wherein the system comprises: an input unit, configured to receive an input agriculture region; a receiver unit, configured to receive from a set of data sources, an input data associated with a set of pre-stored agriculture regions; a processing unit, configured to generate using one or more data processing techniques, a

processed data based on the input data; and a searching unit configured to search using one or more similarity techniques, a set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. Thereafter, the processing unit is further configured to: generate, a crop based data based on the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions; generate, using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions; generate, a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data; and generate and provide, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data.
Another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instructions for providing one or more agricultural recommendations. The storage medium comprising executable code which, when executed by a processor, causes the processor to: enable an input unit to receive an input agriculture region; enable a receiver unit to receive from a set of data sources, an input data associated with a set of pre-stored agriculture regions; generate using one or more data processing techniques, a processed data based on the input data; enable a searching unit to search using one or more similarity techniques, a set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region; generate, a crop based data based on the

processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions; generate, using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions; generate, a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data; and generate and provide, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data.
Another aspect of the present disclosure may relate to a method for generating a set of crop recommendations. The said method comprises receiving, by an input unit, an input agriculture region. The method further comprises receiving, by a receiver unit, a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region. Further, the method comprises generating, by a processing unit using one or more data processing techniques, a set of processed data based on the set of input data. The method further comprises generating, by the processing unit using one or more data scaling techniques, a set of scaled data based on the set of processed data. The method further comprises searching, by a searching unit, a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. Further, the method encompasses identifying, by the searching unit, one or more candidate agriculture regions from the set of nearest

neighbors of the input agriculture region. The method further encompasses retrieving, by the searching unit from a storage unit, one or more pre-stored traditional crops associated with the one or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions. Further, the method encompasses identifying, by the searching unit, a set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops. Thereafter, the method encompasses generating, by a crop recommendation unit, the set of crop recommendations based on at least one of the set of top traditional crops and the set of top additional crops.
Another aspect of the present disclosure may relate to a system for generating a set of crop recommendations. The system comprises an input unit, configured to receive an input agriculture region. The system further comprises a receiver unit, configured to receive a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region. Further, the system comprises a processing unit, configured to generate, using one or more data processing techniques, a set of processed data based on the set of input data. The processing unit is further configured to generate, using one or more data scaling techniques, a set of scaled data based on the set of processed data. The system further encompasses a searching unit, configured to search, a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. The searching unit is further configured to identify, one or more candidate agriculture regions from the set of nearest neighbors of the

input agriculture region. Furthermore, the searching unit is further configured to retrieve, from a storage unit, one or more pre-stored traditional crops associated with the one or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions. The searching unit is further configured to identify, a set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops. Thereafter, the system comprises a crop recommendation unit, configured to generate the set of crop recommendations based on at least one of the set of top traditional crops and the set of top additional crops.
Another aspect of the present disclosure may relate to a user device for generating a set of crop recommendations. The said user device comprises a system, wherein the system comprises: an input unit, configured to receive an input agriculture region; a receiver unit, configured to receive a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region; a processing unit, configured to: 1) generate, using one or more data processing techniques, a set of processed data based on the set of input data, and 2) generate, using one or more data scaling techniques, a set of scaled data based on the set of processed data; a searching unit, configured to: 1) search, a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region, 2) identify, one or more candidate agriculture regions from the set of nearest neighbors of the input agriculture region, 3) retrieve, from a storage unit, one or more pre-stored traditional crops associated with the one

or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions, and 4) identify, a set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops; and a crop recommendation unit, configured to generate the set of crop recommendations based on at least one of the set of top traditional crops and the set of top additional crops.
Yet another aspect of the present disclosure relates to a non-transitory computer readable storage medium storing instructions for generating a set of crop recommendations. The storage medium comprising executable code which, when executed by a processor, causes the processor to: enable an input unit to receive an input agriculture region; enable a receiver unit to receive a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region; generate, using one or more data processing techniques, a set of processed data based on the set of input data; generate, using one or more data scaling techniques, a set of scaled data based on the set of processed data; enable a searching unit to: 1) search, a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region, 2) identify, one or more candidate agriculture regions from the set of nearest neighbors of the input agriculture region, 3) retrieve, from a storage unit, one or more pre-stored traditional crops associated with the one or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions, and 4) identify, a

set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops; and enable a crop recommendation unit to generate the set of crop recommendations based on at least one of the set of top traditional crops and the set of top additional crops.
DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Also, the embodiments shown in the figures are not to be construed as limiting the disclosure, but the possible variants of the method and system according to the disclosure are illustrated herein to highlight the advantages of the disclosure. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components or circuitry commonly used to implement such components.
FIG.1 illustrates an exemplary block diagram depicting an exemplary network architecture diagram [100], in accordance with an embodiment of the present disclosure.
FIG.2 illustrates an exemplary block diagram of a system [200] for providing one or more agricultural recommendations, in accordance with an embodiment of the present disclosure.

FIG.3 illustrates an exemplary method flow diagram [300], for providing one or more agricultural recommendations, in accordance with an embodiment of the present disclosure.
FIG.4 illustrates an exemplary method flow diagram [400], for generating a set of crop recommendations, in accordance with an embodiment of the present disclosure.
The foregoing shall be more apparent from a more detailed description of the invention below.
DETAILED DESCRIPTION
In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.
The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.
Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure.
The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry

for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor.
As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and/or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment/device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of an input unit, a receiver unit, a processing unit, a storage unit, a searching unit, a crop recommendation unit and any other such unit(s) which are required to implement the features of the present disclosure.
As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or

other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.
The terms "input agriculture region", "agriculture region", "farm land", "agriculture land", and other similar terms may be used interchangeably throughout this patent specification. These terms are intended to be used in a descriptive and non-limiting manner, and should not be interpreted as restricting the scope of the invention. The invention is not limited to any particular type of agricultural area or region, and any similar type of area or region can be used as input data for the system and method of the invention. Therefore, the scope of the invention is not limited to any particular term or phrase used in the specification, but rather encompasses any type of agricultural area or region that can be used as input data.
The present invention relates to a system and a method for providing one or more agricultural recommendations to a user. The user may include a farmer, a person agricultural industry, a researcher of crops or harvest or the like person or entity. The present invention is directed towards a novel system and method for providing agricultural recommendations to the user. The present invention utilizes an input agriculture region to determine its nearest neighbour based on a similarity score, and generates recommendations related to crop forecasts, crop yield, and return on investment (ROI) etc. based on a data associated with the nearest neighbour of the input agriculture region. The present invention further encompasses retrieving a set of traditional crops and a set of additional crops associated with one or more closest nearest neighbours of the input agriculture region, and generating one or more crop recommendations for the input agriculture region based on the retrieved set of traditional crops and/or the retrieved set of additional crops. The novel solution offered by the present

invention is its ability to provide precise and customized recommendations based on the user's specific agricultural region, crop yield forecasts, and traditional and additional crop options etc. The system and method as disclosed in the present disclosure have at least a technical effect to enhance agricultural practices and maximize yields, thereby benefiting farmers, the agricultural industry, and crop researchers. Also, technical solution as disclosed in the present disclosure overcomes the limitations of the existing solutions and provides technical advantages over the existing solutions at least by firstly collecting an information from authentic data sources, then extracting cleaning and transforming the collected information to obtain a relevant information. Later, by processing the relevant information through various machine learning based techniques to generate agricultural or crop recommendations. Finally, by calculating return on investment (ROI) to help in assessing an economic outcome of recommended crop(s).
Referring to Figure 1, the Figure 1 illustrates an exemplary block diagram depicting an exemplary network architecture diagram [100], in accordance with exemplary embodiments of the present disclosure. As shown in Figure 1, the exemplary network architecture diagram [100] comprises at least one user equipment / user device [102] connected to at least one server entity [106] via at least one network [104], wherein in an implementation the server entity [106] further comprises a system [200] configured to implement the feature of the present invention. Also, in an implementation the system [200] may reside in the server entity [106] or the user device [102] or both or partially in either the server entity [106] or the user device [102] as obvious to a person skilled in the art to implement the features of the present disclosure.
Also, in Figure 1 only the single user equipment (or may be referred to as user device) [102], the single network [104] and the single server entity [106] are

shown, however, there may be multiple such user equipment [102], server entities [106], and/or networks [104] or there may be any such numbers of said user equipment [102], server entities [106] and/or networks [104] obvious to a person skilled in the art or as required to implement the features of the present disclosure. Further, in the implementation where the system [200] is present in the server entity [106], based on the implementation of the features of the present disclosure, one or more agricultural recommendations may be provided by the system [100] to a user of the user equipment [102], by transmitting and displaying the one or more agricultural recommendations on the user equipment [102].
Referring to Figure 2, an exemplary block diagram of a system [200] for providing one or more agricultural recommendations is shown, in accordance with the exemplary embodiments of the present invention. The system [200] comprises at least one input unit [202], at least one receiver unit [204], at least one processing unit [206], at least one searching unit [208], at least one storage unit [212] and at least one crop recommendation unit [210]. Also, all of the components/ units of the system [200] are assumed to be connected to each other unless otherwise indicated below. Also, in Fig. 2 only a few units are shown, however, the system [200] may comprise multiple such units or the system [200] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system [200] may be present in a server device to implement the features of the present invention. The system [200] may be a part of a service platform or may be independent but in communication with the service platform.
The system [200] is configured for providing the one or more agricultural recommendations, with the help of the interconnection between the components/units of the system [200].

The input unit [202] is configured to receive an input agriculture region. Further, it is important to note that the term used in the patent specification “the input agriculture region” in an implementation of the present invention may include an individual farmland, a collection of farmland in an area, an agriculture region of a district, an agriculture region of a state, an agriculture region of a village, or any other similar area, are intended to be illustrative only and should not be construed as limiting the scope of the invention. The person skilled in the art would appreciate that the invention is not limited to any particular type of agricultural area or region and that any similar type of area or region may be used as the input agriculture region. Therefore, the scope of the invention is not limited to the examples provided in the specification, but rather encompasses any type of agricultural area or region that can be used as an input data for the system [200] and method of the invention.
Further, the system [200] comprises the receiver unit [204] connected to at least the input unit [202]. The receiver unit [204] is configured to receive from a set of data sources, an input data associated with a set of pre-stored agriculture regions. In an implementation of the present disclosure, the set of data sources comprises at least one of a government digital platform, a non-government digital platform, and a statistical datasheet associated with the set of pre-stored agriculture regions. It is important to note that throughout the patent specification, the terms "government digital platform", "non-government digital platform", and "statistical datasheet" may be used to describe digital sources of input data received by the receiver unit [204] of the system [200]. These terms are intended to be used in a descriptive and non-limiting manner, and should not be interpreted as restricting the scope of the invention. The set of data sources may include data source(s) that can source data from various sources, including digital platforms operated by government or non-government organizations, as well as statistical datasheets in

either digital or non-digital form. Additionally, the term "statistical datasheet" as used herein is not limiting in nature, and a person skilled in the art would appreciate that the term includes various forms of graphical or numeric representations of input data. Therefore, the scope of the invention is not limited to any particular type of source for the input data, or any particular form of statistical datasheet.
Further, in another implementation of the present invention, the input data associated with each input agriculture region from the set of pre-stored agriculture regions comprises at least one of one or more land parameters associated with said each input agriculture region, one or more climate parameters associated with said each input agriculture region and one or more historical crop sowing patterns associated with said each input agriculture region. In another implementation of the present invention, the input data associated with said each input agriculture region from the set of pre-stored agriculture regions comprises various parameters. These parameters may include one or more land parameters, such as a soil type, a pH level, a nutrient level, and a topography, associated with said each input agriculture region. Additionally, the input data may comprise one or more climate parameters, such as one or more temperature parameters, one or more humidity parameters, one or more rainfall parameters , one or more wind speed parameters, and one or more solar radiation parameters, associated with said each input agriculture region. Furthermore, the input data may comprise the one or more historical crop sowing patterns associated with said each input agriculture region, such as a crop type, a crop yield, a crop rotation, and a crop sowing time, which can be used to determine the best crops to sow in the future. Furthermore, a person skilled in the art would appreciate that the input data associated with said each input agriculture region from the set of pre-stored agriculture regions as disclosed herein may include

additional or related parameters in addition to the ones mentioned, and that the scope of the present invention is not limited to only those parameters mentioned.
Further, in another implementation of the present invention the input data associated with said each input agriculture region from the set of pre-stored agriculture regions is in at least one of an image format, a spreadsheet format and a portable document format (pdf). It should be understood that the use of image format, a spreadsheet format and portable document format (pdf) for the input data associated with said each input agriculture region from the set of pre-stored agriculture regions is merely exemplary, and the person skilled in the art would appreciate that other media formats, such as video, presentation, and the like, can also be used without departing from the scope of the present invention. The use of specific formats is not intended to limit the scope of the invention in any way.
Further, the system [200] comprises the processing unit [206] connected to at least the input unit [202] and the receiver unit [204]. The said processing unit [206] is configured to generate using one or more data processing techniques, a processed data based on the input data. Further in an implementation of the present disclosure, the one or more data processing techniques may comprises at least one of a data clean-up technique, an error correction technique, a data standardization technique, and a data management technique. In an exemplary implementation of the present invention, the processing unit [206] may include one or more use of these techniques and is not intended to limit the scope of the present invention, and other similar techniques known to a person skilled in the art may also be used for generating the processed data. It should be noted that the one or more data processing techniques used may include inbuilt error handling and error correction techniques to ensure accuracy and reliability of the extracted data. Additionally, the processing unit [206] may also support merging and standardizing data coming from heterogeneous sources, ensuring that the

processed data is consistent and uniform. It should be noted that the data processing unit techniques as disclosed herein can be one or more known processing techniques, one or more novel processing techniques, or a combination of the one or more known processing techniques and the one or more novel processing techniques. The person skilled in the art would appreciate that the selection of a particular processing technique or a combination thereof may depend on various factors such as the type of input data, a desired output, and available computing resources, among others. Therefore, the scope of the present invention is not limited to any particular processing technique or combination thereof used for generating the processed data based on the input data.
Further, in another implementation of the present invention, the processing unit [206] is further configured to extract, the input data associated with the set of pre-stored agriculture regions from the image format, using one or more image data extraction techniques. The person skilled in the art would appreciate that the one or more image data extraction techniques used for extracting the input data associated with the set of pre-stored agriculture regions from the image format, as disclosed herein, can be one or more known image data extraction techniques, one or more novel image data extraction techniques or a combination of the one or more known image data extraction techniques and the one or more novel image data extraction techniques. It should be noted that the use of these techniques is just exemplary and should not be interpreted to limit the scope of the invention.
Further, in another implementation of the present invention the processing unit [206] is further configured to extract, the input data associated with the set of pre-stored agriculture regions from the pdf, using one or more pdf extraction techniques and a set of pre-defined codes. Further, it should be noted that the

one or more PDF extraction techniques mentioned in the patent specification can be one or more known PDF extraction techniques, one or more novel PDF extraction techniques, or a combination of both, wherein these techniques are used for extracting the input data associated with the set of pre-stored agriculture regions from PDF files. A person skilled in the art would appreciate that the scope of the invention is not limited to any particular type of PDF extraction technique. Furthermore, a person skilled in the art would appreciate that the set of pre¬defined codes as disclosed herein is just exemplary and should not be interpreted to limit the scope of the invention. The set of pre-defined codes can be one or more known pre-defined codes, one or more novel pre-defined codes, or a combination of the one or more known pre-defined codes and the one or more novel pre-defined codes used for extracting the input data associated with the set of pre-stored agriculture regions from the pdf by the processing unit [206]. In an exemplary implementation of the present disclosure, the set of pre-defined codes may include but not limited to rainfall, area irrigation, crop shown area, crop irrigated area, fertilizer, sprinkles and/or electrification etc., that may be used to extract the input data associated with the set of pre-stored agriculture regions from the pdf in a tabular format.
Furthermore, in an implementation of the present disclosure the searching unit [208] is configured to search using one or more similarity techniques, a set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. In another implementation of the present invention as disclosed by the present disclosure, the searching unit [208] is further configured to search using the one or more similarity techniques, the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on the processed data associated with the set of pre-stored agriculture regions. It

should be noted that the one or more similarity techniques used for searching the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions are not limited to any particular known or novel similarity technique. The person skilled in the art would appreciate that the one or more similarity techniques can be any known similarity technique, one or more novel similarity techniques or a combination of one or more known and one or more novel similarity techniques. This disclaimer is not intended to limit the scope of the present disclosure.
Furthermore, in an exemplary implementation of the present invention as disclosed by this disclosure, the searching unit [208] searches the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on the similarity score based on processed data from the input data associated with the set of pre-stored agriculture regions such as one or more rainfall associated with each pre-stored agriculture region, one or more irrigation associated with each pre-stored agriculture region, one or more soil parameters (i.e., pH, EC, OC) associated with pre-stored each agriculture region, one or more topology associated with each pre-stored agriculture region etc.. Thereafter, in another exemplary implementation, one or more known similarity techniques such as adjusted cosine, euclidean distance, etc. are used to search the set of nearest neighbors of the input agriculture region.
Further, in an implementation of the present disclosure, the processing unit [206] is further configured to generate, a crop based data based on the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions, wherein the crop based data comprises a data of at least one of one or more traditional crops, one or more additional crops and a ranking one or more crops. As used herein a “traditional crop” is a crop that is traditionally grown in an

agriculture region and an “additional crop” is a crop that is different from the traditionally grown crops in the agriculture region. Further, a person skilled in the art would appreciate that the crop based data generated by the processing unit [206] as disclosed herein can include data of one or more traditional crops, one or more additional crops, and a ranking of one or more crops. The crop based data may be generated using one or more known data processing techniques, one or more novel data processing techniques, or a combination of both, and is not intended to limit the scope of the present invention.
Further, in an implementation of the present invention, the processing unit [206] as disclosed by the present disclosure is further configured to generate, using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions, wherein the crop forecasting data comprises at least one of a crop yield prediction data, a crop market price forecasting data, and a crop cultivation cost data. Further, it should be noted that the one or more forecasting techniques used by the processing unit [206] to generate the crop forecasting data as disclosed herein can be one or more known forecasting techniques, one or more novel forecasting techniques, or a combination of both. The scope of the present invention is not intended to be limited by the specific forecasting techniques used for generating the crop forecasting data. Furthermore, a person skilled in the art would appreciate that the crop forecasting data generated by the processing unit [206] as disclosed herein may include one or more of crop yield prediction data, crop market price forecasting data, and crop cultivation cost data. The crop forecasting data may be generated using one or more known data processing techniques, one or more novel data processing techniques, or a combination of both. Therefore, this disclosure should not be interpreted as limiting the scope of the present invention.

Further, in an implementation of the present invention, the processing unit [206] uses one or more regression models to generate the crop yield prediction data based on data collected from different sources. Further, in an exemplary implementation, a mapping between different parameters and the crop yield may be determined, where the parameters may include a net sown area associated with a crop, an irrigation area associated with a crop, a major and minor irrigation requirement associated with an agricultural region, and a soil chemistry etc. By analyzing the historical data and patterns, the processing unit [206] can predict the crop yield for a specific crop, variety of crop, agricultural region, or multiple agricultural regions at once. This information can be used by farmers to make informed decisions regarding crop selection, planning, and management.
In an implementation of the present invention, the processing unit [206] may generate the crop cultivation cost data based on the processed data associated with the set of pre-stored agriculture regions and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. The crop cultivation cost data may be generated using a custom technique that takes into account various factors such as land parameters, climate parameters, historical crop sowing patterns, and other relevant data sources. The custom technique may be designed to predict the cultivation cost of a specific crop for upcoming seasons, and may take into account factors such as labor costs, fertilizer costs, seed costs, and other relevant factors that contribute to the overall cost of cultivation. This information can be used to estimate the ROI for a specific crop and help farmers make informed decisions about their agricultural practices.
Further, in an exemplary implementation of the present invention, the processing unit [206] may use the one or more forecasting techniques to generate the crop forecasting data based on at least one of the processed data associated with the

set of pre-stored agriculture regions, wherein the crop forecasting data is extracted from the set of data sources comprising an agronomic portal. Also, the present system [200] may apply a time-series forecasting technique and may further need univariate time series data with a seasonal component associated with a crop to generate the crop forecasting data. Further, the crop forecasting data can be forecast for a crop, a variety of crops, an agricultural region, or multiple agricultural regions at once, and in order to achieve this in an implementation, the present system [200] may further generate the crop forecasting data based on the following equation.
P q P Q
yt = C + J] anyt-n + ^ °net-n + J) 4>nyt-sn + ^
n=\ n=\ n=\ n=l
It is important to note that this formula is merely an example, and that other forecasting techniques and equations may be used to generate the crop forecasting data. A person skilled in the art would appreciate that the specific techniques and formulas used may vary based on the specific needs and requirements of the system [200] and are not intended to limit the scope of the present invention.
Further, in an implementation of the present invention, the processing unit [206] as disclosed by the present disclosure is further configured to generate, a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data. Further, in an implementation the return on investment (ROI) data generated by the processing unit [206] as disclosed herein can be based on one or more of the crop based data and the crop forecasting data. Furthermore, the ROI data may be generated using one or more known data processing techniques, one or more novel data processing techniques, or a combination of both. The present invention is not intended to be limited by the specific techniques used to generate the ROI data.

Further, in an implementation of the present invention, the processing unit [206] as disclosed by the present disclosure is further configured to generate and provide, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data. Further, in an exemplary implementation of the present invention, the one or more agricultural recommendations may include one or more of crop selection recommendations, planting time recommendations, crop management practices recommendations, irrigation requirement recommendation, fertilization recommendations, pest control recommendations, and harvest time recommendation. The one or more agricultural recommendations can be based on the analysis of the processed data and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. Further, the one or more agricultural recommendations can be provided to farmers, agricultural consultants, or other stakeholders via a user interface, a mobile application, or any other suitable means. In an exemplary implementation, one or more user-user collaboration techniques may be used to generate the crop recommendation and the ranking of one or more crops. In order to achieve this in an implementation, the present system [200] may further generate the crop recommendation and the ranking of the one or more crops based on the following equation:
n n
Further, it is important to note that this equation is merely an example and that other known collaboration techniques may be used to generate the crop recommendation and the ranking one or more crops. Furthermore, a person skilled in the art would appreciate that the specific techniques and formulas used may vary based on the specific needs and requirements of the system [200] and are not intended to limit the scope of the present invention. The present invention thus provides a comprehensive and efficient solution for agricultural decision-

making that can enable farmers to optimize their crop selection, management, and profitability.
Referring to Figure 3 an exemplary method flow diagram [300], for providing one or more agricultural recommendations, in accordance with exemplary embodiments of the present invention is shown. In an implementation the method [300] is performed by the system [200]. Further, in an implementation, the system [200] may be present in a server device to implement the features of the present invention. Also, as shown in Figure 3, the method [300] starts at step [302].
At step [304], the method [300] as disclosed by the present disclosure comprises receiving, by an input unit [202], an input agriculture region. Further, it is important to note that the term used in the patent specification “the input agriculture region” in an implementation of the present invention may include an individual farmland, a collection of farmland in an area, an agriculture region of a district, an agriculture region of a state, an agriculture region of a village, or any other similar area, are intended to be illustrative only and should not be construed as limiting the scope of the invention. The person skilled in the art would appreciate that the invention is not limited to any particular type of agricultural area or region and that any similar type of area or region may be used as the input agriculture region. Therefore, the scope of the invention is not limited to the examples provided in the specification, but rather encompasses any type of agricultural area or region that can be used as input data for the system [200] and method [300] of the invention.
Next, at step [306], the method [300] as disclosed by the present disclosure comprises receiving, by a receiver unit [204] from a set of data sources, an input data associated with a set of pre-stored agriculture regions. In an implementation

of the present invention, the set of data sources comprises at least one of a government digital platform, a non-government digital platform, and a statistical datasheet associated with the set of pre-stored agriculture regions. It is important to note that throughout the patent specification, the terms "government digital platform", "non-government digital platform", and "statistical datasheet" may be used to describe digital sources of input data received by the receiver unit [204] of the method [300]. These terms are intended to be used in a descriptive and non-limiting manner and should not be interpreted as restricting the scope of the invention. The set of data sources may include data sources that can source data from various sources, including digital platforms operated by government or non¬government organisations, as well as statistical datasheets in either digital or non-digital form. Additionally, the term "statistical datasheet" as used herein is not limiting in nature, and a person skilled in the art would appreciate that the term includes various forms of graphical or numeric representations of input data. Therefore, the scope of the invention is not limited to any particular type of source for the input data or any particular form of statistical data sheet.
Further, in another implementation, the input data associated with each input agriculture region from the set of pre-stored agriculture regions comprises at least one of one or more land parameters associated with said each input agriculture region, one or more climate parameters associated with said each input agriculture region, and one or more historical crop sowing patterns associated with said each input agriculture region. In another implementation of the present invention, the input data associated with each input agriculture region from the set of pre-stored agriculture regions comprises various parameters. These parameters may include one or more land parameters, such as a soil type, a pH level, a nutrient level, and a topography, associated with said each input agriculture region. Additionally, the input data may comprise one or more climate parameters, such as one or more temperature parameters, one or more humidity

parameters, one or more rainfall parameters , one or more wind speed parameters, and one or more solar radiation parameters, associated with said each input agriculture region. Furthermore, the input data may comprise the one or more historical crop sowing patterns associated with said each input agriculture region, such as a crop type, a crop yield, a crop rotation, and a crop sowing time, which can be used to determine the best crops to sow in the future. Furthermore, a person skilled in the art would appreciate that the input data associated with said each input agriculture region from the set of pre-stored agriculture regions as disclosed herein may include additional or related parameters in addition to the ones mentioned, and that the scope of the present invention is not limited to only those parameters mentioned.
Furthermore, in another implementation of the present invention the input data associated with said each input agriculture region from the set of pre-stored agriculture regions is in at least one of an image format, a spreadsheet format and a portable document format (pdf). It should be understood that the use of image format and portable document format (pdf) for the input data associated with said each input agriculture region from the set of pre-stored agriculture regions is merely exemplary, and the person skilled in the art would appreciate that other media formats, such as video, presentation, and the like, can also be used without departing from the scope of the present invention. The use of specific formats is not intended to limit the scope of the invention in any way.
Further, the method [300] as disclosed by the present disclosure further comprises extracting, the input data associated with the set of pre-stored agriculture regions from the image format, by the processing unit [206] using one or more image data extraction techniques. The person skilled in the art would appreciate that the one or more image data extraction techniques used for extracting the input data associated with the set of pre-stored agriculture regions from the image format,

as disclosed herein, can be one or more known image data extraction techniques, one or more novel image data extraction techniques or a combination of the one or more known image data extraction techniques and the one or more novel image data extraction techniques. It should be noted that the use of these techniques is just exemplary and should not be interpreted to limit the scope of the invention.
Further, the method [300] as disclosed by the present disclosure further comprises extracting, the input data associated with the set of pre-stored agriculture regions from the pdf, by the processing unit [206] using one or more pdf extraction techniques and a set of pre-defined codes. Further, it should be noted that the one or more PDF extraction techniques mentioned in the patent specification can be one or more known PDF extraction techniques, one or more novel PDF extraction techniques, or a combination of both, wherein these techniques are used for extracting the input data associated with the set of pre-stored agriculture regions from PDF files. A person skilled in the art would appreciate that the scope of the invention is not limited to any particular type of PDF extraction technique. Furthermore, a person skilled in the art would appreciate that the set of pre¬defined codes as disclosed herein is just exemplary and should not be interpreted to limit the scope of the invention. The set of pre-defined codes can be one or more known pre-defined codes, one or more novel pre-defined codes, or a combination of the one or more known pre-defined codes and the one or more novel pre-defined codes used for extracting the input data associated with the set of pre-stored agriculture regions from the pdf by the processing unit [206]. In an exemplary implementation of the present disclosure, the set of pre-defined codes may include but not limited to rainfall, area irrigation, crop shown area, crop irrigated area, fertilizer, sprinkles and/or electrification etc., that may be used to extract the input data associated with the set of pre-stored agriculture regions from the pdf in a tabular format.

Next, at step [308], the method [300] as disclosed by the present disclosure comprises generating, by a processing unit [206] using one or more data processing techniques, a processed data based on the input data. In an implementation of the present invention, the one or more data processing techniques comprises at least one of a data clean-up technique, an error correction technique, a data standardization technique, and a data management technique. In an exemplary implementation of the present invention, the processing unit [206] may include one or more use of these techniques is not intended to limit the scope of the present invention, and other similar techniques known to a person skilled in the art may also be used for generating the processed data. It should be noted that the one or more data processing techniques used may include inbuilt error handling and error correction techniques to ensure accuracy and reliability of the extracted data. Additionally, the processing unit [206] may also support merging and standardizing data coming from heterogeneous sources, ensuring that the processed data is consistent and uniform. It should be noted that the data processing unit techniques as disclosed herein can be one or more known processing techniques, one or more novel processing techniques, or a combination of the one or more known processing techniques and the one or more novel processing techniques. The person skilled in the art would appreciate that the selection of a particular processing technique or a combination thereof may depend on various factors such as the type of input data, a desired output, and available computing resources, among others. Therefore, the scope of the present invention is not limited to any particular processing technique or combination thereof used for generating the processed data based on the input data
Next, at step [310], the method [300] as disclosed by the present disclosure comprises searching, by a searching unit [208] using one or more similarity

techniques, a set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. In a preferred implementation of the present invention, the searching, by the searching unit [208] using the one or more similarity techniques, the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions is further based on the processed data associated with the set of pre-stored agriculture regions. It should be noted that the one or more similarity techniques used for searching the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions are not limited to any particular known or novel similarity technique. The person skilled in the art would appreciate that the one or more similarity techniques can be any known similarity technique, one or more novel similarity techniques or a combination of one or more known and one or more novel similarity techniques. This disclaimer is not intended to limit the scope of the present disclosure.
Further, in an exemplary implementation of the present invention as disclosed by this disclosure, the searching unit [208] searches the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on the similarity score based on processed data from the input data associated with the set of pre-stored agriculture regions such as one or more rainfall associated with each pre-stored agriculture region, one or more irrigation associated with each pre-stored agriculture region, one or more soil parameters (i.e., pH, EC, OC) associated with each pre-stored agriculture region, one or more topology associated with each pre-stored agriculture region. Thereafter, in another exemplary implementation, one or more known similarity techniques such as adjusted cosine, euclidean distance, etc. are used to search the set of nearest neighbors of the input agriculture region.

Next, at step [312], the method [300] as disclosed by the present disclosure comprises generating, by the processing unit [206], a crop based data based on the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. In a preferred implementation of the present method [300], the crop based data comprises a data of at least one of one or more traditional crops, one or more additional crops and a ranking one or more crops. As used herein a “traditional crop” is a crop that is traditionally grown in an agriculture region and an “additional crop” is a crop that is different from the traditionally grown crops in the agriculture region. Further, a person skilled in the art would appreciate that the crop based data generated by the processing unit [206] as disclosed herein can include data of one or more traditional crops, one or more additional crops, and a ranking of one or more crops. The crop based data may be generated using one or more known data processing techniques, one or more novel data processing techniques, or a combination of both, and is not intended to limit the scope of the present invention.
Next, at step [314], the method [300] as disclosed by the present disclosure comprises generating, by the processing unit [206] using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. In a preferred implementation of the present method [300], the crop forecasting data comprises at least one of a crop yield prediction data, a crop market price forecasting data, and a crop cultivation cost data. Further, it should be noted that the one or more forecasting techniques used by the processing unit [206] to generate the crop forecasting data as disclosed herein can be one or more known forecasting techniques, one or more novel forecasting techniques, or a

combination of both. The scope of the present invention is not intended to be limited by the specific forecasting techniques used for generating the crop forecasting data. Furthermore, a person skilled in the art would appreciate that the crop forecasting data generated by the processing unit [206] as disclosed herein may include one or more of crop yield prediction data, crop market price forecasting data, and crop cultivation cost data. The crop forecasting data may be generated using one or more known data processing techniques, one or more novel data processing techniques, or a combination of both. Therefore, this disclosure should not be interpreted as limiting the scope of the present invention.
Further, in an implementation of the present method [300], the processing unit [206] uses one or more regression models to generate the crop yield prediction data based on data collected from different sources. Further, in an exemplary implementation, a mapping between different parameters and the crop yield may be determined, where the parameters may include a net sown area associated with a crop, an irrigation area associated with a crop, a major and minor irrigation requirement associated with an agricultural region, and a soil chemistry. By analyzing the historical data and patterns, the processing unit [206] can predict the crop yield for a specific crop, variety of crop, agricultural region, or multiple agricultural regions at once. This information can be used by farmers to make informed decisions regarding crop selection, planning, and management.
In an implementation of the present invention, the method [300] as disclosed as disclosed may generate by the processing unit [206] the crop cultivation cost data based on the processed data associated with the set of pre-stored agriculture regions and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. The crop cultivation cost data may be generated using a custom technique that takes into account various factors such as land parameters, climate parameters, historical crop sowing patterns, and other

relevant data sources. The custom technique may be designed to predict the cultivation cost of a specific crop for upcoming seasons, and may take into account factors such as labor costs, fertilizer costs, seed costs, and other relevant factors that contribute to the overall cost of cultivation. This information can be used to estimate the ROI for a specific crop and help farmers make informed decisions about their agricultural practices.
Further, in an exemplary implementation of the present method [300], the processing unit [206] may use the one or more forecasting techniques to generate the crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, wherein the crop forecasting data is extracted from the set of data sources comprising an agronomic portal. Also, the present method [300] may apply a time-series forecasting technique and may further need univariate time series data with a seasonal component associated with a crop to generate the crop forecasting data. Further, the crop forecasting data can be forecast for a crop, a variety of crops, an agricultural region, or multiple agricultural regions at once, and in order to achieve this in an implementation, the present method [300] may further generate the crop forecasting data based on the following equation.
p q P Q
y* =c + X anyt-n + X o*6*-"+ 2 ^»^-*»+ X ^n€t~sn + €t
n=\ n=\ n=\ n=It is important to note that this formula is merely an example, and that other forecasting techniques and equations may be used to generate the crop forecasting data. A person skilled in the art would appreciate that the specific techniques and formulas used may vary based on the specific needs and requirements of the method [300] and are not intended to limit the scope of the present invention.

Next, at step [316], the method [300] as disclosed by the present disclosure comprises generating, by the processing unit [206], a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data. Further, in an implementation of the present method [300], the method [300] may generate by the processing unit [206] the return on investment (ROI) data, wherein the return on investment (ROI) data is further based on one or more of the crop based data and the crop forecasting data. Furthermore, the ROI data may be generated using one or more known data processing techniques, one or more novel data processing techniques, or a combination of both. The present invention is not intended to be limited by the specific techniques used to generate the ROI data.
Next, at step [318], the method [300] as disclosed by the present disclosure comprises generating and providing, by the processing unit [206], the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data. Further, in an implementation of the present method [300], the processing unit [206] as disclosed by the present method [300] is further generates and provides, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data. Further, in an exemplary implementation of the present invention, the one or more agricultural recommendations may include one or more of crop selection recommendations, planting time recommendations, crop management practices recommendations, irrigation requirement recommendation, fertilization recommendations, pest control recommendations, and harvest time recommendation. The recommendations can be further based on the analysis of the processed data and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions. Further, the one or more agricultural

recommendations can be provided to farmers, agricultural consultants, or other stakeholders via a user interface, a mobile application, or any other suitable means. In an exemplary implementation, one or more user-user collaboration techniques may be used to generate the crop recommendation and the ranking of one or more crops. In order to achieve this in an implementation, the present system [200] may further generate the crop recommendation and the ranking of the one or more crops based on the following equation:
n n
Further, it is important to note that this equation is merely an example and that other known collaboration techniques may be used to generate the crop recommendation and the ranking one or more crops. Furthermore, a person skilled in the art would appreciate that the specific techniques and formulas used may vary based on the specific needs and requirements of the system [200] and are not intended to limit the scope of the present invention. The present invention thus provides a comprehensive and efficient solution for agricultural decision¬making that can enable farmers to optimize their crop selection, management, and profitability.
Thereafter, the method [300] terminates at step [320].
Moreover an aspect of the present invention relates to a user device for providing one or more agricultural recommendations, the user device comprising: a system [200], wherein the system [200] comprises: an input unit [202], configured to receive an input agriculture region, a receiver unit [204], configured to receive from a set of data sources, an input data associated with a set of pre-stored agriculture regions, a processing unit [206], configured to generate using one or more data processing techniques, a processed data based on the input data, and a searching unit [208] configured to search using one or more similarity

techniques, a set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region, wherein the processing unit [206] is further configured
5 to: generate, a crop based data based on the processed data associated with the
set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions, generate, using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and
0 the set of nearest neighbors of the input agriculture region in the set of pre-stored
agricultural regions, generate, a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data, and generate and provide, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI)
5 data.
Also, an aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instructions for providing one or more agricultural recommendations. The storage medium comprising executable code
0 which, when executed by a processor, causes the processor to: enable an input
unit to receive an input agriculture region; enable a receiver unit to receive from a set of data sources, an input data associated with a set of pre-stored agriculture regions; generate using one or more data processing techniques, a processed data based on the input data; enable a searching unit to search using one or more
5 similarity techniques, a set of nearest neighbors of the input agriculture region in
the set of pre-stored agricultural regions based on a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region; generate, a crop based data based on the processed data associated with the set of pre-stored agriculture regions, and the

set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions; generate, using one or more forecasting techniques, a crop forecasting data based on at least one of the processed data associated with the set of pre-stored agriculture regions, and the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions; generate, a return on investment (ROI) data based on at least one of the crop based data and the crop forecasting data; and generate and provide, the one or more agricultural recommendations based on at least one of the crop based data, the crop forecasting data and the return on investment (ROI) data.
In one of the implementations of the present disclosure the system [200] is configured for generating a set of crop recommendations, with the help of the interconnection between the components/units of the system [200]. In such implementation the crop recommendation unit [210] may reside within the system [200] or may reside outside the system [200] but is in a connection with the system [200].
Further, in such implementation the input unit [202] is configured to receive an input agriculture region. Furthermore, it is important to note that the term used in the patent specification “the input agriculture region” in an implementation of the present invention may include an individual farmland, a collection of farmland in an area, an agriculture region of a district, an agriculture region of a state, an agriculture region of a village, or any other similar area, are intended to be illustrative only and should not be construed as limiting the scope of the invention. The person skilled in the art would appreciate that the invention is not limited to any particular type of agricultural area or region and that any similar type of area or region may be used as the input agriculture region. Therefore, the scope of the invention is not limited to the examples provided in the specification, but rather encompasses any type of agricultural area or region that can be used as an input data for the system [200] and method of the invention.

Further, the receiver unit [204] is configured to receive a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region. In a preferred implementation of present system [200], the one or more land parameters comprises at least of one or more soil chemistry parameters, one or more location topography parameters, and one more irrigation capacity parameters. It should be noted that the one or more land parameters described in this patent specification is not exhaustive and is provided for illustrative purposes only. The skilled person in the art would appreciate that additional land parameters may exist that could impact crop growth and productivity in an agriculture region. Furthermore, the intention of describing these parameters is not to limit the scope of the specification but to provide a comprehensive example of the types of parameters that can be considered when generating crop recommendations.
Further, in a preferred implementation of present system [200], the one or more climate parameters comprises at least one of one or more past climate parameters and one or more future climate parameters. In a preferred implementation of the present system [200], the one or more climate parameters includes one or more past climate parameters and one or more future climate parameters that are collected from multiple sources. The system [200] can gather past climate data comprising the past climate parameter(s) from historical records or climate databases, while future climate data comprising the future climate parameter(s) can be obtained from weather models or climate change projections. By collecting one or more climate parameters from multiple sources, the system [200] can ensure that the data used for generating the crop recommendations is accurate,

reliable, and up-to-date. This can help to ensure that the crop recommendations generated by the system [200] are based on the most current and comprehensive climate data available. Further, the inclusion of the one or more past climate parameters can provide valuable information on historical weather patterns and trends in the input agriculture region, which can help to inform the crop recommendations for future planting seasons. Similarly, the inclusion of the one or more future climate parameters can provide insights into how the climate may evolve over time and can enable the system [200] to generate the crop recommendations that take into account potential changes in weather patterns. By considering both past and future climate parameters, the present system [200] can provide a comprehensive analysis of the climate conditions in an agriculture region, allowing for more accurate and effective crop recommendations to be generated. It should be noted that the one or more climate parameters described in this patent specification is not exhaustive and is provided for illustrative purposes only. The skilled person in the art would appreciate that there may be additional climate parameters that could impact crop growth and yield in an agriculture region. Furthermore, the intention of describing these parameters is not to limit the scope of the specification but to provide a comprehensive example of the types of parameters that can be considered when generating crop recommendations.
Furthermore, in a preferred implementation of present system [200], the one or more historical crop sowing patterns associated with the input agriculture region comprises at least one of one or more crop parameters, and a net sown area associated with one or more crops sown in the input agricultural region. In an example the one or more crop parameters may include but not limited to at least one of one or more types of the one or more crops sown in the input agricultural region, a weather condition associated with the one or more crops sown in the input agricultural region, and one or more irrigation parameters associated with

the one or more crops sown in the input agricultural region etc. Further, by considering the one or more historical crop sowing patterns, the system [200] can gain valuable insights into the types of crops that have been successfully grown in the input agriculture region in the past. This information can improve one or more crop recommendations generated by the system [200], as it provides a basis for understanding the types of crops that are likely to thrive in the input agriculture region. Additionally, the net sown area associated with each crop can provide valuable information on the relative popularity and success of different crops in the input agriculture region. By incorporating the one or more historical crop sowing patterns into the analysis, the present system [200] can generate more accurate and effective crop recommendations that are tailored to the specific needs and conditions of the input agriculture region. It should be noted that the one or more historical crop sowing patterns provided in the present system [200] are not intended to be exhaustive and do not limit the scope of the specification. The person skilled in the art would appreciate that there may be additional parameters associated with the one or more historical crop sowing patterns and associated data that could be considered in generating crop recommendations. The historical crop sowing patterns included in the present system [200] are provided as examples to illustrate the functionality of the system [200], and other similar data sources may be used as well. The present system [200] is designed to be flexible and adaptable to different data sources and parameters, and should not be limited by the examples provided in the specification.
Further, the processing unit [206] is configured to generate, using one or more data processing techniques, a set of processed data based on the set of input data. In an exemplary implementation of the present invention, the processing unit [206] may include one or more use of these techniques that are not intended to limit the scope of the present invention, and other similar techniques known to a person skilled in the art may also be used for generating the processed data. It

should be noted that the one or more data processing techniques used may include inbuilt error handling and error correction techniques to ensure accuracy and reliability of the extracted data. Additionally, the processing unit [206] may also support merging and standardizing data coming from heterogeneous sources, ensuring that the processed data is consistent and uniform. It should be noted that the data processing techniques as disclosed herein can be one or more known processing techniques, one or more novel processing techniques, or a combination of one or more known processing techniques and one or more novel processing techniques. The person skilled in the art would appreciate that the selection of a particular processing technique or a combination thereof may depend on various factors such as the type of input data, the desired output, and the available computing resources, among others. Therefore, the scope of the invention is not limited to any particular processing technique or combination thereof used for generating the processed data based on the input data.
Further, the processing unit [206] is configured to generate, using one or more data scaling techniques, a set of scaled data based on the set of processed data. Further, in an exemplary implementation of the present invention, the set of processed data based on the set of input data may be aggregated and scaled so that all features are compressed to a similar range (say between 0-1). In an example values of input parameters such as, Ph – Acidity, EC – Electrical Conductivity, OC – Organic Carbon, N – Nitrogen, P – Phosphorous, K – Potassium, Village Topology, Mean Rainfall, Mean Canal Irrigation Ratio – Canal Irrigated Area/Total Sown Area, and/or Mean Total Irrigation Ratio – Total Irrigated Area/Total Sown Area etc. may be scaled using one or more data scaling techniques to a range 0-1. The person skilled in the art would appreciate that the one or more data scaling techniques, as disclosed herein, can be one or more known data scaling techniques, one or more novel data scaling techniques or a combination of one or more known data scaling techniques and one or more novel

data scaling techniques. It should be noted that the use of these techniques is just exemplary and should not be interpreted to limit the scope of the invention.
Further, the searching unit [208] is configured to search, a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region. In a preferred implementation of the system [200], the similarity score between the one or more pre-stored agricultural regions and the input agricultural region is determined based on the set of scaled data and at least one of a cosine similarity technique, an adjusted cosine similarity technique, and a pearson correlation coefficient technique. Further, the person skilled in the art would appreciate that the similarity techniques may include but are not limited to a cosine similarity technique, an adjusted cosine similarity technique, and a Pearson correlation coefficient technique. These techniques are used to determine a degree of similarity between the input agricultural region and the pre-stored agricultural regions in terms of the set of scaled data. The selection of the specific similarity technique may depend on the type and nature of the data being compared and the performance of each technique on that data. The present system [200] is designed to be flexible and adaptable to different similarity techniques, and should not be limited by the examples provided in the specification. Furthermore, it should be noted that the one or more similarity techniques used for searching the set of nearest neighbors of the input agriculture region in the set of pre-stored agricultural regions are not limited to any particular known or novel similarity technique. The person skilled in the art would appreciate that the one or more similarity techniques can be any known similarity technique, one or more novel similarity techniques or a combination of one or more known and one or more novel similarity techniques. This disclaimer is not intended to

Further, the searching unit [208] is configured to identify, one or more candidate agriculture regions from the set of nearest neighbors of the input agriculture region. In a preferred implementation of the system [200], the searching unit [208] is further configured to identify the one or more candidate agriculture regions based on a pre-defined threshold and a similarity score between one or more nearest neighbors in the set of nearest neighbors and the input agriculture region. The pre-defined threshold may be based on a threshold of years, a threshold of net sown area, a threshold of yield in an agriculture region and such other threshold that are obvious to the person skilled in the art would appreciate to be not limiting the scope of the present invention. For instance, the threshold of years may be defined to limit the search to only those agricultural regions with a similar crop history for a specified number of years, while the threshold of net sown area may be set to limit the search to regions with similar acreage under cultivation. Similarly, the threshold of yield may be defined to search for regions with similar crop yields or productivity. The choice of threshold may depend on various factors such as the specific crop, region, or even the preferences of the user.
Further, the searching unit [208] is configured to retrieve, from a storage unit [212], one or more pre-stored traditional crops associated with the one or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions. In a preferred implementation of the system [200], the searching unit [208] is further configured to retrieve the one or more pre-stored traditional crops based on a crop threshold and a match between the one or more historical crop sowing patterns associated with the input agriculture region and one or more historical crop sowing patterns associated with the one or more candidate agriculture regions. Furthermore, in another preferred implementation of the system [200], the searching unit [208] is further configured to retrieve the one or more pre-stored additional crops based

on the crop threshold and a mismatch between the one or more historical crop sowing patterns associated with the input agriculture region and the one or more historical crop sowing patterns associated with the one or more candidate agriculture regions. In an implementation, the crop threshold can be based on various factors. For example, the crop threshold may be based on a minimum number of years for which a particular crop has been traditionally grown in a specific candidate agriculture region. Alternatively, the crop threshold may be based on a minimum net sown area associated with the historical crop sowing patterns of the one or more candidate agriculture regions. Another option may be to set a crop threshold based on a minimum yield of a particular crop in an agriculture region, as recorded in the historical crop sowing patterns. These are just a few examples, and the person skilled in the art would appreciate that other factors could also be used to set the crop threshold, which are not intended to limit the scope of the present invention.
Further, the searching unit [208] is configured to identify, a set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops. In a preferred implementation of the system [200], the searching unit [208] is further configured to identify the set of top traditional crops based on a net sown area associated with one or more crops sown in the one or more candidate agriculture regions. Furthermore, in another preferred implementation of the system [200], the searching unit [208] is further configured to identify the set of top additional crops based on the net sown area associated with the one or more crops sown in the one or more candidate agriculture regions, and a similarity score between the one or more candidate agriculture regions and the input agriculture region. In a preferred implementation of the present system [200], the searching unit [208] may identify the set of top traditional crops based on the net sown area associated with historical crop sowing patterns in the one or more candidate agriculture

regions, and a match between the one or more historical crop sowing patterns associated with the input agriculture region and the one or more historical crop sowing patterns associated with the one or more candidate agriculture regions. Additionally, the searching unit [208] may identify the set of top additional crops based on the net sown area associated with the one or more crops sown in the one or more candidate agriculture regions, a mismatch between the one or more historical crop sowing patterns associated with the input agriculture region and the one or more historical crop sowing patterns associated with the one or more candidate agriculture regions, and a similarity score between the one or more candidate agriculture regions and the input agriculture region. It should be noted that these thresholds and techniques are not exhaustive and can be modified by a person skilled in the art without departing from the scope of the present invention. In an exemplary implementation of the system [200], the set of top additional crops identified by the searching unit [208] may be utilized by a Collaborative Filtering technique to generate a personalized ranking of crops for the input agricultural region. The Collaborative Filtering technique takes into account the user preferences and feedback, and generates a ranking of the identified crops based on their relevance and suitability to the user's needs. Further, in another exemplary implementation of the system [200], the set of top traditional crops and set of top additional crops are ranked to generate a recommendation list for the input agricultural region. The set of traditional crops can be ranked based on the net crop area sown in the candidate agricultural regions. On the other hand, the set of non-traditional (additional) crops may be ranked based on the similarity score between the input agricultural region and the candidate agricultural regions, as well as the net crop area sown in the one or more candidate agriculture regions. In an exemplary implementation ranking technique disclosed by the present system [200] takes into account both a popularity of a crop in a particular region as well as the similarity between the input agriculture region and the one or more candidate agriculture regions. The recommendation

list can be further refined and ranked using a collaborative filtering technique to improve its accuracy. In order to achieve this an exemplary ranking technique may be based on the following equation.
n n
11=1 U = l
Further, the crop recommendation unit [210] is configured to generate the set of crop recommendations based on at least one of the set of top traditional crops and the set of top additional crops. In a preferred implementation of the present invention, the set of crop recommendations comprises one or more crop recommendations of at least one of one or more top traditional crops from the set of top traditional crops and one or more top additional crops from the set of top additional crops. Therefore, the crop recommendation unit [210] of the present invention plays a crucial role in generating a final set of crop recommendations for the input agriculture region. The crop recommendation unit [210] utilizes the information gathered by the searching unit [208] and the ranking technique to generate the set of crop recommendations. The recommendations are based on at least one of the set of top traditional crops and the set of top additional crops obtained from the previous steps. The crop recommendation unit [210] thus ensures that final recommendations are based on a thorough analysis of the historical crop sowing patterns, net sown area, and yield of the agriculture regions, thereby providing accurate and relevant crop recommendations for the input agriculture region.
Referring to Figure 4, an exemplary method [400] flow diagram, for generating a set of crop recommendations, in accordance with exemplary embodiments of the

present invention is shown. In an implementation the method [400]is performed by the system [200]. Further, in an implementation, the system [200] may be present in a server device to implement the features of the present invention. Also, as shown in Figure 4, the method [400] starts at step [402].
At step [404], the method [400] comprises receiving, by an input unit [202], an input agriculture region. Furthermore, it is important to note that the term used in the patent specification “the input agriculture region” in an implementation of the present invention may include an individual farmland, a collection of farmland in an area, an agriculture region of a district, an agriculture region of a state, an agriculture region of a village, or any other similar area, are intended to be illustrative only and should not be construed as limiting the scope of the invention. The person skilled in the art would appreciate that the invention is not limited to any particular type of agricultural area or region and that any similar type of area or region may be used as the input agriculture region. Therefore, the scope of the invention is not limited to the examples provided in the specification, but rather encompasses any type of agricultural area or region that can be used as an input data for the system [200] and method [400] of the invention.
Next, at step [406], the method [400] comprises receiving, by a receiver unit [204], a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region. In a preferred implementation of present method [400], the one or more land parameters comprises at least of one or more soil chemistry parameters, one or more location topography parameters, and one more irrigation capacity parameters. It should be noted that the one or more land parameters described in this patent specification is not exhaustive and

is provided for illustrative purposes only. The skilled person in the art would
appreciate that additional land parameters may exist that could impact crop
growth and productivity in an agriculture region. Furthermore, the intention of
describing these parameters is not to limit the scope of the specification but to
5 provide a comprehensive example of the types of parameters that can be
considered when generating crop recommendations.
Further, in an implementation of the present method [400], the one or more climate parameters comprises at least one of one or more past climate parameters
0 and one or more future climate parameters. In a preferred implementation of the
present method [400], the one or more climate parameters includes one or more past climate parameters and one or more future climate parameters that are collected from multiple sources. The method [400] can gather past climate data comprising the past climate parameter(s) from historical records or climate
5 databases, while future climate data comprising the past climate parameter(s) can
be obtained from weather models or climate change projections. By collecting one or more climate parameters from multiple sources, the method [400] can ensure that the data used for generating the crop recommendations is accurate, reliable, and up-to-date. This can help to ensure that the crop recommendations generated
0 by the method [400] are based on the most current and comprehensive climate
data available. Further, the inclusion of the one or more past climate parameters can provide valuable information on historical weather patterns and trends in the input agriculture region, which can help to inform the crop recommendations for future planting seasons. Similarly, the inclusion of the one or more future climate
5 parameters can provide insights into how the climate may evolve over time and
can enable the method [400] to generate the crop recommendations that take into account potential changes in weather patterns. By considering both past and future climate parameters, the present method [400] can provide a comprehensive analysis of the climate conditions in an agriculture region, allowing

for more accurate and effective crop recommendations to be generated. It should be noted that the one or more climate parameters described in this patent specification is not exhaustive and is provided for illustrative purposes only. The skilled person in the art would appreciate that there may be additional climate parameters that could impact crop growth and yield in an agriculture region. Furthermore, the intention of describing these parameters is not to limit the scope of the specification but to provide a comprehensive example of the types of parameters that can be considered when generating crop recommendations.
Further, in an implementation of the present method [400], the one or more historical crop sowing patterns associated with the input agriculture region comprises at least one of one or more crop parameters, and a net sown area associated with one or more crops sown in the input agricultural region. In an example the one or more crop parameters may include but not limited to at least one of one or more types of the one or more crops sown in the input agricultural region, a weather condition associated with the one or more crops sown in the input agricultural region, and one or more irrigation parameters associated with the one or more crops sown in the input agricultural region etc. Further, by considering the one or more historical crop sowing patterns, the method [400] can gain valuable insights into the types of crops that have been successfully grown in the region in the past. This information can improve one or more crop recommendations generated by the method [400], as it provides a basis for understanding the types of crops that are likely to thrive in the input agriculture region. Additionally, the net sown area associated with each crop can provide valuable information on the relative popularity and success of different crops in the input agriculture region. By incorporating the one or more historical crop sowing patterns into the analysis, the present method [400] can generate more accurate and effective crop recommendations that are tailored to the specific needs and conditions of the input agriculture region. It should be noted that the

We Claim:
1. A method for generating a set of crop recommendations, the method comprising:
- receiving, by an input unit [202], an input agriculture region;
- receiving, by a receiver unit [204], a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region;
- generating, by a processing unit [206] using one or more data processing techniques, a set of processed data based on the set of input data;
- generating, by the processing unit [206] using one or more data scaling techniques, a set of scaled data based on the set of processed data;
- searching, by a searching unit [208], a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region;
- identifying, by the searching unit [208], one or more candidate agriculture regions from the set of nearest neighbors of the input agriculture region;
- retrieving, by the searching unit [208] from a storage unit [212], one or more pre-stored traditional crops associated with the one or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions;

- identifying, by the searching unit [208], a set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops; and
- generating, by a crop recommendation unit [210], the set of crop recommendations based on at least one of the set of top traditional crops and the set of top additional crops.

2. The method as claimed in claim 1, wherein the set of crop recommendations comprises one or more crop recommendations of at least one of one or more top traditional crops from the set of top traditional crops and one or more top additional crops from the set of top additional crops.
3. The method as claimed in claim 1, wherein the one or more land parameters comprises at least of one or more soil chemistry parameters, one or more location topography parameters, and one more irrigation capacity parameters.
4. The method as claimed in claim 1, wherein the one or more climate parameters comprises at least one of one or more past climate parameters and one or more future climate parameters.
5. The method as claimed in claim 1, wherein the one or more historical crop sowing patterns associated with the input agriculture region comprises at least one of one or more crop parameters, and a net sown area associated with one or more crops sown in the input agricultural region.
6. The method as claimed in claim 1, wherein the identifying, by the searching unit [208], the one or more candidate agriculture regions is further based on a pre-defined threshold and a similarity score between one or more nearest neighbors in the set of nearest neighbors and the input agriculture region.
7. The method as claimed is claim 1, wherein the similarity score between the one or more pre-stored agricultural regions and the input agricultural

region is determined based on the set of scaled data and at least one of a cosine similarity technique, an adjusted cosine similarity technique, and a pearson correlation coefficient technique.
8. The method as claimed in claim 1, wherein the retrieving, by the searching unit [208], the one or more pre-stored traditional crops is based on a crop threshold and a match between the one or more historical crop sowing patterns associated with the input agriculture region and one or more historical crop sowing patterns associated with the one or more candidate agriculture regions.
9. The method as claimed in claim 7, wherein the retrieving, by the searching unit [208], the one or more pre-stored additional crops is based on the crop threshold and a mismatch between the one or more historical crop sowing patterns associated with the input agriculture region and the one or more historical crop sowing patterns associated with the one or more candidate agriculture regions.
10. The method as claimed in claim 1, wherein the identifying, by the searching unit [208], the set of top traditional crops is based on a net sown area associated with one or more crops sown in the one or more candidate agriculture regions.
11. The method as claimed in claim 9, wherein the identifying, by the searching unit [208], the set of top additional crops is based on the net sown area associated with the one or more crops sown in the one or more candidate agriculture regions, and a similarity score between the one or more candidate agriculture regions and the input agriculture region.
12. A system [200] for generating a set of crop recommendations, the system [200] comprising:

- an input unit [202], configured to receive an input agriculture region;
- a receiver unit [204], configured to receive a set of input data associated with the input agriculture region, wherein the set of input

data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region;
- a processing unit [206], configured to:
generate, using one or more data processing techniques, a set of processed data based on the set of input data, and
generate, using one or more data scaling techniques, a set of scaled data based on the set of processed data;
- a searching unit [208], configured to:
search, a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region,
identify, one or more candidate agriculture regions from the set of nearest neighbors of the input agriculture region,
retrieve, from a storage unit [212], one or more pre-stored traditional crops associated with the one or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions, and identify, a set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops; and
- a crop recommendation unit [210], configured to generate the set of
crop recommendations based on at least one of the set of top
traditional crops and the set of top additional crops.
13. The system [200] as claimed in claim 12, wherein the set of crop recommendations comprises one or more crop recommendations of at

least one of one or more top traditional crops from the set of top traditional crops and one or more top additional crops from the set of top additional crops.
14. The system [200] as claimed in claim 12, wherein the one or more land parameters comprises at least of one or more soil chemistry parameters, one or more location topography parameters, and one more irrigation capacity parameters.
15. The system [200] as claimed in claim 12, wherein the one or more climate parameters comprises at least one of one or more past climate parameters and one or more future climate parameters.
16. The system [200] as claimed in claim 12, wherein the one or more historical crop sowing patterns associated with the input agriculture region comprises at least one of one or more crop parameters, and a net sown area associated with one or more crops sown in the input agricultural region.
17. The system [200] as claimed in claim 12, wherein the searching unit [208] is further configured to identify the one or more candidate agriculture regions based on a pre-defined threshold and a similarity score between one or more nearest neighbors in the set of nearest neighbors and the input agriculture region.
18. The system [200] as claimed is claim 12, wherein the similarity score between the one or more pre-stored agricultural regions and the input agricultural region is determined based on the set of scaled data and at least one of a cosine similarity technique, an adjusted cosine similarity technique, and a pearson correlation coefficient technique.
19. The system [200] as claimed in claim 12, wherein the searching unit [208] is further configured to retrieve the one or more pre-stored traditional crops based on a crop threshold and a match between the one or more

and one or more historical crop sowing patterns associated with the one or more candidate agriculture regions.
20. The system [200] as claimed in claim 19, wherein the searching unit [208] is further configured to retrieve the one or more pre-stored additional crops based on the crop threshold and a mismatch between the one or more historical crop sowing patterns associated with the input agriculture region and the one or more historical crop sowing patterns associated with the one or more candidate agriculture regions.
21. The system [200] as claimed in claim 12, wherein the searching unit [208] is further configured to identify the set of top traditional crops based on a net sown area associated with one or more crops sown in the one or more candidate agriculture regions.
22. The system [200] as claimed in claim 21, wherein the searching unit [208] is further configured to identify the set of top additional crops based on the net sown area associated with the one or more crops sown in the one or more candidate agriculture regions, and a similarity score between the one or more candidate agriculture regions and the input agriculture region.
23. A user device for generating a set of crop recommendations, the user device comprising:
- a system [200], wherein the system [200] comprises:
an input unit [202], configured to receive an input agriculture region,
a receiver unit [204], configured to receive a set of input data associated with the input agriculture region, wherein the set of input data comprises at least one of one or more land parameters associated with the input agriculture region, one or more climate parameters associated with the input agriculture region and one or more historical crop sowing patterns associated with the input agriculture region,

a processing unit [206], configured to:
generate, using one or more data processing techniques, a set of processed data based on the set of input data, and
generate, using one or more data scaling techniques, a set of scaled data based on the set of processed data; a searching unit [208], configured to:
search, a set of nearest neighbors of the input agriculture region in a set of pre-stored agricultural regions based on the set of scaled data and a similarity score between one or more pre-stored agricultural regions in the set of pre-stored agricultural regions and the input agriculture region,
identify, one or more candidate agriculture regions from the set of nearest neighbors of the input agriculture region,
retrieve, from a storage unit [212], one or more pre-stored traditional crops associated with the one or more candidate agriculture regions, and one or more pre-stored additional crops associated with the one or more candidate agriculture regions, and
identify, a set of top traditional crops from the one or more pre-stored traditional crops, and a set of top additional crops from the one or more pre-stored additional crops, and
a crop recommendation unit [210], configured to generate the set of crop recommendations based on at least one of the set of top traditional crops and the set of top additional crops.