1. A remote sensing method for management of soil nutrients of cultivated land, comprising the steps of: collecting remote sensing data using satellite or drone-based sensors; pre-processing the remote sensing data by filtering and normalizing it; applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels; generating a soil nutrient map based on the estimated levels of soil nutrients; and providing farmers with the soil nutrient map to aid in the management of soil nutrients for cultivated land.

2. The method of claim 1, wherein the remote sensing data includes spectral data in the visible, near-infrared, and infrared regions.

3. The method of claim 1, wherein the machine learning algorithms is selected from artificial neural networks, support vector machines, and decision trees.

4. The method of claim 1, wherein the estimated levels of soil nutrients are based on the relationships between remote sensing data and laboratory-measured soil nutrient values.

5. The method of claim 1, wherein the soil nutrient map generated in step d) is overlaid on a map of the cultivated land to provide a visual representation of the spatial distribution of soil nutrients.

6. The method of claim 1, wherein the soil nutrient map generated is used to guide variable rate fertilization applications.

7. The method of claim 1, wherein the soil nutrient map generated is used to identify areas of the cultivated land that may require soil remediation.

8. The method of claim 1, wherein the remote sensing data is collected at a high spatial resolution to provide detailed information on soil nutrient variability across the cultivated land.

9. A system for remote sensing-based management of soil nutrients of cultivated land, comprising a remote sensing platform to acquire data related to soil, a data processing unit is arranged to pre-process the remote sensing data, applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels, and generating a soil nutrient map based on the estimated levels of soil nutrients; and a remote computing device is arranged to display the soil nutrient map to aid framers.

10. The system of claim 12, wherein the remote sensing platform includes satellite or drone-based sensors.   REMOTE SENSING METHOD FOR MANAGEMENT OF SOIL NUTRIENTS OF CULTIVATED LAND Abstract The current disclosure may comprise a remote sensing technique for managing the soil nutrients of farmed land. This method may involve the stages of gathering remote sensing data utilising sensors based on satellites or drones. In certain embodiments, preparing the data obtained through remote sensing may also include filtering and normalising the data. Estimating the amounts of soil nutrients may also be accomplished by applying machine learning algorithms to the results of pre-processed remote sensing data in certain embodiments. In certain implementations, one of the steps involves creating a soil nutrient map based on an estimation of the amounts of various soil nutrients. In certain embodiments, farmers may be given a map of the soil's nutrients to assist in the management of soil nutrients for farmed land. Here is one example of an embodiment. Fig. 1 , Claims:Claims :

1. A remote sensing method for management of soil nutrients of cultivated land, comprising the steps of: collecting remote sensing data using satellite or drone-based sensors; pre-processing the remote sensing data by filtering and normalizing it; applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels; generating a soil nutrient map based on the estimated levels of soil nutrients; and providing farmers with the soil nutrient map to aid in the management of soil nutrients for cultivated land.

2. The method of claim 1, wherein the remote sensing data includes spectral data in the visible, near-infrared, and infrared regions.

3. The method of claim 1, wherein the machine learning algorithms is selected from artificial neural networks, support vector machines, and decision trees.

4. The method of claim 1, wherein the estimated levels of soil nutrients are based on the relationships between remote sensing data and laboratory-measured soil nutrient values.

5. The method of claim 1, wherein the soil nutrient map generated in step d) is overlaid on a map of the cultivated land to provide a visual representation of the spatial distribution of soil nutrients.

6. The method of claim 1, wherein the soil nutrient map generated is used to guide variable rate fertilization applications.

7. The method of claim 1, wherein the soil nutrient map generated is used to identify areas of the cultivated land that may require soil remediation.

8. The method of claim 1, wherein the remote sensing data is collected at a high spatial resolution to provide detailed information on soil nutrient variability across the cultivated land.

9. A system for remote sensing-based management of soil nutrients of cultivated land, comprising a remote sensing platform to acquire data related to soil, a data processing unit is arranged to pre-process the remote sensing data, applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels, and generating a soil nutrient map based on the estimated levels of soil nutrients; and a remote computing device is arranged to display the soil nutrient map to aid framers.

10. The system of claim 12, wherein the remote sensing platform includes satellite or drone-based sensors.

Description:REMOTE SENSING METHOD FOR MANAGEMENT OF SOIL NUTRIENTS OF CULTIVATED LAND
Field of the Invention
[0001] This present invention relates to system and method for managing nutrient to optimize agricultural production. More particularly, to remote sensing method for management of soil nutrients of cultivated land.

Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The management of soil nutrients is crucial for maintaining crop productivity and ensuring sustainable agricultural practices. Overuse of fertilizers can lead to soil degradation, environmental pollution, and increased production costs. Underuse of fertilizers can result in lower crop yields, reduced soil fertility, and economic losses for farmers. Hence, it is essential to monitor and manage soil nutrients in cultivated land to optimize fertilizer applications and minimize negative environmental impacts.Various techniques proposed for managing soil nutrients of cultivated land. Few of exemplary documents are discussed below.
[0004] TheEP3664591A1 (By: INDIGO) - A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.
[0005] The AU2014254489A1 (By: SAMOILENKO LURII N) - The invention relates to the methods for soil cultivation, in particular under desert and semi-desert conditions, which enables to increase productivity while saving the soil fertility. It is proposed to form, in pre-sowing period, a screening layer mixed with the soil at the depth of the topsoil and containing milled sodium bentonite and a poultry manure previously treated with an enzyme preparation produced under name of Oxyzyme. After sowing or planting the plants, the screening layer is saturated with water, into the water of the first watering another enzyme preparation - Agrozyme, being added in a certain ratio with water. Optimum conditions of carrying out of the method allow to ensure effective development of biocoenosis and, as a result, to achieve restoration and increase of the fertility of the cultured soil.
[0006] The CA2616578C (By: PROFILE PRODUCTS) - A biological soil nutrient system that combines beneficial soil fungi and bacteria in a growth promoting nutrient medium, embedded in an inorganic porous ceramic particle for direct delivery during soil aerification to the rhizosphere of adventitious plants, including sports turf, landscape and agricultural applications.
[0007] The WO2015110975A1 (By: COOK ROBIN) - A particlized biotic soil amendment product for preparing a damaged or degraded soil ecosystem to establish a self-sustaining floral / vegetative rhizosphere contains a mixture of inorganic "mineral" material, organic material, charcoal, and small amounts of inoculants to promote the growth of beneficial microorganisms including mycorrhizal fungi and nitrogen-fixing bacteria. These ingredients are prilled to form roughly uniform, spherical, ovoid, capsular or other-shaped particles suitable for handling with prior-art agricultural machines. The prilling should produce particles that are resilient enough to survive standard shipping, handling and application procedures, but thereafter break down under irrigation and weathering so as to release their ingredients for use by plants in the vicinity.
[0008] However, the known techniques are expensive, non-reliable and required technical expertise. Thus, there is need for technological advancement in this domain.
[0009] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary
[00010] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00011] The following paragraphs provide additional support for the claims of the subject application.
[00012] This present invention relates to system and method for managing nutrient to optimize agricultural production. More particularly, to remote sensing method for management of soil nutrients of cultivated land.
[00013] Embodiments of the present disclosure may include a remote sensing method for management of soil nutrients of cultivated land, including the steps of collecting remote sensing data using satellite or drone-based sensors. Embodiments may also include pre-processing the remote sensing data by filtering and normalizing it. Embodiments may also include applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels. Embodiments may also include generating a soil nutrient map based on the estimated levels of soil nutrients. Embodiments may also include providing farmers with the soil nutrient map to aid in the management of soil nutrients for cultivated land.
[00014] In some embodiments, the remote sensing data includes spectral data in the visible, near-infrared, and infrared regions. In some embodiments, the machine learning algorithms may be selected from artificial neural networks, support vector machines, and decision trees. In some embodiments, the estimated levels of soil nutrients may be based on the relationships between remote sensing data and laboratory-measured soil nutrient values.
[00015] In some embodiments, the soil nutrient map generated may be overlaid on a map of the cultivated land to provide a visual representation of the spatial distribution of soil nutrients. In some embodiments, the soil nutrient map generated may be used to guide variable rate fertilization applications. In some embodiments, the soil nutrient map generated may be used to identify areas of the cultivated land that may require soil remediation. In some embodiments, the remote sensing data may be collected at a high spatial resolution to provide detailed information on soil nutrient variability across the cultivated land.
[00016] Embodiments of the present disclosure may also include a system for remote sensing-based management of soil nutrients of cultivated land, including a remote sensing platform to acquire data related to soil, a data processing unit that may be arranged to pre-process the remote sensing data, applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels, and generating a soil nutrient map based on the estimated levels of soil nutrients. Embodiments may also include a remote computing device may be arranged to display the soil nutrient map to aid framers.
[00017] Embodiments of the present disclosure may also include remote sensing platform includes satellite or drone-based sensors.

Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 is a flowchart illustrating a remote sensing method for management of soil nutrients of cultivated land, according to some embodiments of the present disclosure.
[00020] FIG. 2 is a block diagram illustrating a system for management of soil nutrients of cultivated land, according to some embodiments of the present disclosure.
[00021] FIG. 3 is a detailed block diagram illustrating the system for management of soil nutrients of cultivated land, according to some embodiments of the present disclosure.
Detailed Description
[00022] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00023] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00024] Following are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of present disclosure. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.
[00025] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00026] This present invention relates to system and method for managing nutrient to optimize agricultural production. More particularly, to remote sensing method for management of soil nutrients of cultivated land.
[00027] FIG. 1 is a flowchart that describes a remote sensing method for management of soil nutrients of cultivated land, according to some embodiments of the present disclosure. At 110, the remote sensing method may include collecting remote sensing data using satellite or drone-based sensors. At 120, the remote sensing method may include pre-processing the remote sensing data by filtering and normalizing it. At 130, the remote sensing method may include applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels. At 140, the remote sensing method may include generating a soil nutrient map based on the estimated levels of soil nutrients. At 150, the remote sensing method may include providing farmers with the soil nutrient map to aid in the management of soil nutrients for cultivated land.
[00028] In some embodiments, the remote sensing data may include spectral data in the visible, near-infrared, and infrared regions. In some embodiments, the machine learning algorithms may be selected from artificial neural networks, support vector machines, and decision trees. In some embodiments, the estimated levels of soil nutrients may be based on the relationships between remote sensing data and laboratory-measured soil nutrient values.
[00029] In some embodiments, the soil nutrient map generated can be overlaid on a map of the cultivated land to provide a visual representation of the spatial distribution of soil nutrients. In some embodiments, the soil nutrient map generated may be used to guide variable rate fertilization applications. In some embodiments, the soil nutrient map generated may be used to identify areas of the cultivated land that may require soil remediation. In some embodiments, the remote sensing data may be collected at a high spatial resolution to provide detailed information on soil nutrient variability across the cultivated land.
[00030] FIG. 2 is a block diagram that describes a system 200 for management of soil nutrients of cultivated land, according to some embodiments of the present disclosure. In some embodiments, the system 200 may also include a remote sensing platform 210 to acquire data related to soil. A data processing unit may be arranged to pre-process the remote sensing data, by applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels, and generating a soil nutrient map based on the estimated levels of soil nutrients. The remote sensing platform 210 may include a remote computing device 212 that may be arranged to display the soil nutrient map to aid framers.
[00031] FIG. 3 is a detailed block diagram that describes the system 310 for management of soil nutrients of cultivated land, according to some embodiments of the present disclosure. In some embodiments, the remote sensing platform 210 may include satellite 322 and drone-based sensors 324. The satellites 322 have a larger field of view and may operate further from Earth than drone-based sensors 324. But this broader coverage has a drawback in the form of a somewhat lower degree of detail and a view that is frequently obscured by clouds or other obstructions.
[00032] The current disclosure may comprise a remote sensing technique for managing the soil nutrients of farmed land. This method may involve the stages of gathering remote sensing data utilising sensors based on the satellite 322 and drone-based sensors 324. In certain embodiments, preparing the data obtained through remote sensing may also include filtering and normalising the data. Estimating the amounts of soil nutrients may also be accomplished by applying machine learning algorithms to the results of pre-processed remote sensing data in certain embodiments. In certain embodiments, farmers may be given a map of the soil's nutrients to assist in the management of soil nutrients for farmed land.
[00033] In certain implementations, the data obtained by remote sensing include spectral information for the visible, near-infrared, and infrared parts of the electromagnetic spectrum. Artificial neural networks, support vector machines, and decision trees are some of the examples of the types of machine learning algorithms that may be used in various applications. The estimated levels of soil nutrients may, in certain implementations, be derived from the associations between remote sensing data and laboratory-measured values of soil nutrients. This can be the case, for example, if the data were collected in a laboratory.
[00034] In certain implementations, the soil nutrient map may be overlaid on a map of the cultivated land to provide a visual representation of the spatial distribution of soil nutrients. This can be accomplished by placing the soil nutrient map on top of the map of the cultivated land. The soil nutrient map that is created may, in some implementations, be used to direct the application of variable rate fertilisation. The soil nutrient map that is created may, in certain implementations, be used to pinpoint specific locations on the farmed land where more soil amendments might be necessary. It is possible that the remote sensing data might be obtained at a high spatial resolution in some implementations. This would allow for the provision of specific information on the nutrient variation throughout the cultivated area.
[00035] A system for remote sensing-based management of soil nutrients of cultivated land may also be included in embodiments of the present disclosure. This system may include the remote sensing platform 210 to acquire data related to soil, a data processing unit that is arranged to pre-process the remote sensing data, the application of machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels, and the generation of a soil nutrient map based on the estimated levels of soil nutrients. Embodiments of the remote computing device 212 that can be structured to show the soil nutrient map is another thing that might be included in embodiments to help framers out.
[00036] The remote sensing platform 210 that utilises sensors based on drones or satellites may also be included in certain embodiments of the present disclosure.
[00037] Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context
[00038] As used herein, the term “wireless communication network” or “network interface” refers to a network following any suitable wireless communication standards, such as LTE-Advanced (LTE-A), LTE, Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and so on. Furthermore, the communications between network devices in the wireless communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G) communication protocols, and/or any other protocols either currently known or to be developed in the future.
[00039] As used herein, the term “network device” refers to a device in a wireless communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. The “network device” or “terminal device” or “computing device” may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a terminal device access to the wireless communication network or to provide some service to a terminal device that has accessed the wireless communication network. The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, a tablet, a wearable device, a personal digital assistant (PDA), portable computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, wearable terminal devices, vehicle-mounted wireless terminal devices and the like. In the following description, the terms “terminal device”, “terminal”, “user equipment”, “computing device”, “network device” and “UE” may be used interchangeably.
[00040] Processing device may be provided by one or more processors such as a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00041] In addition, the present disclosure may also provide a memory containing the computer program as mentioned above, which includes machine-readable media and machine-readable transmission media. The machine-readable media may also be called computer-readable media, and may include machine-readable storage media, for example, magnetic disks, magnetic tape, optical disks, phase change memory, or an electronic memory terminal device like a random access memory (RAM), read only memory (ROM), flash memory devices, CD-ROM, DVD, Blue-ray disc and the like. The machine-readable transmission media may also be called a carrier, and may include, for example, electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals, and the like.
[00042] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00043] All references to “a/an/the element, apparatus, component, means, step, etc.” are to be interpreted as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The discussion above and below in respect of any of the aspects of the present disclosure is also in applicable parts relevant to any other aspect of the present disclosure.
[00044] The wordings such as “include”, “including”, “comprise” and “comprising” do not exclude elements or steps which are present but not listed in the description and the claims.
[00045] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
[00046] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors

Claims
I/We Claim:
1. A remote sensing method for management of soil nutrients of cultivated land, comprising the steps of:
collecting remote sensing data using satellite or drone-based sensors;
pre-processing the remote sensing data by filtering and normalizing it;
applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels;
generating a soil nutrient map based on the estimated levels of soil nutrients; and
providing farmers with the soil nutrient map to aid in the management of soil nutrients for cultivated land.

2. The method of claim 1, wherein the remote sensing data includes spectral data in the visible, near-infrared, and infrared regions.

3. The method of claim 1, wherein the machine learning algorithms is selected from artificial neural networks, support vector machines, and decision trees.

4. The method of claim 1, wherein the estimated levels of soil nutrients are based on the relationships between remote sensing data and laboratory-measured soil nutrient values.

5. The method of claim 1, wherein the soil nutrient map generated in step d) is overlaid on a map of the cultivated land to provide a visual representation of the spatial distribution of soil nutrients.

6. The method of claim 1, wherein the soil nutrient map generated is used to guide variable rate fertilization applications.

7. The method of claim 1, wherein the soil nutrient map generated is used to identify areas of the cultivated land that may require soil remediation.

8. The method of claim 1, wherein the remote sensing data is collected at a high spatial resolution to provide detailed information on soil nutrient variability across the cultivated land.

9. A system for remote sensing-based management of soil nutrients of cultivated land, comprising a remote sensing platform to acquire data related to soil, a data processing unit is arranged to pre-process the remote sensing data, applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels, and generating a soil nutrient map based on the estimated levels of soil nutrients; and a remote computing device is arranged to display the soil nutrient map to aid framers.

10. The system of claim 12, wherein the remote sensing platform includes satellite or drone-based sensors.
 

REMOTE SENSING METHOD FOR MANAGEMENT OF SOIL NUTRIENTS OF CULTIVATED LAND
Abstract
The current disclosure may comprise a remote sensing technique for managing the soil nutrients of farmed land. This method may involve the stages of gathering remote sensing data utilising sensors based on satellites or drones. In certain embodiments, preparing the data obtained through remote sensing may also include filtering and normalising the data. Estimating the amounts of soil nutrients may also be accomplished by applying machine learning algorithms to the results of pre-processed remote sensing data in certain embodiments. In certain implementations, one of the steps involves creating a soil nutrient map based on an estimation of the amounts of various soil nutrients. In certain embodiments, farmers may be given a map of the soil's nutrients to assist in the management of soil nutrients for farmed land. Here is one example of an embodiment.

Fig. 1
, Claims:Claims
I/We Claim:
1. A remote sensing method for management of soil nutrients of cultivated land, comprising the steps of:
collecting remote sensing data using satellite or drone-based sensors;
pre-processing the remote sensing data by filtering and normalizing it;
applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels;
generating a soil nutrient map based on the estimated levels of soil nutrients; and
providing farmers with the soil nutrient map to aid in the management of soil nutrients for cultivated land.

2. The method of claim 1, wherein the remote sensing data includes spectral data in the visible, near-infrared, and infrared regions.

3. The method of claim 1, wherein the machine learning algorithms is selected from artificial neural networks, support vector machines, and decision trees.

4. The method of claim 1, wherein the estimated levels of soil nutrients are based on the relationships between remote sensing data and laboratory-measured soil nutrient values.

5. The method of claim 1, wherein the soil nutrient map generated in step d) is overlaid on a map of the cultivated land to provide a visual representation of the spatial distribution of soil nutrients.

6. The method of claim 1, wherein the soil nutrient map generated is used to guide variable rate fertilization applications.

7. The method of claim 1, wherein the soil nutrient map generated is used to identify areas of the cultivated land that may require soil remediation.

8. The method of claim 1, wherein the remote sensing data is collected at a high spatial resolution to provide detailed information on soil nutrient variability across the cultivated land.

9. A system for remote sensing-based management of soil nutrients of cultivated land, comprising a remote sensing platform to acquire data related to soil, a data processing unit is arranged to pre-process the remote sensing data, applying machine learning algorithms to the pre-processed remote sensing data to estimate soil nutrient levels, and generating a soil nutrient map based on the estimated levels of soil nutrients; and a remote computing device is arranged to display the soil nutrient map to aid framers.

10. The system of claim 12, wherein the remote sensing platform includes satellite or drone-based sensors.