The present disclosure relates to the field of fertilization of
agricultural land. More specifically, the present invention relates to an apparatus for dispensing fertilizer in an intelligent way that is based on severity of disease detected in the agricultural land.
BACKGROUND
[0002] Background description includes information that may be useful in
understanding the present disclosure. It is not an admission that any of the
information provided herein is prior art or relevant to the presently claimed
disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] Crop pest control is a key link in determining crop yield and quality,
and it occupies a vital position in agricultural production. At present, the prevention and control of diseases in the crops in agricultural land is still at the stage of spraying fertilizers with manual operation. Manual spraying of fertilizers is time-consuming and labour-intensive, and is prone to uneven application of fertilizers and poisoning of pesticides problem. As the rural labour continues to decrease, agricultural production is becoming more and more convenient, digital and intelligent.
[0004] Accurate identification of crop diseases and pest is the key to
efficient prevention and controlling of crop diseases. The refined identification of crop diseases still relies on plant protection experts and professional technical personnel. Due to the constraints of time, space, and energy, a limited number of experts cannot provide timely guidance to each farmer. Possess the professional knowledge and skills to accurately identify pests and diseases, often identify pests based on experience, and control pests based on experience. The collection, analysis and application of agricultural data are of great significance to the transformation and upgrading of agriculture.
[0005] Prior approaches to fertilizer dispensing have been large, stand along
push dispensers, or tractor attachments, or attachments for lawn mowers, which dispense massive amounts of fertilizer over a large area. Even smaller, portable

units tend towards the volume method of fertilizer dispensation and have no means
to dispense controlled amount of the fertilizer based on disease found on the crop.
Therefore, an apparatus is needed to save fertilizer, by controlling dosage of
fertilizer dispensed at a location, an apparatus that is portable, and easy to operate,
that will enable the user to operate easily, and efficiently measuring the amounts of
fertilizer being applied to pre-selected areas, replacing large, space consuming,
volume spreaders which cannot supply metered amounts of fertilizer to small areas.
[0006] There is, therefore, a need in the art to provide an efficient solution
that can obviate the above mentioned limitations, and provides an equipment that assist the farmers to spray fertilizers in a controlled manner.
OBJECTS OF THE PRESENT DISCLOSURE
[0007] A general object of the present disclosure is to obviate the above-
mentioned problems and assists in fertilizing crops in a controlled manner.
[0008] Another object of the present disclosure is to provide an intelligent
fertilizer, to enable a user to apply a specified amount of fertilizer to a given area,
instead of over large areas where fertilizer might not be needed.
[0009] Another obj ect of the present disclosure is to provide a fertilizer that
enhance growth and quality of the crop.
[0010] Another object of the present disclosure is to provide fertilizer to
reduce wastage of the fertilizer.
SUMMARY
[0011] Various aspects of the present disclosure relates to the field of
fertilization of agricultural land, and in particular, the present invention relates to an apparatus for dispensing fertilizer in an intelligent way that is based on severity of disease detected in the agricultural land.
[0012] According to an aspect of the present disclosure, a fertilizer
apparatus is disclosed. The fertilizer apparatus can include a housing, an image capturing unit positioned on the housing that may be configured to acquire one or more images of a pre-defined area of an agricultural field, a container may be

coupled to the housing and configured to receive and store fluid, a dispensing unit
may be fluidically coupled to the container, and the dispensing unit may be
configured to dispense the fluid stored in the container, and a processing unit.
[0013] In an aspect, the processing unit may be operatively coupled with
the image capturing unit and the dispensing unit, the processing unit may
comprising one or more processors coupled with a memory, the memory storing
instructions executable by the one or more processors and may be configured to
receive the acquired one or more images, extracts one or more characteristics of
crop planted in the agricultural field from the images, compare the extracted
characteristics of the crop with a pre-defined set of characteristics of the crop;,
determine severity of one or more diseases on the crop planted in the pre-defined
area of the agricultural field, and activate the dispensing unit to dispense a pre¬
defined amount of the fluid based on the severity of one or more disease.
[0014] In an aspect, an inlet may be provided on the container that facilitates
in refilling the fluid.
[0015] In an aspect, the dispensing unit may include any or a combination
of nozzle, pipe, valve and sprayer.
[0016] In an aspect, the one or more characteristics may include any or a
combination of color and shape.
[0017] In an aspect, the one or more diseases may inclde any or a
combination of bacterial leak streak, leaf scald, bacterial blight, bakanae, brown
spot, false smut, cercospora leaf spot, blast, sheath blight, and grassy stunt.
[0018] In an aspect, the fluid may be selected from a group consisting of
Nitrogen (N), phosphorus (P), potassium (K), and pesticide.
[0019] In an aspect, a power source may be operatively coupled to the
image capturing unit, and the processing unit, and the power source may be configured to provide power supply.
[0020] Various objects, features, aspects and advantages of the inventive
subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings are included to provide a further
understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0022] FIG. 1 illustrates an exemplary block diagram of the proposed
system, in accordance with an exemplary embodiment of the present disclosure.
[0023] FIG. 2 illustrates an exemplary functional components of a
processing unit of the proposed system, in accordance with an exemplary embodiment of the present disclosure.
[0024] FIG. 3 illustrates an exemplary flowchart in order to explain its
working, in accordance with an exemplary embodiment of the present disclosure.
DETAILED DESCRIPTION
[0025] The following is a detailed description of embodiments of the
disclosure depicted in the accompanying drawings. The embodiments are in such details as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosures as defined by the appended claims.
[0026] Embodiments explained herein relate to the field of fertilization of
agricultural land. More specifically, embodiments of the present invention relates to an apparatus for dispensing fertilizer in an intelligent way that is based on severity of disease detected in the agricultural land.
[0027] Embodiments herein describe a fertilizer application apparatus that
dispense fertilizer in a controlled manner, i.e. dispense a controlled amount of fluid based on severity of disease on the crop, thus save fertilizer. The fertilizer can include an image capturing unit to capture images of crop in an agricultural field,

where the fertilized to be sprayed. The captured images can be analysed using learning techniques
[0028] FIG. 1 illustrates an exemplary block diagram of the proposed
system in accordance with an exemplary embodiment of the present disclosure.
[0029] As illustrated in FIG. 1, a fertilizer apparatus 100 for dispensing
controlled amount of fertilizer on a crop planted in the agricultural field is disclosed. The crop can be rice, wheat, maze, vegetables, and the likes. The fertilizer apparatus 100 can include a housing 102 that can be made of light weighted material such as plastic but not limited to like. The housing 102 can include an image capturing unit 104, a container 106, a dispensing unit 108, and a processing unit 110. The image capturing unit 104 can be a camera, webcam, and the likes, that can be positioned on the housing 102 such that when a user move in the agricultural field, the image capturing unit 104 can acquire one or more images (referred as images, herein) of the crop easily.
[0030] In an embodiment, the container 106 can be of any shape but not
limited to square, rectangle, cubical, and the likes. The container 106 can be coupled to the housing 102, and can be configured to receive and store fluid in the container 106. The fluid can be any fertilizer and pesticides such as Nitrogen (N), phosphorus (P), potassium (K), but not limited to likes. An inlet (not shown) can be provided on the container 106 that facilitates in refilling the fluid in the container 106, a cap can be used to cover and uncover the inlet to prevent spilling of the fluid from the container.
[0031] In an embodiment, the dispensing unit 108 can be fluidically
coupled to the container 106, and the dispensing unit 108 can be configured to dispense the fluid stored in the container 106 to the crop planted in the agricultural filed. The dispensing unit 108 can include any or a combination of nozzle, pipe, valve and sprayer, and the likes. The pipe can be coupled to an outlet of the container 106, and the valve can be controlled to dispense a pre-defined amount of the fluid, also sprayer can be positioned at one end of the pipe that can enable the user to spray the fertilizer on the crop easily.

[0032] In an embodiment, the processing unit 110 can be operatively
coupled with the image capturing unit 104 and the dispensing unit 108. The processing unit 110 can comprising on or more processors coupled with memory, the memory storing instructions executable by one or more processors. The processing unit 110 can be configured to receive the one or more images acquired or captured by the image capturing unit 104, and extract one or more characteristics of crop planted in the agricultural field from the images. The characteristics can be color and shape, but not limited to likes.
[0033] In an embodiment, the processing unit 110 can be further configured
to compare the extracted characteristics of the crop with a pre-defined set of characteristics of the crop and determine severity of one or more diseases on the crop planted in the pre-defined area of the agricultural field using one or more learning techniques such as convolutional neural network (CNN). The one or more diseases can include but not limited to bacterial leak streak, leaf scald, bacterial blight, bakanae, brown spot, false smut, cercospora leaf spot, blast, sheath blight, and grassy stunt.
[0034] In an exemplary embodiment, the fertilizer apparatus 100 can be
placed in vehicle and used, but for sites inaccessible by the vehicle, the user can use the device as a backpack. The user first remove the cap an pour the desired amount of the fertilizer ino the container 106. When the user walks in the agricultural field, images of crop are captured for example, the bacterial leak streak detected on the crop, the dispensing unit 108 can be activated to dispense a pre-defined amount of fertilizer on the crop. Similarly, the crop of another area can be detected in another images, and correspondingly the fluid can be dispensed, thus prevent wastage of the fertilizer.
[0035] In an embodiment, the apparatus 100 can include a power source
(not shown) that can contemplated including, but not limited to, rechargeable battery, NiCad battery, lithium (Li) ion cell, rechargeable cells, solar cell, solar battery, electrochemical cells, storage battery, secondary cell, etc. The power source can be operatively coupled to supply electricity to the image capturing unit 104, and the processing unit 110.

[0036] FIG. 2 illustrates an exemplary functional components of a
processing unit of the proposed apparatus, in accordance with an embodiment of the present disclosure.
[0037] As illustrated in FIG. 2, a processing unit 110 can include one or
more processor(s) 202. The one or more processor(s) 202 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) can be configured to fetch and execute computer-readable instructions stored in a memory of the processing unit 110. The memory 204 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 204 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0038] In an embodiment, the processing unit 110 can also include an
interface(s) 206. The interface(s) can include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the processing unit 110 with various devices coupled to the processing unit 110. The interface(s) 206 may also provide a communication pathway for one or more components of processing unit. Examples of such components include, but are not limited to, processing engine(s) and database.
[0039] In an embodiment, a processing engine(s) 208 can be implemented
as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may include a processing resource (for

example, one or more processors), to execute such instructions. In the present
examples, the machine-readable storage medium may store instructions that, when
executed by the processing resource, implement the processing engine(s) 208. In
such examples, the processing unit 110 can include the machine-readable storage
medium storing the instructions and the processing resource to execute the
instructions, or the machine-readable storage medium may be separate but
accessible to the processing unit 110 and the processing resource. In other
examples, the processing engine(s) may be implemented by electronic circuitry.
[0040] In an embodiment, the processing engine(s) 208 can include an
image processing and classification unit 212, a comparison unit 214, a signal generation unit 216, and other unit(s) 218. The other unit(s) 218 can implement functionalities that supplement applications or functions performed by the apparatus 100 or the processing engine(s) 208.
[0041] In an embodiment, a database 210 can include data that is either
stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208.
[0042] It would be appreciated that units being described are only
exemplary units and any other unit or sub-unit may be included as part of the apparatus 100. These units too may be merged or divided into super- units or sub-units as may be configured.
[0043] In an embodiment, the processing engine(s) 208 can be configured
to receive one or more images of a pre-defined area of an agricultural field in an electric form, and further transmits the received images to the image processing and classification unit 212. The image processing and classification unit 212 can be configured to extract one or more characteristics such as color, shape, and etc. of crop planted in the agricultural field from the images by applying one or more learning techniques.
[0044] In an embodiment, the one or more learning techniques can include
deep learning algorithms, but not limited to such as support vector machines, decision trees, artificial neural networks, and convolutional neural networks (CNN).

[0045] In another embodiment, the image processing and classification unit
212 can be trained and updated based on the received images. A deep leaning model
can be trained based on the received images to determine severity of disease on the
crop, where the deep leaning model can be stored in the database 210. In yet another
embodiment, once the database 210 is trained correctly, a deep learning algorithm
can be used to perform repetitive, and routine tasks within a shorter period of time.
[0046] In an embodiment, the comparison unit 214 can be configured to
compare the extracted characteristics of the crop with a pre-defined set of
characteristics of the crop. Upon detection of severity of one or more diseases on
the crop, the signal generation unit 216 can be configured to transmit a signal to a
dispensing unit to get activated. In addition, the signal pertain information of
amount of fluid to be dispensed from the dispensing unit 108.
[0047] In an embodiment, upon receiving the signal the dispensing unit can
be activated, i.e. a valve can be opened to dispense the fluid i.e. fertiliser from a container 106 through a pipe on the detected crop.
[0048] In an embodiment, the processing unit 110 can be configured to
further transmit the received images in machine readable form or binary form to the image processing and classification unit 212, where the one or more features can be extracted and classified to detect type of leakage, and the size of leakage. Further, the image processing and classification unit 212 can be updated and trained based on extracted features
[0049] FIG. 3 illustrates an exemplary flowchart in order to explain its
working, in accordance with an exemplary embodiment of the present disclosure.
[0050] With reference to FIG. 3, at step 304, one or more images
(collectively referred as images, herein) of a pre-defined area of an agricultural field
can be acquired by an image capturing unit 104 such as camera.
[0051] At step 306, the captured images can be transmitted to a processing
unit 110. The processing unit 110 can be configured to analyse the received images using one or more learning techniques such as convolutional neural networks (CNN). The processing unit 110 can be configured to extracts one or more characteristics from the images such as color, and shape of the crop planted in the

agricultural field and compare the extracted characteristics with a pre-defined set of characteristics.
[0052] At step 308, determine severity of one or more diseases on the crop
planted in the pre-defined area of the agricultural field by applying learning techniques such as convolutional neural networks (CNN) The one or more diseases can include but not limited to of bacterial leak streak, leaf scald, bacterial blight, bakanae, brown spot, false smut, cercospora leaf spot, blast, sheath blight, and grassy stunt.
[0053] At step 310, a dispensing unit 108 can be activated by the processing
unit 110 to dispense fluid (i.e. fertilizer) from the container 106 on the crop planted
in the pre-defined area of the agricultural field in a controlled manner. For example,
several diseases and pests are found on the crop, the processing unit 110 can activate
the dispensing unit 108 to dispense large amount of fertilizer on the crop.
[0054] At step 312, the process is retuned back to main routine to acquire
images of the crop in another area of the agricultural field.
[0055] The foregoing discussion discloses and describes merely exemplary
embodiments of the present disclosure. As will be understood by those skilled in the art, the present disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Accordingly, the present disclosure is intended to be illustrative and not limiting thereof. The disclosure, including any readily discernible variants of the teachings herein, defines in part, the scope of the foregoing claim terminology.
ADVANTAGES OF THE PRESENT INVENTION
[0056] The present invention provides an intelligent fertilizer.
[0057] The present invention provides a fertilizer for spraying fertilizer on
crops in a controlled manner.
[0058] The present invention provides an intelligent fertilizer that enable a
user to apply a specified amount of fertilizer to a given area, instead of over large
areas where fertilizer might not be needed.

[0059] The present invention provides a fertilizer that enhance growth and
quality of the crop.
[0060] The present invention provides a fertilizer to reduce wastage of the
fertilizer.

We Claim:

1. A fertilizer apparatus 100 comprising:
a housing 102;
an image capturing unit 104 positioned on the housing and configured to acquire one or more images of a pre-defined area of an agricultural field;
a container 106 coupled to the housing and configured to receive and store fluid;
a dispensing unit 108 fluidically coupled to the container, wherein the dispensing unit configured to dispense the fluid stored in the container; and
a processing unit 110 operatively coupled with the image capturing unit and the dispensing unit, the processing unit comprising one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors and configured to: receive the acquired one or more images; extracts one or more characteristics of crop planted in the agricultural field from the images;
compare the extracted characteristics of the crop with a pre-defined set of characteristics of the crop;
determine severity of one or more diseases on the crop planted in the pre-defined area of the agricultural field; and
activate the dispensing unit to dispense a pre-defined amount of the fluid based on the severity of one or more disease.
2. The fertilizer apparatus as claimed in claim 1, wherein an inlet is provided on the container that facilitates in refilling the fluid.
3. The fertilizer apparatus as claimed in claim 1, wherein the dispensing unit 108 comprises any or a combination of nozzle, pipe, valve and sprayer.
4. The fertilizer apparatus as claimed in claim 1, wherein the one or more characteristics comprises any or a combination of color and shape.

5. The fertilizer apparatus as claimed in claim 1, wherein the one or more diseases comprises any or a combination of bacterial leak streak, leaf scald, bacterial blight, bakanae, brown spot, false smut, cercospora leaf spot, blast, sheath blight, and grassy stunt.
6. The fertilizer apparatus as claimed in claim 1, wherein the fluid is selected from a group consisting of Nitrogen (N), phosphorus (P), potassium (K), and pesticide.
7. The fertilizer apparatus as claimed in claim 1, wherein a power source operatively coupled to the image capturing unit 104, and the processing unit 110, wherein the power source is configured to provide power supply.