The present disclosure relates to crop yield estimation techniques. In
particular, the present disclosure relates to impact of natural calamities such as flood, drought etc. on crop yield. More particularly, the present disclosure relates to systems and method for mapping crop yield.
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] It has been observed by scientists that since population of humans is increasing
on the planet, so is their impact on the nature or environment of the planet earth. Scientists are able to relate human pollution increase with pollution i.e. the increase in human population is directly related to pollution impact on the environment.
[0004] During last two decades there has been increase in frequency of occurrences of
natural hazards (such as drought and flood during Kharif season and abnormally rise in temperature and fall in temperature during Rabi season) under the backdrop of climate change. This erratic weather pattern is causing large scale damages to the crops frequently across the India. Hence, in order to assess quick assessment of crop yield loss triggered by abovementioned hazards there is need of location and crop specific readymade damage functions/vulnerability curves. This will help the relevant stakeholders to immediately estimate the crop yield loss well in advance of crop harvesting in the event of happening of these hazards during the crop growing cycle. Keeping this in mind and demand of this kind of products from the various stakeholders (particularly from the insurance and reinsurance companies), RMSI Cropalytics developed location and crop specific damage/vulnerability functions for all major crops being grown in respective district for both crop growing seasons (i.e., Kharif and Rabi). While developing the damage functions we analysed the long time series historical crop yield and weather data thoroughly and rigorously for each and every crop of a particular district and subsequently applied the best fit curve between the polynomial and logarithmic to finally develop the curve.

[0005] Efforts have been made in the past to overcome problem associated with
estimation of crop yield but the accuracy of these techniques was very low owing to the fact that these techniques require manually observing the parameters and predicting based on human experience. However, these techniques are less accurate and require human intervention and hence the error in predicting is high.
[0006] There is, therefore, a need in the art to provide a crop yield estimation
techniques that seeks to overcome or at least ameliorate one or more of the above-mentioned problems and other limitations of the existing solutions and utilize techniques, which are robust, accurate, fast, efficient, cost effective and simple.
OBJECTS OF THE PRESENT DISCLOSURE
[0007] Some of the objects of the present disclosure, which at least one embodiment
herein satisfies are as listed herein below.
[0008] It is an object of the present disclosure to provide system and method for
mapping crop yield.
[0009] It is another object of the present disclosure to provide system and method for
mapping crop yield that is cost effective and easy to implement.
[0010] It is another object of the present disclosure to provide system and method for
mapping crop yield that is highly accurate as the estimation technique require determining
impact of weather parameters on yield during various stages growth of the crop.
[0011] It is another object of the present disclosure to provide system and method for
mapping crop yield that helps predicting an aggregate yield of the larger area like a district, a
state, a country, a continent etc. to help estimating impact on economy of that area.
[0012] It is yet another object of the present disclosure to provide system and method
for mapping crop yield that helps estimating an overall loss and/or gain on the crop yield
based on weather/climatic conditions.
SUMMARY
[0013] The present disclosure relates to crop yield estimation techniques. In
particular, the present disclosure relates to impact of natural calamities such as flood, drought, etc. on crop yield. More particularly, the present disclosure relates to systems and method for mapping crop yield.

[0014] An aspect of the present disclosure provides a method for mapping crop yield,
said method comprises: capturing, by a set of sensors configured at one or more geographic
locations that forms part of a map of an area of interest, one or more attributes associated
with weather of the area of interest; identifying, by a location identifier operatively coupled
with the set of sensors, geographic location of the area of interest; and determining, by one or
more processors of a control unit operatively coupled with the set of sensors and the location
identifier, crop yield variables from the sensed one or more attributes relating to crop
production of the area of interest which together characterize yield of the area of interest;
estimating, by the one or more processors, values of the determined yield variables of the
area of interest by comparing the determined yield variables with a dataset comprising a set
of predefined variables pertaining to one or more attributes, wherein a yield map, stored on a
first database, of the area of interest is updated based on the estimated yield of the area of
interest and the identified geographic location, wherein based on the updated yield map the
control unit is configured to estimate an average yield of the area of interest of the map.
[0015] In an aspect, the method comprises repeating of the capturing of the one or
more attributes after a gap of first predefined time period.
[0016] In an aspect, the one or more crop yield variables comprises stage of growth of
crop, impact of rainfall on the crop based on the stage of growth of crop.
[0017] In an aspect, the stage of growth of crop comprises vegetative stage, flowering
stage, grain filling stage and ripening stage.
[0018] In an aspect, the impact of rainfall and temperature are determined based on
type of crop being yielded.
[0019] In an aspect, the type of crop is selected from a group comprising of any or a
combination of Kharif and Rabi crops.
[0020] In an aspect, the captured one or more attributes are stored on any or a
combination of the first database and a second database.
[0021] In an aspect, the one or more attributes comprises rainfall, pattern of rainfall,
temperature, weather and climate.
[0022] In an aspect, the method comprises determining an aggregate yield of crops of
one or more area of interests.
[0023] Another aspect of the present disclosure provides a crop yield mapping
system, said system comprising: a set of sensors configured at one or more geographic
locations that forms part of a map of an area of interest, and the set of sensors configured to

capture one or more attributes associated with weather of the area of interest; a location identifier operatively coupled with the set of sensors to identify geographic location of the area of interest; and a control unit operatively coupled to the set of sensors and the location identifier, the control unit comprises one or more processors, the one or more processors coupled with a memory storing instructions executable by the one or more processors to: determine crop yield variables from the sensed one or more attributes relating to crop production of the area of interest which together characterize yield of the area of interest; estimate values of the determined yield variables of the area of interest by comparing the determined yield variables with a dataset comprising a set of predefined variables pertaining to the one or more attributes, wherein a yield map, stored on a first database, of the area of interest is updated based on the estimated yield of the area of interest and the identified geographic location, wherein based on the updated yield map the control unit is configured to estimate an average yield of the area of interest of the map.
BREIF DESCRIPTION OF THE DRAWINGS
[0024] In the figures, similar components and/or features may have the same
reference label. Further, various components of the same type may be distinguished by
following the reference label with a second label that distinguishes among the similar
components. If only the first reference label is used in the specification, the description is
applicable to any one of the similar components having the same first reference label
irrespective of the second reference label.
[0025] FIG. 1 illustrates an exemplary network architecture in which or with which
proposed system can be implemented in accordance with an embodiment of the present
disclosure.
[0026] FIG. 2 illustrates an exemplary module diagram for mapping traffic in
accordance with an embodiment of the present disclosure.
[0027] FIG. 3 is a flow diagram illustrating a process for mapping crop yield in
accordance with an embodiment of the present disclosure.
[0028] FIG. 4 illustrates an exemplary computer system in which or with which
embodiments of the present invention can be utilized in accordance with embodiments of the
present disclosure.

DETAILED DESCRIPTION
[0029] In the following description, numerous specific details are set forth in order to
provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0030] Embodiments of the present invention include various steps, which will be
described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, firmware and/or by human operators.
[0031] Embodiments of the present invention may be provided as a computer
program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).
[0032] Various methods described herein may be practiced by combining one or more
machine-readable storage media containing the code according to the present invention with
appropriate standard computer hardware to execute the code contained therein. An apparatus
for practicing various embodiments of the present invention may involve one or more
computers (or one or more processors within a single computer) and storage systems
containing or having network access to computer program(s) coded in accordance with
various methods described herein, and the method steps of the invention could be
accomplished by modules, routines, subroutines, or subparts of a computer program product.
[0033] If the specification states a component or feature "may", "can", "could", or
"might" be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.

[0034] As used in the description herein and throughout the claims that follow, the
meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0035] Exemplary embodiments will now be described more fully hereinafter with
reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this invention will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
[0036] While embodiments of the present invention have been illustrated and
described, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the invention, as described in the claim.
[0037] The present disclosure relates to crop yield estimation techniques. In
particular, the present disclosure relates to impact of natural calamities such as flood, drought etc. on crop yield. More particularly, the present disclosure relates to systems and method for mapping crop yield.
[0038] An aspect of the present disclosure provides a method for mapping crop yield,
said method comprises: capturing, by a set of sensors configured at one or more geographic locations that forms part of a map of an area of interest, one or more attributes associated with weather of the area of interest; identifying, by a location identifier operatively coupled with the set of sensors, geographic location of the area of interest; and determining, by one or more processors of a control unit operatively coupled with the set of sensors and the location identifier, crop yield variables from the sensed one or more attributes relating to crop production of the area of interest which together characterize yield of the area of interest; estimating, by the one or more processors, values of the determined yield variables of the area of interest by comparing the determined yield variables with a dataset comprising a set

of predefined variables pertaining to one or more attributes, wherein a yield map, stored on a
first database, of the area of interest is updated based on the estimated yield of the area of
interest and the identified geographic location, wherein based on the updated yield map the
control unit is configured to estimate an average yield of the area of interest of the map.
[0039] In an aspect, the method comprises repeating of the capturing of the one or
more attributes after a gap of first predefined time period.
[0040] In an aspect, the one or more crop yield variables comprises stage of growth of
crop, impact of rainfall on the crop based on the stage of growth of crop.
[0041] In an aspect, the stage of growth of crop comprises vegetative stage, flowering
stage, grain filling stage and ripening stage.
[0042] In an aspect, the impact of rainfall is determined based on type of crop being
yielded.
[0043] In an aspect, the type of crop is selected from a group comprising of any or a
combination of kharif and rabi crops.
[0044] In an aspect, the captured one or more attributes are stored on any or a
combination of the first database and a second database.
[0045] In an aspect, the one or more attributes comprises rainfall, pattern of rainfall,
temperature, weather and climate.
[0046] In an aspect, the method comprises determining an aggregate yield of crops of
one or more area of interests.
[0047] Another aspect of the present disclosure provides a crop yield mapping
system, said system comprising: a set of sensors configured at one or more geographic
locations that forms part of a map of an area of interest, and the set of sensors configured to
capture one or more attributes associated with weather of the area of interest; a location
identifier operatively coupled with the set of sensors to identify geographic location of the
area of interest; and a control unit operatively coupled to the set of sensors and the location
identifier, the control unit comprises one or more processors, the one or more processors
coupled with a memory storing instructions executable by the one or more processors to:
determine crop yield variables from the sensed one or more attributes relating to crop
production of the area of interest which together characterize yield of the area of interest;
estimate values of the determined yield variables of the area of interest by comparing the
determined yield variables with a dataset comprising a set of predefined variables pertaining
to the one or more attributes, wherein a yield map, stored on a first database, of the area of

interest is updated based on the estimated yield of the area of interest and the identified
geographic location, wherein based on the updated yield map the control unit is configured to
estimate an average yield of the area of interest of the map.
[0048] FIG. 1 illustrates an exemplary network architecture in which or with which
proposed system can be implemented in accordance with an embodiment of the present
disclosure.
[0049] In an embodiment, network architecture 100 can include a system 102. The
system 102 can be implemented in any computing device and can be configured/operatively
connected with a server 110. The system 102 can be operatively coupled with a plurality of
set of sensors 106-1, 106-2 106-2 (collectively referred to as pluralities of set of sensors
106 herein and individually referred to as pluralities of set of sensors 106 herein) through a network 104. The plurality of set of sensors 106 can be operatively coupled with location identifiers 108-1, 108-2....108-N (collectively referred to as location identifiers 108 herein and individually referred to as location identifier 108 herein).
[0050] Further, the network 104 can be a wireless network, a wired network or a
combination thereof. The network 104 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, Wi-Fi, LTE network, CDMA network, and the like. Further, the network 104 can either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 104 can include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0051] In an embodiment, users can register themselves directly with the system 102
using any or a combination of a mobile number, date of birth, place of birth, first name and last name, a biometric or any other such unique identifier-based input. On successful registration, the user can be provided with a user name and password which can be used for accessing the system 102 for providing information.
[0052] In an embodiment, the system 102 can be operatively coupled with the server
108. Further, the server 110 can be used for storing a diagrammatic representation of an area of land or sea showing physical features, cities, roads, etc. in form of a map of the area.

[0053] In an embodiment, the system 102 can access the map of an area of interest
that can further be accessed by the users on their respective computing devices. In an
embodiment, the system 102 can enable the users to access the map of the area of interest.
[0054] In an embodiment, the set of sensors 106 can be configured at one or more
geographic locations that forms part of the map of an area of interest. The set of sensors 106 can be configured to capture one or more attributes associated with weather of the area of interest. The one or more attributes comprises rainfall, pattern of rainfall, temperature, weather and climate.
[0055] In an embodiment, corresponding location identifier 108 can be operatively
coupled with the set of sensors to identify geographic location of the area of interest.
[0056] In an embodiment, the system 102 can be configured to determine crop yield
variables from the sensed one or more attributes relating to crop production of the area of interest which together characterize yield of the area of interest. The crop yield variables can include but not limited to stage of growth of crop, impact of rainfall and temperature on the crop based on the stage of growth of crop. Further, the system 102 configured to estimate values of the determined yield variables of the area of interest by comparing the determined yield variables with a dataset comprising a set of predefined variables pertaining to the one or more attributes, wherein a yield map, stored on a first database, of the area of interest is updated based on the estimated yield of the area of interest and the identified geographic location, wherein based on the updated yield map the control unit is configured to estimate an average yield of the area of interest of the map.
[0057] FIG. 2 illustrates an exemplary module diagram for mapping traffic in
accordance with an embodiment of the present disclosure.
[0058] In an aspect, module diagram 200 of the system 102 may comprise one or
more processor(s) 202. The one or more processor(s) 202 may 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) 202 are configured to fetch and execute computer-readable instructions stored in a memory 206 of the system 102. The memory 206 may 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 206 may comprise 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.
[0059] The system 102 may also comprise an interface(s) 204. The interface(s) 204
may comprise 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) 204 may
facilitate communication of system 102. The interface(s) 204 may also provide a
communication pathway for one or more components of the system 102. Examples of such
components include, but are not limited to, processing engine(s) 208 and data 210.
[0060] The processing engine(s) 208 may 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 comprise 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 system 102 may comprise 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 system 102 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry.
[0061] The data 210 may comprise data that is either stored or generated as a result of
functionalities implemented by any of the components of the processing engine(s) 208 or the system 102.
[0062] In an exemplary embodiment, the processing engine(s) 208 may include an
attributes capturing engine 212, a weather determination engine 214, a yield map updating engine 216, and a yield estimation engine 218 and other engine(s) 220.
[0063] In an embodiment, the attributes capturing engine 212 can be configured to
capture one or more attributes associated with weather of the area of interest. The one or more attributes comprises rainfall, pattern of rainfall, temperature, weather and climate. Further, the attributes capturing engine 212 can further be configured to receive geographic location of the area of interest.

[0064] In an embodiment, the captured one or more attributes of the area of interest
can be stored on a database (not shown). Further, the process of capturing of the one or more
attributes can be repeated periodically after a first predefined time period. In an embodiment,
the captured one or more attributes can be associated with a time stamp.
[0065] In an embodiment, the weather determination engine 214 can be configured to
determine the weather and/or climate conditions based on the received one or more attributes associated with weather of the area of interest. In an embodiment, the weather determination engine 214 can be configured for analysing the stored one or more attributes and based on the analysis determine various crop yield variables.
[0066] In an embodiment, the weather determination engine 214 based on analysis of
the stored one or more attributes with time and date stamp can be used for estimating various
weather phenomenon such as drought, flood and the like. Further, the analysis can be used for
preparing a weather data that is time and the one or more attributes dependent.
[0067] In an embodiment, a yield estimation engine 218 can be used for estimating
the yield of the area of interest based on the determined one or more crop yield variables. The biggest challenge to develop crop yield loss assessment statistical model is to get quality long time series historical weather and crop yield data as longer the quality historical data better the model can be developed. Another challenge to develop robust damage function is to have in-house experts with sound practical field knowledge of different agro-climatic regions of India as experts' knowledge and judgments are also required while developing the damage functions using historical crop and weather data.
[0068] As mentioned in the previous point there is two challenges while developing
the damage functions (i.e., availability of quality long time series historical crop and weather data and field knowledge). To the greater extent RMSI overcame these limitations as we are in the field of assessment of agriculture sector's vulnerability to various weather/climate shocks over the last more than one decade. And while implementing the projects related to weather/climate risk assessment we collected agricultural and weather data and made archive of these data.
Development of model for deficit water hazard (i.e., drought) for Kharif season
[0069] As a first step of this module is to source location wise long time series
weather data, crop data, and past climatic hazards' events from the concerned agencies such as India Meteorological Department (EVID), state government agricultural departments, and

central agricultural department. Subsequently all the collected data are cleaned and filled the missing values (if exist in the raw data) using standard methodology.
[0070] Once data are cleaned and filled drought hazard is quantified in terms of
different severity level and frequency of occurrence of all quantified severity levels using daily time rainfall data. It is to note that drought hazard quantification process is the computation of extent of shortfall in rainfall with respect to normal rainfall of a particular location. As extent of water deficit impact is not same at different crop growth stages if same level of water stress happens at different stages, analysis is carried out separately for four critical crop growth phases (i.e., vegetative stage, flowering stage, grain filling stage, and ripening stage). Finally outcome of four stages is integrated together by applying appropriate weighting factor for each stage applicable for the drought hazard. For example, maximum water stress impact happens when there is water deficit at flowering stage so maximum weighting factor is applied at this stage whereas least impact happens at ripening stage hence lower weighting factor is applied at ripening stage.
[0071] Similarly crop and location wise Mean Damage Ratio (MDR) is computed
using long time series crop yield data by applying following formula:
(Reference year yield — Normal yield)
MDR = -— —
Normal yield
[0072] Once hazard quantification and MDR calculation are done next step is to
develop crop yield loss (i.e., MDR) vs_ water deficit model for each crop and each district of
India by applying best fit curve (e.g., logarithmic regression and polynomial regression) and
subsequently crop and location specific damage function is developed against this hazard.
One of examples of water deficit hazard damage functions is y = -44.15xA2 + 26.62x + -3.44;
where "y" is the mean damage ratio of the crop and "x" is the deficit water severity level.
This exercise was carried out using 20 years of district level historical crop data and daily
time series historical rainfall data corresponding to the period for which crop data was
available. Secondly frequency and severity analysis of this hazard using long time series daily
rainfall data (115 years) is conducted to assign a rate of occurrence to different severity
events. Finally crop and location specific return period loss and Average Annual Loss (AAL)
are computed using internationally standard loss modelling technique in conjunction with the
damage function and 115 years of daily time series historical rainfall data.

Development of model for excess water hazard for crop yield loss assessment Kharif season
[0073] As discussed in the drought model, first step of this module is also to source
the required followed by data cleaning and filling, quantification of excess water hazard, and
calculation of MDR using the same approach explained in the drought hazard section. It is to
note that excess water hazard quantification process is the computation of extent of excess
rainfall with respect to normal rainfall of a particular location. Similar to drought hazard,
analysis is carried out at all four critical crop growth stages separately and finally integrated
together by applying appropriate weighting factor applicable for excess water hazard.
[0074] Once excess water hazard is quantified, crop yield loss (i.e., MDR) vs excess
water is modelled for each crop and each district of India by applying best fit curve (e.g., logarithmic regression and polynomial regression) and subsequently crop and location specific damage function is developed against this hazard. One of examples of excess water hazard damage functions is y = -7.0518x + 3.562x - 0.0658; where "y" is the mean damage ratio of the crop and "x" is the excess water severity level]. This exercise was carried out using 20 years of district level historical crop data and daily time series historical rainfall data corresponding to the period for which crop data was available. Secondly frequency and severity analysis is conducted to assign a rate of occurrence to all severity levels of excess water events. Finally crop and location-wise specific return period loss and AAL are computed using internationally standard loss modelling technique in conjunction with the damage function and 115 years of daily time series historical rainfall data. Development of model for excess heat unit hazard for crop yield loss assessment for Rabi season
[0075] The Rabi crop production during winter in India is modulated by maximum
temperature (Tmax) and minimum temperature (Tm;n). Overall impact analysis indicates that the productivity of different Rabi crops across India is influenced by variability in local maximum and minimum temperatures1. Interestingly, studies suggest that the precipitation during this season (December-March) is negatively correlated at 90% confidence level with the yield of total Rabi crops meaning thereby that the role of winter rainfall is insignificant in the productivity of Rabi crops in India. For example either at very low temperature or at very high temperature as compared to normal Tm;n and Tmax crop does not grow well due to impediment in the crops' normal biochemical reaction where water is not a limiting factor

which is true in case of Rabi season crop. Hence, we considered temperature as a main climatic hazard for the Rabi season crops.
[0076] As a first step of this module, daily temperature is converted in to crop and
location specific crop heat unit [also called growing degree days (GDDs)] in order to quantify the excess crop heat unit (CHU) hazard using following algorithm:
'/Maximum temperature 4- Minimum temperatureODD = [( )
— Crop base temperature}
The reason for using CHU instead of directly using temperature is that CHU is specific to a particular crop and there is a direct relationship between growth and development of plant and total accumulated heat unit in the plant.
As base temperature of a particular crop is not fixed throughout the growing and it varies
with growing stage, CHU is calculated separately for four critical crop growth phases (i.e.,
vegetative stage, flowering stage, grain filling stage, and ripening stage) by applying
appropriate base temperature and finally aggregated the CHU of the four stages together. For
example, maximum water stress impact happens when there is water deficit at flowering
stage so maximum weighting factor is applied at this stage whereas least impact happens at
ripening stage hence lower weighting factor is applied at ripening stage. It is to note that
neither excess nor deficit CHU is beneficial for crop growth and development.
[0077] Once excess CHU quantification is done next step is to develop crop yield loss
vs excess CHU model for each crop and each district of India by applying best fit curve (e.g., logarithmic regression and polynomial regression) and subsequently crop and location specific damage function is developed against this hazard. One of examples of excess CHU damage functions is y = 34.35xA2 + -1.5x + 0.07; where "y" is the mean damage ratio of the crop and "x" is the excess CHU. This exercise was carried out using 20 years of district level historical crop data and daily time series historical Tm;n and Tmax data corresponding to the period for which crop data was available. Secondly frequency and severity analysis of this hazard using long time series daily Tm;n and Tmax data (64 years) is conducted to assign a rate of occurrence to different severity events of excess CHU. Finally crop and location specific return period loss and AAL are computed using internationally standard loss modelling technique in conjunction with the damage function and 64 years of daily time series historical Tmin and Tmax data.

Development of model for deficit heat unit hazard for crop yield loss assessment for Rabi season
[0078] As discussed in the excess CHU hazard model, first step of this module is also
to quantify the deficit CHU hazard using the same approach explained in the excess CHU hazard section. Similar to excess CHU hazard, analysis is carried out at all four critical crop growth stages separately and finally aggregated together.
[0079] Once deficit CHU quantification is done next step is to develop crop yield loss
vs deficit CHU model for each crop and each district of India by applying best fit curve (e.g., logarithmic regression and polynomial regression) and subsequently crop and location specific damage function is developed against this hazard. One of examples of deficit CHU damage functions is y = 34.22xA2 + -2.36x + 0.06; where "y" is the mean damage ratio of the crop and "x" is the deficit CHU. This exercise was carried out using 18 years of district level historical crop data and daily time series historical Tmin and Tmax data corresponding to the period for which crop data was available. Secondly frequency and severity analysis of this hazard using long time series daily Tmm and Tmax data (64 years) is conducted to assign a rate of occurrence to different severity events of deficit CHU. Finally crop and location specific return period loss and AAL are computed using internationally standard loss modelling technique in conjunction with the damage function and 64 years of daily time series historical Tmin and Tmax data.
[0080] In an embodiment, the yield map updating engine 212 can be configured to
update the yield map based on the determined or estimated yield of the area of interest. In another embodiment, the area of interest can be a local farm land, a district, a state and a country etc. in yet another embodiment, the yield map can be aggregate of the plurality of the area of interests.
[0081] FIG. 3 is a flow diagram illustrating a process for mapping crop yield in
accordance with an embodiment of the present disclosure.
[0082] In an aspect, the proposed method may be described in general context of
computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method can also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a

distributed computing environment, computer executable instructions may be located in both
local and remote computer storage media, including memory storage devices.
[0083] The order in which the method as described is not intended to be construed as
a limitation, and any number of the described method blocks may be combined in any order to implement the method or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above described system.
[0084] In context of flow diagram 300, block 302 pertains to capturing, by a set of
sensors configured at one or more geographic locations that forms part of a map of an area of
interest, one or more attributes associated with weather of the area of interest.
[0085] Further, block 304 pertains to identifying, by a location identifier operatively
coupled with the set of sensors, geographic location of the area of interest. Furthermore, block 306 pertains to determining, by one or more processors of a control unit operatively coupled with the set of sensors and the location identifier, crop yield variables from the sensed one or more attributes relating to crop production of the area of interest which together characterize yield of the area of interest. In response to determined crop yield variables block 308 pertains to estimating, by the one or more processors, values of the determined yield variables of the area of interest by comparing the determined yield variables with a dataset comprising a set of predefined variables pertaining to one or more attributes, wherein a yield map, stored on a database, of the area of interest is updated based on the estimated yield of the area of interest and the identified geographic location, wherein based on the updated yield map the control unit is configured to estimate an average yield of the area of interest of the map.
[0086] FIG. 4 illustrates an exemplary computer system in which or with which
embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[0087] As shown in FIG. 4, computer system 400 can include an external storage
device 410, a bus 420, a main memory 430, a read only memory 440, a mass storage device 450, communication port 460, and a processor 470. A person skilled in the art will appreciate that computer system may include more than one processor and communication ports.

Examples of processor 470 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on a chip processors or other future processors. Processor 470 may include various modules associated with embodiments of the present invention. Communication port 460 can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port 460 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0088] Memory 430 can be Random Access Memory (RAM), or any other dynamic
storage device commonly known in the art. Read only memory 440 can be any static storage
device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for
storing static information e.g., start-up or BIOS instructions for processor 470. Mass storage
450 may be any current or future mass storage solution, which can be used to store
information and/or instructions. Exemplary mass storage solutions include, but are not
limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced
Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external,
e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from
Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar
7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage,
e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill
Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0089] Bus 420 communicatively couples processor(s) 470 with the other memory,
storage and communication blocks. Bus 420 can be, e.g. a Peripheral Component
Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI),
USB or the like, for connecting expansion cards, drives and other subsystems as well as other
buses, such a front side bus (FSB), which connects processor 470 to software system.
[0090] Optionally, operator and administrative interfaces, e.g. a display, keyboard,
and a cursor control device, may also be coupled to bus 420 to support direct operator interaction with computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 460. External storage device 410 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-

RW), Digital Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0091] Embodiments of the present disclosure may be implemented entirely
hardware, entirely software (including firmware, resident software, micro-code, etc.) or
combining software and hardware implementation that may all generally be referred to herein
as a "circuit," "module," "component," or "system." Furthermore, aspects of the present
disclosure may take the form of a computer program product comprising one or more
computer readable media having computer readable program code embodied thereon.
[0092] Thus, it will be appreciated by those of ordinary skill in the art that the
diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named.
[0093] As used herein, and unless the context dictates otherwise, the term "coupled
to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean "communicatively coupled with" over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.
[0094] It should be apparent to those skilled in the art that many more modifications
besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all

terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C .... and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0095] While the foregoing describes various embodiments of the invention, other and
further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
ADVANTAGES OF THE PRESENT DISCLOSURE
[0096] The present disclosure provides system and method for mapping crop yield.
[0097] The present disclosure provides system and method for mapping crop yield
that is cost effective and easy to implement.
[0098] The present disclosure provides system and method for mapping crop yield
that is highly accurate as the estimation technique require determining impact of weather
parameters on yield during various stages growth of the crop.
[0099] The present disclosure provides system and method for mapping crop yield
that helps predicting an aggregate yield of the larger area like a district, a state, a country, a
continent etc. to help estimating impact on economy of that area.
[00100] The present disclosure provides system and method for mapping crop yield
that helps estimating an overall loss and/or gain on the crop yield based on weather/climatic
conditions.

We Claim:

1.A method for mapping crop yield, said method comprises:
capturing, by a set of sensors configured at one or more geographic locations that forms part of a map of an area of interest, one or more attributes associated with weather of the area of interest;
identifying, by a location identifier operatively coupled with the set of sensors, geographic location of the area of interest; and
determining, by one or more processors of a control unit operatively coupled with the set of sensors and the location identifier, crop yield variables from the sensed one or more attributes relating to crop production of the area of interest which together characterize yield of the area of interest;
estimating, by the one or more processors, values of the determined yield variables of the area of interest by comparing the determined yield variables with a dataset comprising a set of predefined variables pertaining to one or more attributes, wherein a yield map, stored on a first database, of the area of interest is updated based on the estimated yield of the area of interest and the identified geographic location, wherein based on the updated yield map the control unit is configured to estimate an average yield of the area of interest of the map.
2. The method as claimed in claim 1, wherein the method comprises repeating of the capturing of the one or more attributes after a gap of first predefined time period.
3. The method as claimed in claim 1, wherein the one or more crop yield variables comprises stage of growth of crop, impact of rainfall and temperature on the crop based on the stage of growth of crop.
4. The method as claimed in claim 3, wherein the stage of growth of crop comprises vegetative stage, flowering stage, grain filling stage and ripening stage.
5. The method as claimed in claim 3, wherein the impact of rainfall is determined based on type of crop being yielded.
6. The method as claimed in claim 5, wherein the type of crop is selected from a group comprising of any or a combination of kharif and rabi crops.
7. The method as claimed in claim 1, wherein the captured one or more attributes are stored on any or a combination of the first database and a second database.

8. The method as claimed in claim 1, wherein the one or more attributes comprises rainfall, pattern of rainfall, temperature, weather and climate.
9. The method as claimed in claim 1, wherein the method comprises determining an aggregate yield of crops of one or more area of interests.
10. A crop yield mapping system, said system comprising:
a set of sensors configured at one or more geographic locations that forms part of a map of an area of interest, and the set of sensors configured to capture one or more attributes associated with weather of the area of interest;
a location identifier operatively coupled with the set of sensors to identify geographic location of the area of interest; and
a control unit operatively coupled to the set of sensors and the location identifier, the control unit comprises one or more processors, the one or more processors coupled with a memory storing instructions executable by the one or more processors to:
determine crop yield variables from the sensed one or more attributes relating to crop production of the area of interest which together characterize yield of the area of interest;
estimate values of the determined yield variables of the area of interest
by comparing the determined yield variables with a dataset comprising a set of
predefined variables pertaining to the one or more attributes,
wherein a yield map, stored on a first database, of the area of interest is updated
based on the estimated yield of the area of interest and the identified geographic
location,
wherein based on the updated yield map the control unit is configured to estimate an average yield of the area of interest of the map.