Claims:WE CLAIM:

1. A processor implemented method (200) comprising:
receiving in real time, crop variables including market value of products of a crop, demand of the crop and products thereof, service cost for processing the crop to market and cost of production to raise the crop (202);
deriving growth rates based on the crop variables (204); and
determining crop valuation in real time for different market locations based on the growth rates (206).

2. The processor implemented method of claim 1, wherein the step of deriving growth rates comprises applying statistical techniques to the crop variables.

3. The processor implemented method of claim 1, wherein the step of deriving growth rates comprises:
performing univariate and multivariate analyses of the crop variables based on regression models and machine learning models to generate the growth rates; and
forecasting the growth rates based on time series modeling techniques.

4. The processor implemented method of claim 3, wherein the step of forecasting the growth rates comprises:
generating probabilities of incidences of the growth rates based on machine learning models using the growth rates and factors influencing the growth rates.

5. The processor implemented method of claim 1, wherein the step of determining crop valuation comprises executing mathematical models using the growth rates.

6. A system (100) comprising:
one or more data storage devices (102) operatively coupled to one or more hardware processors (104) and configured to store instructions configured for execution by the one or more hardware processors to:
receive in real time, crop variables including market value of products of a crop, demand of the crop and products thereof, service cost for processing the crop to market and cost of production to raise the crop;
derive growth rates based on the crop variables; and
determine crop valuation in real time for different market locations based on the growth rates.

7. The system of claim 6, wherein the one or more hardware processors are further configured to apply statistical techniques to the crop variables for deriving growth rates.

8. The system of claim 6, wherein the one or more hardware processors are further configured to:
perform univariate and multivariate analyses of the crop variables based on regression models and machine learning models to generate the growth rates; and
forecast growth rate based on time series modeling techniques.

9. The system of claim 8, wherein the one or more hardware processors are further configured to generate probabilities of incidences of the growth rates based on machine learning models using the growth rates and factors influencing the growth rates.

10. The system of claim 1, wherein the one or more hardware processors are further configured to determine crop valuation by executing mathematical models using the growth rates.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEMS AND METHODS FOR CROP VALUATION

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the embodiments and the manner in which it is to be performed.

TECHNICAL FIELD
The embodiments herein generally relate to creating measures for valuation of crop, and more particularly to systems and methods for real time valuation of crop based on various market conditions that drive agribusiness apart from seasonal trends.

BACKGROUND
Crop valuation is one of the important aspects for agribusiness to succeed. Farmers are most affected by volatile return on investments. Agricultural marketing in countries like India is mostly governed by the service sector which consumes the cream of harvests. It is a matter of concern that crops valued by consumers do not reflect in payoffs received by farmers. Conventionally known valuation systems forecast probable market price based on seasonal trends. But such forecasted estimates are of not much use to farmers. The volatility of the market based on natural calamities and synthetic market turmoil are the worst enemies in market price predictability making it a challenge for farmers to select crops that can assure best possible profit.

SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
In an aspect, there is provided a processor implemented method comprising: receiving in real time, crop variables including market value of products of a crop, demand of the crop and products thereof, service cost for processing the crop to market and cost of production to raise the crop; deriving growth rates based on the crop variables; and determining crop valuation in real time for different market locations based on the growth rates.
In another aspect, there is provided a system comprising: one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors to: receive in real time, crop variables including market value of products of a crop, demand of the crop and products thereof, service cost for processing the crop to market and cost of production to raise the crop; derive growth rates based on the crop variables; and determine crop valuation in real time for different market locations based on the growth rates.

In yet another aspect, there is provided a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive in real time, crop variables including market value of products of a crop, demand of the crop and products thereof, service cost for processing the crop to market and cost of production to raise the crop; derive growth rates based on the crop variables; and determine crop valuation in real time for different market locations based on the growth rates.
In an embodiment of the present disclosure, wherein the one or more hardware processors are further configured to apply statistical techniques to the crop variables for deriving growth rates.
In an embodiment of the present disclosure, wherein the one or more hardware processors are further configured to perform univariate and multivariate analyses of the crop variables based on regression models and machine learning models to generate the growth rates; and forecast growth rate based on time series modeling techniques.
In an embodiment of the present disclosure, wherein the one or more hardware processors are further configured to generate probabilities of incidences of the growth rates based on machine learning models using the growth rates and factors influencing the growth rates.
In an embodiment of the present disclosure, the one or more hardware processors are further configured to determine crop valuation by executing mathematical models using the growth rates.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the embodiments of the present disclosure, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
FIG.1 illustrates an exemplary block diagram of a system for crop valuation, in accordance with an embodiment of the present disclosure; and
FIG.2 illustrates an exemplary flow diagram of a method for crop valuation, in accordance with an embodiment of the present disclosure.
It should be appreciated by those skilled in the art that any block diagram herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.

DETAILED DESCRIPTION
The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items.
It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the preferred, systems and methods are now described.
Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
Before setting forth the detailed explanation, it is noted that all of the discussion below, regardless of the particular implementation being described, is exemplary in nature, rather than limiting.
Crop valuation plays a critical role in agribusiness. Systems and methods of the present disclosure aim to provide a mathematical model framework to determine valuation of a crop based on some key drivers for crops apart from seasonal trends alone that have been traditionally considered in crop valuation. The key drivers, hereinafter referred to as crop variables include production costs, productivity and demand of crop products due to urban tastes and population growth. The framework provided by the present disclosure is based on linked differential equations. Farmers are provided a valuation of their harvests based on end consumer pay-offs, thereby providing them reasonable bargaining power to increase their farming profits. Crop valuation is also evaluated based on market rates of different crop products. The framework of the present disclosure provides valuation of crops to farmers before cultivation, which enables farmers to make a choice based on best profitable crop for a given farm location.
Referring now to the drawings, and more particularly to FIG.1 and FIG.2, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and method.
FIG.1 illustrates an exemplary block diagram of a system 100 for crop valuation, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the system 100 can be stored in the memory 102.
FIG.2 illustrates an exemplary flow diagram of a method 200 for crop valuation, in accordance with an embodiment of the present disclosure. In an embodiment, the method of the present disclosure comprises two main stages.
In an embodiment, the system 100 comprises one or more data storage devices or memory 102 operatively coupled to the one or more processors 104 and is configured to store instructions configured for execution of steps of the method 200 by the one or more processors 104.
In an embodiment, at step 202, the one or more processors 104 of the system 100 are configured to receive in real time, key drivers for crop valuation. In accordance with the present disclosure, the key drivers are market value of products of a crop, demand of the crop and its products, service cost for processing the crop to market and cost of production to raise the crop. For instance, such drivers may be received from social networking media, connect to farmers through mobile applications or field agents, various networking models available today to farmers, and the like. Data from various sources may be integrated in real time.
In an embodiment, at step 204, the one or more processors 104 of the system 100 are configured to derive growth rates based on the crop variables received at step 202.
In an embodiment, the step of deriving growth rates may include applying statistical techniques to the crop variables. An exemplary embodiment is described hereinafter. Let c(t), p(t) and u(t) be three statistically significant dependent variables representing crop valuation, population and urbanization, all depending on time t. Based on observations of population growth in under developed and developing countries, it has been assumed that the population is practically independent of crop valuation and urbanization. In accordance with the present disclosure, partial differential equations are developed for determining population growth as represented by equations (1) through (13).
Euler and Malthus have suggested generic models for representing population growth which have been further modified in accordance with the present disclosure.
dp/dt=?p,?>0 ? (1)
wherein ? represents population growth rate.
Thus,
p(t)= p_0 e^(?t),p_0=p(0) ? (2)
Population cannot grow in uninhibited fashion indefinitely, thus replacing ? of equation (1) by function f(p).
dp/dt=f(p) p ? (3)
In accordance with the present disclosure, it is assumed that growth rate decreases in proportion (a) to the population, thus
f(p)=(? -ap),(a>0) ? (4)
Now from (3),
dp/dt=(? -ap) p
dp/dt=?p(1 -p/K),K=?/a ? (5) at t = 0, p = p0 based on logistic model for population suggested by Verhulst.
Solving equation (5),
p (t)= (Me^(?t))/(1+M/K e^(?t) ) ? (6)
where,
M=p_0/(1-p_0/K) ? (7)

Solving further, “a” is decrease in growth rate of population, let K = ?/a.
The law of entropy in informatics suggests that as population grows a census of a nation may miss some number of people which is likely to be caused mostly by the dynamics of the very system of recording the census.
Hence, in accordance with the present disclosure, it is assumed,
p(t)= P(t)+ e(t) ? (9)
where, P(t) is population recorded by census and e(t) is error in the record. It is expected that with efforts of statisticians and increase in use of technology in governments the census gets better with time, it may be assumed e(t) satisfies the following properties:
e(t)>0 ?t
e^' (t)<0,which implies that e(t)is decreasing function of t

Assuming P(t) satisfies the logistic equation (5),
dP/dt=?P(1-P/K),?>0 ? (10)
and
de/dt=-me,m>0 ? (11)
At t=0,P=P_0 and e=e_0 from (9)
p_0=P_0+e_0 ? (12)
and
dP/dt=?P(1-P/K)- me ? (13)
In accordance with the present disclosure, final population model (13) accounts for decrease in population growth rate (K=?/a) and error in estimate of population (me) component. In accordance with an embodiment of the present disclosure, “?”, “a” and “m” are determined through univariate analysis.
Further mathematical analysis of the model (13) are performed as represented by equations 14 through 19 herein below.
+ dp/dt¦|_(t=0)=?P_0 (1-P_0/K)-me_0 ? (14)
In order that p(t) is an increasing function at t=0,
?P_0 (1-P_0/K)>me_0 ? (15)
Considering population at time t = 0 and discussing further on ?,
?>(me_0)/(P_0 (1-P_0/K) ) ? (16)
If K is assumed as the limit of p(t) as well as P(t) based on (8),

Let e_0=qP_0 and K=rP_(0 ),r>1.
Then from (16),
?>mq/((1-1/r) ) ? (18)
If m=0.01, q=0.01, r=10
?>0.0111 ? (19) so that population may increase at
t=0
If population is small, as in case of some of the northern European countries, m and q may be small. Then ? could be much less than 1%, which means rate of growth of population could be lot less than 1%. Hence it is assumed that at t=0, p'(t) is positive and p(t) is increasing.
A portion of the population lives in urban areas. In accordance with the present disclosure, it is assumed that rate of change of urbanization is proportional to the rate of change of population.
Accordingly, du/dt=? dp/dt ? (20) at t=0, u=u_0=?p_0
Equation (20) represents the growth of urbanization relative to the growth of population in accordance with the present disclosure, wherein “?” is the urbanization growth rate proportional to the rate of change of population and is determined through regression analysis having population as independent variable.
In accordance with the present disclosure, the mathematical model for crop valuation has been developed based on four key drivers or crop variables viz., market value of products of a crop, demand of the crop and products thereof, service cost for processing the crop to market and cost of production to raise the crop. Considering population growth represented in (13) and urbanization growth represented in (20) along with cost factors such as service cost and production cost,
c=U+V-W+X+Y ? (21)
wherein, “c” is market value of the crop based on its products and the growth is determined based on other related factors (U, V, W, X & Y).
U = increase of c per unit of time due to service cost for processing the units of crop to produce unit products
V = increase of c per unit of time due to cost of production of units of crop to produce unit products
W = decrease of c per unit of time due to productivity of units of crop to produce unit products
X = increase of c per unit of time due to urbanization per unit of time
Y = increase of c per unit of time due to population per unit of time
Equation (21) represents cost factors, urbanization and the population growths together to form a single equation.
Thus, the differential equation with growth parameters in accordance with the present disclosure is represented as -
dc/dt=?/n c+dc-?c+f/q_1 cu+?/q_2 cp ? (22)
wherein, U=?/n c, V=dc, V=?c, X=f/q_1 cu, Y=?/q_2 cp
? = % of growth of c per unit time; n= crop units processed to produce a product.
d = % of growth of c per unit time.
? = % of loss of c per unit time.
f= % of growth of c per unit of u per unit time; q_1= unit of products of crop available in urban space.
?= % of growth of c per unit of p per unit time; q_2= unit of products of crop available.
In an embodiment, the step of deriving growth rates may include performing univariate and multivariate analyses of the crop variables based on regression models and machine learning models (such as Artificial Neural Network) to generate the growth rates. The growth rates, in accordance an embodiment of the present disclosure, may be further explained as given below.
“?” is % growth of “c” for service cost depends on transportation, processing of crops to its products, and the like. It is determined through multivariate analysis.
“d” is the % growth of “c” for cost of production depending on input costs (fertilizer, pesticide, labour, seed). It is analysed through multivariate analysis.
“?” is the % growth of “c” for productivity of crops. The productivity of crops are depends on above mentioned inputs, soil, water availability and weather. It is analysed through multivariate analysis.
“f” is the % growth of “c” for demand on urbanization. It is determined through multivariate analysis, as it is related to population growth.
“?” is the % growth of “c” for demand due to population growth. It is determined through univariate analysis.
In accordance with the present disclosure, the growth rates may be forecasted based on time series modeling techniques. For instance, Autoregressive integrated moving average (ARIMA) set of models may be used for forecasting growth rates. In an embodiment, forecasting the growth rates may include generating probabilities of incidences of the growth rates based on machine learning models using the growth rates and factors influencing the growth rates.
In an embodiment, at step 204-1, the one or more processors 104 of the system 100 are configured to check whether the growth rate estimates are satisfactory for executing the differential equations. In an embodiment, such a check may be based on historical study or pre-defined threshold or even standard or empirical data available. If not, at step 204-2, the crop variables provided as input are checked for correctness, else, at step 204-3, the differential equations are executed with the estimated growth rates and crop valuation is determined as explained in step 206.
In an embodiment, at step 206, the one or more processors 104 of the system 100 are configured to determine crop valuation in real time for different market locations based on the growth rates derived at step 204. In an embodiment, determining crop valuation includes executing mathematical models using the growth rates.
In accordance with an embodiment, at t=0, c=c_0, solution of equation (22) provides valuation of the crop based on market dynamics. Farmers may use this valuation to their advantage and bargain for a suitable profit share.
Since u=?p,
dc/dt={(?/n+d-?)+(f/q_1 ?+?/q_2 )p}c ? (23)
dc/dt={(?/n+d-?)+?p}c ? (24)
wherein, ?=(f/q_1 ?+?/q_2 ) ? (25)
At t=0,
+ dc/dt¦|_(t=0)={(?/n+d-?)+?p_0}c_0 ? (26)
Since, c_0>0, p_0>0, if µ>0 and (?/n+d)>?
+ dc/dt¦|_(t=0)>0 ? (27)
Making c an increasing function at t = 0
If (?/n+d)0, then since p(t)>0, F^' (t)>0 if (?/n+d)>? and which is true when the service cost and cost of production are greater than the crop productivity. F(t) is then an increasing function.
?>0, implies that the demand for crop products with urbanization and population growth are in positive trend.
Larger values of p(t)will increase F^' (t) which mean F(t) will increase faster. However it will also increase the ratio p/p_0 which will affect (40) adversely.
Also if ? is large such that (?/n+d)=?, since p'(0)>0 [p(t)>0, increasing at t=0], F^'' (t)>0. This means F(0) is a minimum, F(t) is increasing at t=0.
It also explains that the growths of (service cost and cost of production) are greater than the crop productivity. Thus it’s explains that the crop valuation is in positive growth.
Case 2: An unrealistic scenario where there is no market demand for a crop product, in a given condition.
If ?=0 , then F^' (t)=(?/n+d-?). Then F(t) is an increasing function. Thus demand of crop products due to population has no effect on crop valuation. Here the model of the present disclosure fails.
Case 3: In this scenario if demand for crop product is negative and growth productivity of crop is more than that of service cost and cost of production, the crop valuation will decrease. That means the crop products are not wanted in the market.
If ?<0, then in order F(t) is an increasing function, (?/n+d)>?+?p(t), where ?=-?>0. If the condition fails the valuation of crop is bound to decrease.
From the above analyses, it may be noted that considering the different scenarios in the mathematical model of the present disclosure, the valuation of crops may be estimated based on statistical estimates of the growth rates for the crop variables included in the mathematical model. The various growth rates based on the four key drivers (crop variables) provided in the present disclosure enable a reliable crop valuation model as compared to traditional known models wherein seasonal trends alone formed basis of crop valuation. As disclosed, statistical techniques are applied to the crop variables in order to generate the growth rates where the growth rates are measured both in univariate (based on the historic trend affecting the crop variables such as urbanization and population) and multivariate ways (depending on factors other than urbanization and population such as weather, etc.). Further, the growth rates are used in an algorithm developed on solving partial differential equations to generate crop value of a location. The data is analyzed in real time to provide present and future crop valuations of different market places. More specifically, the present disclosure utilizes regression equations to identify statistically significant variables and estimates by evaluating important factors influencing the growth rates and their level of contribution (growth rate variability depends on the variability of the contributing factors such as labor cost, fertilizer cost, price of seed, etc.). Further, machine learning models like Artificial Neural Network (ANN) are implemented to analyze the data and generate growth rates and probabilities of growth rates. Furthermore, forecasting of growth rates is done through time series modeling techniques such as ARIMA. Lastly, based on the generated growth rates the differential equation based model is trained to create a crop valuation model of a crop and location through which the decision on the profitable crop can be based. Thus by applying statistical machine learning models to evaluate growth rates automatically in real time, based on aforementioned crop variables, results in a framework adapted to provide farmers reliable crop valuation before cultivation, which will enable them to take into account uncertainty of the market price of the harvest and accordingly make a choice on the best profitable crop. In an embodiment, the systems and methods of the present disclosure may present a list of crops in hierarchy based on crop valuation for selection. In an embodiment, information on best marketplace / crop advisory / market linkages may be presented based on crop valuation.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments of the present disclosure. The scope of the subject matter embodiments defined here may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language.
The scope of the subject matter embodiments defined here may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language.
It is, however to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments of the present disclosure may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules comprising the system of the present disclosure and described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The various modules described herein may be implemented as software and/or hardware modules and may be stored in any type of non-transitory computer readable medium or other storage device. Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY, flash memory, and hard disk drives.
Further, although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
The preceding description has been presented with reference to various embodiments. Persons having ordinary skill in the art and technology to which this application pertains will appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.