mobile information system through ontological design

International Journal of Computer Science & Management Studies (IJCSMS), Volume 41, Issue: 02

Publishing Date: 9th June, 2020

An Indexed, Referred, Peer Reviewed and Impact Factor Journal

ISSN (Online): 2231-5268

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Mobile Information System through Ontological Design

Sitanath biswas1, G Samba Rao

2, Pravat Kumar Rautray

3, Prakash Kumar Sarangi

4

1Associate Professor, Department of Computer Science Engineering, Gandhi Institute For

Technology (GIFT), Bhubaneswar 2Assistant Professor, Department of Computer Science Engineering, Gandhi Engineering College,

Bhubaneswar Assistant Professor, Department of Computer Science Engineering Aryan Institute of Engineering and

Technology Bhubaneswar, Odisha

Assistant Professor, Department of Computer Science Engineering NM Institute of Engineering and

Technology Bhubaneswar, Odisha

Abstract Mobile devices have obtained a significant role in our life providing a large variety of useful

functionalities and features. Mobile applications are the software programs that can be based on

different mobile devices and operating systems such as android, ios, symbians etc. Mobile apps provide

information to any user at any place at any time. The user can also access the mobile phone at anywhere

and anytime. Mobile devices can also be used in any domain like agriculture system, healthcare system and learning. This paper presents proposed information system architecture for any application using ontology and uses a case study that provide information to farmers about agriculture system through mobile apps based on ontology. This paper also shows design of ontology using RDF tool for maintaining semantics in data in an agricultural system with respect to different contexts. This paper is also an attempt to comprise how farmers get information regarding agriculture through mobile applications to some extent. This paper also builds a comparative analysis between different information systems used in agricultural application with the proposed information system architecture. Keywords: Context, context elements-agriculture, context-aware mobile apps, ontology, resource description frame work. 1. Introduction

Mobile computing is a paradigm in which applications can discover information and provide

relevant contextual information. Mobile and context-aware computing applications respond to

changes in the environment in an intelligent manner. Mobile apps provide unique characteristics

such as ubiquity, convenience, positioning and personalization. The emergence of pervasive

computing in mobile technologies made computing with user everywhere and every time. Using

context and context awareness as the key concepts in mobile computing provide ubiquitous

computing in mobile devices [1]. Hence the users access various services from mobile apps at

anywhere and anytime. The context-awareness paradigm [2], helps to discover information that

might be interesting for a user based on the situation the user is in and provides service to the

user based on the user’s current context. Using ontology in any application provides

extensibility, expressiveness, and ease of sharing and reuse. Ontology provides a well-founded

mechanism for the representation and reasoning of context information. In the context-aware





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computing environments, ontology is referred as the shared understanding of a set of entities and

their relations and functions [3].

Most of the information systems such as e-choupal, M-krishi, and IKSL used for agricultural

applications provides information to farmers. But those information systems do not provide

information based on ontology and context elements. In this paper we have proposed an

information system architecture which takes ontology with respect to different context of mobile

apps. To obtain semantics in data, this proposed information system has taken resource

description framework (RDF) for designing agricultural ontology by using RDF data model. This

paper provides information to farmers based on various context parameters by implementing

advanced java and xml tool in it.

The rest of the paper is organized as follows. In section 2, we discuss related works. In section 3

we have proposed an architecture or model and have taken RDF data tool for designing ontology

in agricultural system which generates the information in a triple and graph form. Section 4

discusses implementation of this architecture and provides various user interfaces using xml tool.

This section also provides a comparative analysis between the proposed information system

architecture and existed agriculture information systems.

2. Related works

2.1 Agricultural information systems

The rapid advances in the technologies of mobile computing and wireless communications have

brought opportunities for various mobile applications running on handheld devices. Through

enabling technologies, for e.g. Wi-Fi and 3G mobile application users may access services such

as payment through mobiles and share information at anywhere & anytime. Government has

started the e-choupal project which deals with establishing internet centers in rural areas where

farmers access real time information easily.

The e-Choupal portal [4] also provides the rural agricultural communities with information in

their respective local languages on weather forecasting, education on improved farm practices,

risk management, knowledge and purchases of better quality farm inputs. The e-Choupal system

has required ITC to make significant investments to create and maintain its own IT network in rural India

and to identify and train a local farmer to manage each e-Choupal. Using e-choupal, the farmers can

know daily closing prices on local mandis as well as track global price trends and get information about

new farming techniques. The farmer also use the e-Choupal to order seed, fertilizer and other products

such as consumer goods from ITC or its partners at prices lower than those available from village traders. e-Choupal began offering third-party access to rural India. Currently, there are more than 160 partners

from domains as diverse as seeds, consumer products, finance, insurance and employment who sell their

products to rural consumers through e-Chou pal’s channel. Government has established kissan call

centers in 2004 in every state to deliver information about agriculture to the farmers in their local

languages.





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M-agriculture refers to the delivery of agriculture-related services through mobile

communications technology. Mobile communication technology includes all kinds of portable

devices like basic mobile phones, smart phones, PDAs or tablet devices. m-Krishi[5] is a high-

end technical service started by Tata consultancy services (TCS) in 2007 in India to deliver

customized advisory services to farmers on crop production, market information and weather

forecasting. m-Krishi also involves installation of different kinds of sensors in farmers’ field to

collect information on soil humidity and weather conditions. In the current m-Krishi setup, an

automated weather station is deployed at the center of the village that procures soil/weather

characteristics and parameters for the entire village. The information related to crop, soil and

micro-environment is gathered by sensors and sent to a central server using the cellular network.

M-Krishi relies on an automated frequently asked questions database. This is able to handle the

majority of farmer questions, since most are generic. More specific or sophisticated questions

that cannot be answered automatically are forwarded to ten experts with internet access. These

experts interact with a system that resembles e-mail. They are able to see attached photos and

soil sensor information with each message and their response is sent back to the farmer by SMS.

Farmers receive responses to their queries through the same channel within 24 hrs.

Mobile operator bharti airtel partnered with IFFCO (Indian Farmer Fertilizer Cooperative Ltd) to

form the joint venture IKSL [6] in 2007. This company provides information on market prices,

farming techniques, weather forecasts, rural health initiatives and fertilizer availability, etc. IKSL

sends 5 free daily voice updates except Sunday in local language so that also illiterate farmers

can benefited.

e-sagu[7] is a teleagriculture project started in 2004 by the International Institute of Information

Technology IIT, Hyderabad, and Media Lab Asia. e-sagu delivers farm-specific, query-less

advice once a week from sowing to harvesting. This service reduces the cost of cultivation and

increases farm productivity as well as the quality of agricultural products. The coordinator collects

farm registration details including soil data, water resources, previous crops, pest issues, current crop,

seed variety, capital availability, etc. and sends these to the main e-sagu system. Every week, the

coordinator collects the data on farm operations over the last seven days. He then makes his own

observation of the crop status and problems, and takes four to five digital photographs of the problem

areas. A compact disk is prepared with the photographs and other information and transported to the

main system by a regular courier service. The agricultural experts with diverse backgrounds of respective

departments such as entomology, pathology, agronomy, etc. at the e-sagu main lab analyze the crop

situation with respect to soil, weather and other agronomic practices and prepare farm-specific advice.

The advice is downloaded electronically at the local e-sagu center. The coordinator prints out and delivers

the advice to the farmer. In this way each farm gets the proactive advice at regular intervals starting from

pre-sowing operations to post-harvest precautions. The advice contains the steps the farmer should take to

improve crop productivity.

2.2 Context and context elements in mobile apps

Context is a set of situations and actions. Such situations force to change over time, and describe

humans’ behaviors, application and environmental states whenever specific actions are applied.

In fact, contexts have been used in various applications in mobile devices that deal with





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modeling situations, beliefs, probabilities, spatial, and temporal relations, web semantics,

integration of heterogeneous knowledge resources, and databases [8]. With respect to mobile

apps, context can be characterized into different context elements or categories such as

computing context, user context, physical context and time context [9]. Context awareness in

mobile computing discovers contextual information from various context parameters such as

computing context, user context, physical context and time context. Mobile apps can provide

information to users based on these contexts.

Computing context includes device properties which classifies as network connectivity,

communication cost & bandwidth, orientation, touch and display. Mobile apps provide

communication services through standard internet protocol using General Packet Radio Service

(GPRS), Bluetooth transfers, Short Message Service (SMS), and Multimedia Messaging Service

(MMS) to users as computing contexts [10].

User context includes the user’s profile, role of user, location, process &task associated with user

and current social situation. User and Role holds context information related to user and its

activity, for example, movements and dynamic gestures of a mobile device user are examined by

processing the three accelerometer signals, which provide the acceleration data of a device in

three orthogonal directions aligned with the axes of the device. Activities of a user cause

dynamic accelerations to the device a user is carrying, for example, walking causes certain

periodic signal characteristics. The recognition of user [11] movements can be carried out using

methods in frequency domain, processing signal waveforms in time domain and using other

feature extraction methods such as, wavelets. The font size of the mobile device for any

application can be changed according to the user’s activity. When the user is on movement, he

could make the font size large and make small when he is stationary. Mobile apps present

information about user’s activity on mobile screen either touch or swipe which are captured by

touch sensors in mobile devices as user contexts. Location [12], represents the current position of

the device as well as the user using GPS-coordinates and a value for the altitude. Mobile apps

present information about these user contexts as it avails GPS and wireless LAN.

Physical contexts or physical context elements like light, temperature, humidity and air pressure

are captured by physical sensors. The mobile apps include color sensors, IR and UV sensors to

capture light. It is also possible to identify places based on the characteristic background noise.

For example the microphone sensor is able to recognize with background noise context only if

they are located in an office or at any place. Temperature and humidity of a device’s

environment give about the climatic conditions as the outdoors may vary according to the time of

the day and season.

Time refers to different types of time information of the object. Users can identify time as

seasonal, weekdays, weekends and holidays. Users identified weekdays and weekends as

contexts with different informational needs Time information gives two types of context

information which includes absolute and relative time context.





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It is a fact that context has no uniform or standard definition. So everyone can give his

understanding about context and it can be classified into any dimension. The m-learning model

[13], consists of various contexts as device context, learner /user context, social context and

mobility contexts. The device context includes screen size, hardware components and sensors to

capture light, temperature and location etc. The learner context or aspect consists of the

following element such as learner profile, cognitive, physiological and preferences. The social

aspect or context includes web server and mobile browser by which any information can be

processed through mobile device. The web server allows to host web site and to retrieve any

information from it. The web server refers to either the hardware or the software that helps to

deliver web content that can be accessed through the Internet. The mobile web server also allows

linking or connecting with people in all over the world through social web sites. Mobility aspect

allows the mobile users to sense and react in everyday situations in the world with simple system

architecture. The mobility of the mobile device is the key to the idea of mobile context

awareness where sensing and reacting is done by the mobile device itself. To achieve mobile

context awareness, mobile device should be equipped with various sensors.

2.3 Based on ontology and context elements the requirement analysis of an agricultural system In recent years, ontology have emerged as one of the most popular and widely accepted tools for

modeling information in mobile computing domains. Nowadays, mobile and web environments

converge in one shared communication sphere. Technologies [14], rising from semantic web and

mobile communication fields get combined to achieve this convergence towards the vision of

ontology enabled ubiquitous mobile communication. Semantic and ontology technologies are

seen as being able to advance the seamless integration of the mobile and the web worlds.

Ontology plays an ever-increasing role in service platforms and mobile communications.

Ontology represents semantics, concepts and relationships in the context data. Context data represent instances of contexts [15]. Contexts may exist in the form of stored data on a disk file (context database) or in the form of context instances obtained from the sensors. Ontology-driven applications [16], exhibit features such as expressiveness, extensibility, ease of sharing and reuse and logic reasoning support. Thus, it helps users to guide enquiry based applications effectively. However, as the mobile world starts to integrate with the Web world in delivering new value-added services, the area of semantics ubiquitous mobile communication inevitably gains a larger importance and potential. In an agricultural system, the first phase that selected by the farmers is the crop choosing phase, the second phase is the crop growing phase and the third phase is the crop selling phase. Here the table-1[17], shows requirements based on ontology and specifies various context elements for an agricultural system which are summarized below in phases.

http://en.wikipedia.org/wiki/Computer_hardware

http://en.wikipedia.org/wiki/Software

http://en.wikipedia.org/wiki/Web_content

http://en.wikipedia.org/wiki/Internet





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Table 1: The requirement analysis of an agricultural system In crop choosing phase, the farmer, the user requires information on choosing crops through mobile apps. The mobile apps include different contexts such as computing context, physical context and time context parameters. Mobile apps include device properties as context parameters which provide GPRS services and different browsers. These apps include physical context as context elements which gives GPS services that provides location based weather information. It also includes time as context parameters provide up-to-date market prices in a particular season. In crop growing phase, the farmer, the user requires information on growing crops through mobile apps. The mobile apps include different contexts such as computing context and user context parameters. Mobile apps include device properties as context parameters which provide GPRS services and different browsers. These apps also include user’s role and his task as context elements which are captured in the device to alert the user.





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In crop selling phase, the farmer, the user requires information on selling crops through mobile apps. The mobile apps include different contexts such as computing context, time context and user context parameters. Mobile apps include device properties as context parameters which provide GPRS services and different browsers. Mobile apps include time as context parameters which provides up-to-date market prices at a particular time or season. Mobile apps include location as context elements which provide information on collectors using GPS services and wireless LAN networks. 3. Proposed architecture

Figure: 1 Proposed architecture for mobile agricultural information system

Here the proposed architecture consist of various components like ontology, context elements,

modeling of context elements using RDF in mobile web and generating user interfaces in the

mobile apps to build any information system in the mobile apps. At first we have taken context

aware elements with respect to mobile information systems as device context, user context,

social context and mobility context. We have taken several existing agriculture information

systems and analyze these mobile context elements into the information systems as shown in

section 3.1.1. We have also built ontology for an agriculture system which makes the

organization of knowledge in a systematic and formal way. Using ontology in any application, it

makes the knowledge sharable and reusable among the users and developers. Building ontology

for agriculture system in mobile web, the users get powerful support of semantic information

from the mobile web apps. To make a structural analysis in ontology, we have done it through

UML class diagram. We have taken various classes and establish many kind of relationships

among classes. Ontology can be designed in OWL or RDF. Here the ontology is modeled or

designed in triple form and semantic graph form using RDF to provide semantic information in





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the web. Here RDF is used to organize the information in a graph database form in the web. By

having connectivity with the web server or database server, we can build user interfaces for users

or farmers to provide information about agricultural system through mobile web apps.

3.1 Building ontology and RDF in agricultural domain

3.1.1 Analyzing the various agricultural information systems with mobile contexts elements

Here we have taken the mobile context elements as device context, user context, social contexts

and mobility context and analyzing them in various agricultural information systems. From this

analysis, we have built and design the ontology for an agriculture application in mobile apps.





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Table 2: Analysis of mobile contexts with various agricultural information systems

3.1.2 Building Ontology in agriculture application

The ontology for agricultural system is composed of various farming phases or stages which

have been already discussed above in section (2.3) as crop choosing phase etc. The various

things used in those farming phases such as crop, environment, zone cultivar fertilizers and

pesticides. The ontology for agricultural system also includes about the new mechanisms, control

methods used for disease control and pest control. It also includes current market prices, current

selling prices and interested buyers information. Here the fig.2. shows the various ontology used

in agricultural system.

Figure 2: Ontology used in agricultural domain





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3.1.3 Analyzing ontology using UML in agricultural system

To analyze ontology using UML, we have taken several classes and establishing various kinds of

relationships among the classes using class diagram. Here the figure-3 shows, the class

agricultural system is aggregated or composed of many classes such as crops, farmer,

environmental factor etc. The class diagram shows one- to- many cardinality or kind of

relationship among the classes. For example the farmer class is a part of agricultural system and

an agricultural system has many farmers. Hence it shows one –to-many kind of aggregation

between the agricultural system class and farmer class. Further the farmer class is extended to

other classes as small scale farmer, large scale farmer and commercial farmer as shown in the

figure-3.

Figure -3: class diagram of agricultural system

3.2 Building RDF data model for agriculture application RDF, resource description framework is a framework or general purpose language to represent





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information on the web. The Resource Description Framework (RDF) is an infrastructure that enables the encoding, exchange and reuse of structured metadata. The Resource Description Framework (RDF) developed under the auspices of the World Wide Web Consortium which is an infrastructure that enables the encoding, exchange, and reuse of structured metadata. This infrastructure enables metadata interoperability through the design of mechanisms that support common conventions of semantics, syntax, and structure. RDF additionally provides a means for publishing both human-readable and machine-process able vocabularies. Vocabularies are the set of properties or metadata elements. RDF is a general method to decompose knowledge into small pieces, with some rules about the semantics, or meaning, of those pieces. RDF can express any fact so that computer applications can do useful things with knowledge expressed in it. 3.2.1 Building RDF data model in agriculture domain RDF provides a model for describing resources. Resources have properties or attributes or characteristics. RDF defines a resource as any object that is uniquely identifiable by a uniform resource identifier as URI. The RDF Graph has nodes and labeled directed arcs that link pairs of nodes and this is represented as a set of RDF triples where each triple contains a subject Node, property arc and object node.

Figure 4: RDF data model

http://www.w3.org/TR/rdf-concepts/#section-Graph-Node

http://www.w3.org/TR/rdf-concepts/#xtocid103646





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RDF provides a data model for describing resources where resources have attributes. Here the

node agricultural system is the subject node and it have various property attributes such as “is

affected by”, “is done by” and “produce” so on. These property elements are represented by

arrows or arcs called as statements. Each statement asserts a fact about a resource. A statement

has three parts such as the subject is the resource from which the arc leaves, the predicate is the

property that labels the arrow and the object is the resource or literal pointed to by the arrow. The

RDF statement is called as RDF triple because it contains the three parts as subject, predicate and

object. Also information is represented by triples as subject-predicate-object in RDF. All

elements in RDF triple are resources which are represented as URI or literal. For example using

RDF in agricultural domain, the first resource element with URI is

http://www.example.org/agri_system. It has so many predicates which are represented with

URIs. The first predicate element is http://www.example.org/isaffected and the object node is

http://www.agri_system/envfactors/. The other resource elements can further be built and

represented with URIs or string literal.

3.2.2 Building the RDF document

<? xml version="1.0"?>

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:ex="http://www.example.org/">

<rdf: Description rdf:about="http://www.agri_system/">

<ex:isaffected>

<rdf:Description rdf:about="http://www.agri_system/envfactors/">

<ex:env_name> Soil </ex:env_name>

</rdf:Description>

</ex:isaffected>

</rdf:Description>

<rdf:Description rdf:about="http://www.agri_system/">

<ex:hasinfluence>

<rdf:Description ex:influenceon="interested Buyers">

</rdf:Description>

</ex:hasinfluence>

</rdf:Description>


<ex:haveimpact>

<rdf:Description ex:impacton="Market prices">

</rdf:Description>

</ex:haveimpact>

</rdf:Description>


<ex:apply>

<rdf:Description ex:areapplying="Fertilizers, Pesticides and Disease control

methods">

</rdf:Description>

</ex:apply>

</rdf:Description>

</rdf:RDF>

3.2.3 Generating the triples and semantic graph in RDF document through validation

http://jena.apache.org/tutorials/rdf_api.html#glos-Subject

http://jena.apache.org/tutorials/rdf_api.html#glos-Predicate

http://jena.apache.org/tutorials/rdf_api.html#glos-Object

http://www.example.org/isaffected

http://www.agri_system/envfactors/





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Here we have used RDF, resource description framework in the agriculture application to

provide semantics, syntax and structure in the metadata. RDF provides a flexible, graph-based

model for recording data that is interchangeable globally in any application. Here we have built

ontology in an agriculture application and RDF is the format for defining various ontology as

discussed above in the fig.2. Implementing or validating the RDF document in the w3school’s

online validaor, it generate the following triples and semantic graph. The triples are shown here

in the form of subject,predicate and object.

Num

ber Subject Predicate Object

1 http://www.agri_sy

stem/

http://www.example.

org/isaffected

http://www.agri_sy

stem/envfactors/


stem/envfactors/

http://www.example.

org/env_name "Soil"

3 genid:A1044079 http://www.example.

org/influenceon

"interested

Buyers"


stem/

http://www.example.

org/hasinfluence genid:A1044079


org/impacton "Market prices"


stem/

http://www.example.

org/haveimpact genid:A1044080


org/areapplying

"Fertilizers,Pesti

cides and Disease

control methods"


stem/

http://www.example.

org/apply genid:A1044081

Table 2: The triples in RDF

http://www.agri_system/








http://www.example.org/env_name

http://www.example.org/env_name

http://www.example.org/influenceon

http://www.example.org/influenceon



http://www.example.org/hasinfluence

http://www.example.org/hasinfluence

http://www.example.org/impacton

http://www.example.org/impacton



http://www.example.org/haveimpact

http://www.example.org/haveimpact

http://www.example.org/areapplying

http://www.example.org/areapplying



http://www.example.org/apply

http://www.example.org/apply





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Figure 5: Semantic graph of the data model in agricultural domain





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4. Implementation

4.1 Providing user interfaces to farmers through m-agriculture information system

The aim and objective is to provide information based on different contexts to the farmers or the

users through the mobile application. Here we have implemented user context in the mobile apps

to provide information about crops, fertilizers, pesticides and interested buyers to the farmers,

through xml and advanced java programming in an agricultural application. Here the user

interfaces are designed using extensible markable language i.e. xml and the navigation from one

page to the next page is done using advanced java tool which are given below.

Figure 6: Navigating from one page or screen to other screen in mobile apps

Here putting a click on the user button, the users navigate from the home screen to the next screen which

shows information about user context elements as crops, fertilizers and buyers. It shows the information

about various fields by designing GUIs in the mobile apps.





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Figure 7: Providing information about crops in a grid view

By putting click action on the crops button, the farmer, the user can see the various crops in a grid view

form. Similarly if the user put click on the fertilizer and pesticides, the user can view the grid view of

fertilizers.

Figure 7: Details about the wheat crop





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When the user put clock on the wheat crop present in that grid view, the farmer, the user can find the

details of wheat crop. The farmer can get the information as name, area, production, season, yield and

year about wheat crop.

5. Comparative analysis between the existing information systems and the proposed in an

agricultural system

We have already analyzed the mobile context elements in various agricultural information

systems in section (3.1.1) and based on that the ontology for agricultural system is defined in

section (3.1.2). The ontology is designed in the form of data model using RDF and it organizes

the data in triple form like<subject, predicate object> form. RDF also generates the semantic

graph through which unambiguity and semantics in data is obtained in mobile web based

systems. Keeping ontology in mind, we have built user interfaces in mobile apps for agricultural

system using android SDK, to provide information to farmers. After that, we have put a

comparison between the information provided by the existing information systems and the

proposed one below in table -3.





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Table 3: Comparative analysis

6. Conclusion

In this paper we have proposed architecture for m-agricultural information system which uses ontology in

terms of different contents to define the requirements for this system. Here we have taken ontology to

obtain the knowledge expressive, sharable and reusable for this system. Here RDF tool is used to design

the ontological elements in the form of graph database in the web to provide semantic information to

users/farmers. RDF generates the data or information in triple form as subject, predicate and objects for

agricultural system. At last we have taken xml to design the interface in mobile apps and linking on one

page to next page is done by advanced java programming. This m-agricultural information system

provides information to farmers about high yield crop as area, production, size, season and yield of that

high yield crop through user interfaces in the mobile apps. When the user as farmer chooses the fertilizer

button, it will give information about fertilizers and pesticides through user interfaces in the mobile apps.

But the proposed information system does not provide information to the farmers in a local language.

However, we can use language interfaces to use it in local languages.

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