1. Literature Survey

1.1 Sources

[1] Carrascal, M.J. and Pau, L.F. “A survey of expert systems in agriculture and food

processing.” AI Applications, 6(2): 27-49, 1992

[2] Chakraborty, P. and Chakrabarti, D.K. “A brief survey of computerized expert systems

for crop protection being used in India.” Progress in Natural Science, 18(4): 469-473, 2000.

[3] Kolhe, S. and Gupta, G. K. “Web-based Soybean Disease Diagnosis and Management

System.” Fifth Conference of the Asian Federation for Information Technology in

Agriculture (AFITA, 553-559), 2006.

[4] Wang Zhi-Qiang, Chen Zhi-Chao “A Web-based Agricultural Decision Support System

on Crop Growth Monitoring and Food Security Strategies” Third International Symposium

on Computer Science and Computational Technology(ISCSCT ’10) Jiaozuo, P. R. China,

14-15, ISBN 978-952-5726-10-7, pp. 487-491, August 2010.

[5] Russell Yost, Tasnee Attanandana, Carol J. Pierce Colfer and Stephen Itoga, “Decision

Support Systems in Agriculture: Some Successes and a Bright Future”, University of Hawai`i

at Manoa, USA.

1.2 Summary of relevant papers

1. Decision Support Systems for Food & Agriculture

Author P.H. Heinemann

Conference Systems Analysis and Modeling in Food and Agriculture, 2010

Web Link http://www.eolss.net/Eolss-sampleAllChapter.aspx

Summary This research paper describes how the management of agricultural production

operations can be complex & daunting, and many factors4 need to be

simultaneously concerned while taking a decision. Basically a broad used of

effective project management & decision making techniques when it comes to the

DSS of Food & Agriculture. It provides description & examples of three types of

Decision Support System: qualitative based expert system, quantitative based

simulation models, & a hybrid model combination of qualitative & quantitative

approach

1

2. A critical analysis of Decision Support Systems research

Author Pervan, Graham and Arnott, David

Conference Journal of Information Technology, 20, 2, June, 2005, pp67-87

Web Link http://espace.library.curtin.edu.au/cgi-bin/espace.pdf?file=/2008/11/13/

file_14/20246

Summary This paper critically analyses the nature and state of decision support systems

(DSS) research. To provide context for the analysis, a history of DSS is

presented which focuses on the evolution of a number of sub-groupings of

research and practice: personal decision support systems, group support

systems, negotiation support systems, intelligent decision support systems,

knowledge management based DSS, executive information systems/business

intelligence, and data warehousing. To understand the state of DSS research an

empirical investigation of published DSS research is presented. This

investigation is based on the detailed analysis of 1,020 DSS articles published

in 14 major journals from 1990 to 2003

3. A Web-based Agricultural Decision Support System on Crop Growth Monitoring and

Food Security Strategies

Author Wang Zhi-Qiang, Chen Zhi-Chao

Conference Third International Symposium on Computer Science and Computational

Technology(ISCSCT ’10) Jiaozuo, P. R. China, ISBN 978-952-5726-10-7,

August 2010, pp. 487-491

Web Link http://www.academypublisher.com/proc/iscsct10/papers/iscsct10p487.pdf

Summary Food shortage has been among the most threatening problems to the world

since the beginning of the new century. Chinese governments at different levels

have being taken different kinds of actions to stabilize and increase the yields

of grains. A major premise of making right decisions is the ability to accurately

assess crop growth and food supply, and a scientific decision-making process

to provide appropriate strategies or countermeasures based on them. This can

be accomplished partly by using the decision support system (DSS) that

provide accurate and detailed information about crop growth and food supply.

In this paper, an agricultural spatial DSS (ADSS) frame was studied and

developed to meet the increasing demands.

4. Strengthening Agricultural Innovation Capacity: Are innovation brokers the answer?

2

Author Laurens Klerkx , Andy Hall, and Cees Leeuwis

Conference International journal of agricultural innovation and research, 2009

Web Link www.merit.unu.edu/publications/wppdf/2009/wp2009-019.pdf

Summary This paper examines the role of innovation brokers in stimulating innovation

system interaction and innovation capacity building, and illustrates this by

taking the case of Dutch agriculture as an example. Subsequently, it reflects

upon the potential role of innovation brokers in developing countries’

agriculture. It concludes that innovation brokerage roles are likely to become

relevant in emerging economies and that public or donor investment in

innovation brokerage may be needed to overcome inherent tensions regarding

the neutrality and funding of such players in the innovation system.

5. A Knowledge Management System for Exchanging and Creating Knowledge in

Organic Farming

Author Vincent Soulignac, Jean-Louis Ermine, Jean-Luc Paris, Olivier Devise and

Jean-Pierre Chanet

Conference The Electronic Journal of Knowledge Management Volume 10 Issue 2 (pp163-

182)

Web Link www.ejkm.com

Summary Agriculture is involved in a vast societal movement, linked to the framework

and the values associated with sustainable development. To make a success of

this transformation, agriculture will have to become both integrated into its

environment, and organic. Agriculture must evolve into a more

environmentally-friendly approach while remaining economically workable.

This type of agriculture is said to be sustainable. It has a systemic logic and

therefore requires a strong knowledge base. In this study we propose a

knowledge management IT-based system. In the first part of the paper, they

discuss the potential actors of the system and their possible implications. The

second part deals with the knowledge selection and formalization. The third

part describes the main computing features of the knowledge server we propose

2. Results of Literature Survey

3

Agriculture has been a part of human life since the beginning of the human race and the need

for agricultural information is probably almost as old as agriculture itself. Babylonian clay

tablets have been found that contain agricultural information [1]. Throughout history, in

many civilizations, there have been libraries that have included agricultural information, and

separate agricultural libraries were established in Europe in the mid-eighteenth century. In

India, special attention was paid to development of an agricultural research infrastructure

immediately after Independence. The Indian Council of Agricultural Research (ICAR) acts as

a repository of information and provides consultancy on agriculture, horticulture, resource

management, animal sciences, agricultural engineering, fisheries, agricultural extension,

agricultural education, home science, and agricultural communication. ICAR has established

various research centres in order to meet the agricultural research and education needs of the

country. It is actively pursuing human resource development in the field of agricultural

sciences by setting up numerous agricultural universities spanning the entire country. The

Technology Intervention Programs also form an integral part of ICAR's agenda which

establishes Krishi Vigyan Kendras (KVKs) responsible for training, research, and

demonstration of improved technologies. Agriculture universities were set up in various

states and national level agriculture labs were established under the ICAR. Presently there are

38 state agricultural universities, 37 research institutes, five research bureaus, 17 national

research centres, seven project directorates and other allied departments. These universities

and laboratories have helped in bringing the Green Revolution, White Revolution, and Blue

Revolution, and have helped the country to come out of the situation of food scarcity and

achieve food self-sufficiency and food surplus. The Indian agricultural sector provides

employment to about 65% of the labour force, accounts for 27% of the GDP, contributes 21%

of total exports, and provides raw materials to several industries.

The future growth in agriculture must come from new technologies which are not only "cost

effective" but also "in conformity" with natural climatic regime of the country (Singh, 2004);

[7] technologies relevant to rain-fed areas specifically; continued genetic improvements for

better seeds and yields; data improvements for better research, better results, and sustainable

planning; bridging the gap between knowledge and practice; and judicious land use resource

surveys, efficient management practices, and sustainable use of natural resources.

Recommendations of the United Nations Conference on Environment and Development-

Agenda 21 (United Nations 1992) [1] on "Information for decision making" are the

development of indicators for sustainable development; promotion of global use of indicators

for sustainable development; improvement of data collection and use and methods of data

4

assessment and analysis; establishment of comprehensive information framework;

strengthening of capacity for traditional information; production of information usable for

decision making; development of documentation about information; establishment of

standards and methods for handling information; establishment and strengthening of

electronics networking capabilities, and making use of commercial information sources. The

Agenda-21 recommends major adjustments in agricultural, environmental and macro-

economic policy to create the conditions for the Sustainable Agriculture and Rural

Development.

2.1 Integrated Summary of the literature studied

S.No. Title of the

paper

Motivation Learning

1 Decision

Support

Systems for

Food &

Agriculture

The management of agricultural

production operations can be complex and

daunting. A manager who is faced with a

decision confronts many factors that need

to be simultaneously considered. In

addition to facing management decision

that will potentially improve the immediate

operation, the manager must ultimately be

accountable to society and to the

environment. So, impacts of decision go

beyond farm. A DSS is a computer based

program that assists with the decision

making process. The program can be

qualitative, quantitative or both. These

programs are important because

agriculture productions are complex due to

biological, chemical and physical process

involved.

Decision support systems

provide managers with

recommendations for

specific situations and help

with analyzing choices. By

better understanding the

decision support systems

in agriculture, it helps us

understand meaning of

system analysis and

decision making.

2 A critical

analysis of

Decision

Decision support system (DSS) is the area

of the information systems (IS) discipline

that is focused on supporting and

DSS is an important field

of information systems

research and practice, is at

5

Support

Systems

research

improving managerial decision-making. In

terms of contemporary professional

practice, DSS includes personal decision

support systems, group support systems,

executive information systems, online

analytical processing systems, data

warehousing, and business intelligence.

Over the three decades of its history, DSS

has moved from a radical movement that

changed the way information systems were

perceived in business, to a mainstream

commercial IT movement that all

organizations engage.

the crossroads; its future is

both bright and troubled.

Its share of IS research is

declining but in industry it

is growing significantly

despite the IT downturn.

We believe that left

unchanged, the current

agendas of DSS

researchers will lead the

field to irrelevance.

3 A Web-based

Agricultural

Decision

Support

System on

Crop Growth

Monitoring

and Food

Security

Strategies

Food shortage has been among the most

threatening problems to the world since the

beginning of the new century. Chinese

governments at different levels have being

taken different kinds of actions to stabilize

and increase the yields of grains. A major

premise of making right decisions is the

ability to accurately assess crop growth

and food supply, and a scientific decision-

making process to provide appropriate

strategies or countermeasures based on

them.

The ADSS [5] was aimed

at suggesting efficient

strategies for problems in

crop growth and food

safety as well as providing

timely and accurate

information about crop

growth and food supply.

The system, based on the

spatial information

technologies and crop

growth simulation method.

4 Strengthenin

g

Agricultural

Innovation

Capacity:

Are

innovation

brokers the

answer?

The multifunctional agricultural sector of

the 21st century is embedded in a fast-

changing global context of market,

technology, policy and regulatory settings

that present both challenges and

opportunities. In this fast-changing world,

innovation is a central strategy in tackling

challenges and grasping opportunities and

as a means of achieving economic, social

It has seen that existing

organizations expand their

mandate and are already

taking up brokerage roles.

Whether such

organizations are ideally

placed to play these roles

should be a subject for

6

and environmental goals further study. The

Dutch case [9] suggests

that specialized innovation

brokers can be more

neutral and credible in

fulfilling important roles

such as demand

articulation, network

building and innovation

process management. In

any case, there remains

significant scope for

existing research and

extension organizations to

retool themselves in order

to play new roles.

5 A Knowledge

Management

System for

Exchanging

and Creating

Knowledge

in Organic

Farming

Agriculture has been a part of human life

since the beginning of the human race and

the need for agricultural information is

probably almost as old as agriculture itself.

In India, special attention was paid to

development of an agricultural research

infrastructure immediately after

Independence

Agriculture must evolve

into a more

environmentally-friendly

approach while remaining

economically workable.

This type of agriculture is

said to be sustainable. It

has a systemic logic and

therefore requires a strong

knowledge base. In this

study a knowledge

management IT-based

system is proposed.

Table 1

2.2 Problem statement

The basic idea behind designing an Expert System for Agriculture is to accelerate the rate of

good agricultural practices in India. A DSS is a computer based program that assists with the

7

decision making process [2]. The program can be qualitative, quantitative or both. These

programs are important because agriculture productions are complex due to biological,

chemical and physical process involved, and require a great deal of information to be

processed for proper management. We all know India is an agricultural country, with

agricultural sector contributing to the largest share in GDP. The proposed project would serve

as a live example of the role of ICT for sustainable development [10]. We all know that

agricultural sector has the greatest potential for improving rural livelihood and eradicating

poverty, all we need to have a good decision making analytical skills with the sound

knowledge about the yield of a crop. The proposed system is a communication channel

between the thousands of farmers and agricultural scientists in India. Agriculture has been a

part of human life since the beginning of the human race and the need for agricultural

information is probably almost as old as agriculture itself. Throughout history, in many

civilizations, there have been libraries that have included agricultural information, and

separate agricultural libraries were established in Europe in the mid-eighteenth century.

In India, special attention was paid to development of an agricultural research infrastructure

immediately after Independence. The future growth in agriculture must come from new

technologies which are not only "cost effective" but also "in conformity" with natural

climatic regime of the country, technologies relevant to rain-fed areas specifically; continued

genetic improvements for better seeds and yields; data improvements for better research,

better results, and sustainable planning; bridging the gap between knowledge and practice;

and judicious land use resource surveys, efficient management practices, and sustainable use

of natural resources. Technology of today would help to make the smart and competent

farmer of tomorrow.

Developing such kind of an Advice Dissemination System can prove to be a revolution in the

agriculture of India, as it would be helpful in the production of a quality crop, helping our

farmers and ensuring food for all, the concept of the “National Food Security Bill” can’t be

achieved without the help of our agricultural sector [11]. Basic aim is to have another Green

Revolution in the country powered the theories and concepts of Information &

Communication Technology.

2.3 Solution approach

Our Proposed Expert System would gather the knowledge by both manual &

automated methods. Knowledge will be extracted from variety of sources including

8

books, bulletins, photographs taken by expert/farmer and human experts, concerning

the identification & control of insect-pests/diseases, schedules for irrigation and

fertilization and selecting the right pulse crop for a specific field

On the basis of knowledge acquired, a knowledge-base will be developed in the form

of production rules for pulse agricultural activities.

Interpreting & manipulating the knowledge by the reasoning methods.

It will provide a detailed analysis of the soil profile & climatic conditions of a

particular agricultural region under critical study.

Providing an Expert’s View for post harvesting and irrigation techniques thus

providing with proper management of water & other resources. Capable enough to

handle uncertainty and is based on Fuzzy Logic Approach.  Fuzzy logic has been

extended to handle the concept of partial truth, where the truth value may range

between completely true and completely false. Furthermore, when linguistic variables

are used, these degrees may be managed by specific functions.

Use of Real & Authenticated Data available till date, with the help of Indian Council

for Agriculture Research & Ministry of Agriculture, New Delhi, India.

Use of the concept of Bioinformatics in agriculture, providing an expert decision &

diagnostics based on the input of DNA structure of a particular crop under a specific

diseases/condition.

The proposed expert system will be made up of two main components

1. Advisory component: It will consist of two sub-systems

Diagnosis : This will be used to identify diseases & insect-pests of pulses on the

basis of plant damage symptoms

Control measures: This will recommend most appropriate treatments (cultural

practices, chemical control etc.) for the identified diseases & insect-pests

2. Strategic: It will consist of three sub-systems

9

Irrigation: This will produce a schedule for irrigation, including No. of irrigation,

critical stages of irrigation, depth of irrigation(cm.) & total water

requirement(cm.)

Fertilization: This will generate a fertilization regime, including fertilizer type,

source of fertilizer, dose, & application time

Crop economic: This will used to seek advice on selecting the right pulse crop in

the right season for a particular field/location. It will carry out cost benefit

analysis for different pulse crops including cost of cultivation, expected yield &

prices

2.4 Empirical Study

Expert system evolved as the first commercial product of Artificial Intelligence and is now

available in the large number of areas. The potency, scope and appropriateness of expert

system in the area of agriculture have been well realized two decades back in developed

countries (Carrascal et al., 1992; Perini et al., 2005) [8] and several successful systems have

been developed in the field of agriculture. In the recent years, main focus of agricultural

expert system applications research is on crop management and plant disease and insect-pests

diagnosis (Plant, 1989; Robinson, 1996; Chakraborty et al., 2008; Johnen et al., 2000; Chen

et al., 2002; Ismail et al., 2001) [2]. These areas are followed by irrigation management,

fertilization management, varietal selection, farm management, crop economics etc.

Expert System is not new for crop disease diagnostic domain. Attempts have been made by

various research workers in various institutions/universities for developing expert systems in

the field of agriculture. Since, the use of Expert Systems in agriculture is at an early stage and

very few are available in market. Based on our literature survey, we have to give the

overview of some of the expert systems to diagnose the insect-pests and diseases of many

crops. The table (Table 6) shows major types of expert systems currently underutilization in

agriculture.

1. COMAX [8] provides information on integrated crop management in cotton. It is

designed for use by farmers, farm managers, and county and soil conservation agents.

The system uses a combination of expert-derived rules and result generated by the

cotton - crop simulation model GOSSYM. It requires external information such as

weather data, soil physical parameters, soil fertility levels, and certain pest

10

management information. From this input of data, the system produces daily

management decision recommendations.

2. CALEX [1] package is modular, and domain specific modules were organized into

four broad categories: agronomic management, pest management, economic

management, and diagnosis. The objective of the CALEX Project was to develop a

general purpose shell program that can be used by growers, pest control advisors,

consultants, and other managers for overall agricultural management decision support.

The package was written in 'C' language. A major function of the CALEX program

was to generate schedules for in-season commodity management both CALEX/cotton

and CALEX/Peaches was developed and tested scheduling is done in terms of

activities. Each activity represents a major type of management decision that must be

made. For example, in CALEX/cotton the activities consist of spider mite, lygus bug,

verticillium wilt, nematodes, nitrogen growth regulator and crop termination.

3. CUPTEX [8] currently provides services on disorder diagnosis, disorder treatment,

irrigation scheduling, and fertilization scheduling and plant care subsystem. It was

developed in KADS. KADS is a methodology for building knowledge - based system.

KADS was used for representation of the inference and task knowledge. Finally,

LEVEL 5 object was object for the implementation.

4. CITEX [2] currently provides services on assessment of farm, irrigation, scheduling,

and fertilization scheduling disorder diagnosis and disorder treatment. CLAES has

developed and expert system for cattle and buffalo health caring the scope of this

expert system is milted to newly born calves (up to four weeks age) and contains

clinical diagnosis and line of treatment.

5. NEPER WHEAT [2] was developed at the Central Laboratory of Agricultural Expert

System (CLAES) in Egypt. It performs various functions viz., Advice the farmer on

field preparation, control pests and weeds, manage harvests, prevent malnutrition,

design schedule for irrigation and fertilization, select the appropriate variety for a

specific field, diagnose disorder, suggest treatments etc. It is an easy-to-use in

Microsoft Windows based application with an English and Arabic interface.

6. TOMATEX [8] provides its users with disorder diagnosis and recommendations

about how to treat these disorders. This version contains recommendations concerning

various agricultural activities viz., disorder diagnosis, disorder treatment etc. It

includes the causes of user compliant and also verifies the user assumption. The

output includes a complete specification about the treatment operation: disorder name,

11

materiel name, material quantity, made of entry, method of application, and the tool

used in the treatment operation, application time, and advice. The system is

implemented using special expert system called knowledge representation object

language (KROL), developed at the CLAES. The system is running on personal

computers under Window 95 environment, which facilitate subsequent porting to run

on the Internet to be accessible World Wide.

7. LIMEX is an integrated expert system with multimedia that has been developed to

assist lime growers and extension agents in the cultivation of time for the purpose of

improving their yield. The scope of LIMEX expert system includes: assessment,

irrigation, fertilization and pest control. This system was augmented with multimedia

capabilities as enhancing an expert system by the integration an expert image, sound,

video and data, allows for a good feedback from users, assists in better understanding

of the system, and allows for more flexibility in the interactive use of the system. It

was developed using an adapted KADS methodology for the knowledge part. CLIPS

TM Ver. 6.0 shell on Windows and CLIPS Object - oriented language (COOL) was

used for development of Expert System [1].

8. FPCES is a fababean pest control expert system (FPCES) is being developed jointly

by CLAES and ICARDA to address the needs for diagnosis and treatment of diseases

that attack this crop. The system will display all possible disorders and related

symptoms that appear during crop growth. The user will be able to examine the

symptoms and correctly diagnosis the problem. The system will then offer possible

solutions. It will enable the user to view pictures and videos of the disorders and help

in correct diagnosis.

9. VEGES is a multilingual expert system for the diagnosis of pests, disease and

nutritional disorder of six greenhouse vegetable viz., pepper, lettuce, cucumber, bean,

tomato, and aborigine. It provides the user with a diagnosis on the basis of a brief

description of the external appearance of the affected plant. It then suggests method to

remedy the problem (e.g., fertilizer, adjustment, fungicides or pesticide applications).

The system is accompanied by a new language translation module which allows a

non-specialist user (e.g. extension officer) to translate the knowledge base to the

native language or dialect of the local farmers.

10. POMME [8] provides information about pest and orchard management of apples.

This system provides growers with knowledge about fungicides, insecticides, freeze,

frost and drought damage, non-chemical care options as well as information from a

12

disease model. External information such as weather data including forecasts and crop

symptoms are utilized by the system to generate management decision

recommendations. The system contains more than 550 rules. PROLOG language was

used to build POMME.

11. PLANT/ds supports to diagnosis of soybean diseases and can be used by growers and

country agents alike. It contains diagnostic knowledge represented by decision rules,

which specify all conditions indicating each disease. Additionally, it uses external

data such as weather input, plant growing conditions, and plant symptoms.

12. PLANT/cd predicts corn damage resulting from the black cutwarm using a

combination of rules and a set of black cutworm simulation models. Required input

includes: trap counts, a measure of field weedicides, and the age spectrum of larvae,

soil conditions, and corn variety information.

13. RID was essentially an exploratory process that was performed in incremental steps

beginning with the acquisition of knowledge. Modular techniques were adopted.

Three independent modules were developed. These modules are designed using

PROLOG programming language. Main module consists of facts and rules which are

being used for diagnosing various insects and diseases of rice crops. A symptom

module consists of the plant damage symptoms caused by various diseases and insect

and describe module consists of brief description and control measures for all the

diseases and insects of rice crop. In knowledge acquisition, system provides

information on 10 important disease and 10 important insect pests besides information

on plant damage description, disease symptoms insect-pests description and other

necessary information.

14. CHICKBUG is a computer based decision aid for extension agronomists,

agribusiness personnel, grain growers and student. It provides information relating

insect-pest management in chickpea and other winter grain legumes. The program

provides information about - Haliothis and derives the required advice, which can

used to make a sound management decision [2].

15. SOYBUG was developed to advice Florida farmer on control of four important pests

of soybeans: Velvetbean caterpillar, stinkbug, corn earwarm, and soybean looper.

System integrates a variety of phenology and economics and gives specific

recommendations of pesticides and application rates. A major goal of the SOYBUG

project was to develop working knowledge acquisition techniques. SOYBUG was

13

built using INSIGHT 2 + expert system shell (Level V Research, 1987) running on an

IBM - PC compatible microcomputer [1].

16. HERB a computer-based expert system for Soybean weed management developed at

North Carolina State University was evaluated for managing weeds under Georgia

conditions. Field evaluation indicated that HERB was not highly accurate for

predicting final yield loss because of weed species senescence and environment

extremes. It also provides herbicide application recommendations based on a

weed/crop competition research data base and a number of parameters including weed

species present, population counts, rotational crop plans, herbicide efficacy and cost,

estimated weed free yield, and expected crop price per unit. HERB was written in the

CLIPPER language from Nantucket Corporation (Los Angeles, A) [8].

17. MANGODSP provides diagnosis for insect pests, diseases and physiological disorder

of Mango Crop. PROLOG, a logic programming, language, was used to build

MANGODSP. PROLOG [1] represents rules and facts uniformly, thus, facilitating

knowledge base construction. The programming of the system was done stepwise.

Initially a program for diagnosing diseases was written, and then it was expanded to

important diseases, insect-pests and physiological disorders. Rules were used to

construct a knowledge base for this system. The knowledge for identification and

control of insect-pests, diseases and physiological disorders derived from written

sources was efficiently captured in this rule - based system. Knowledge engineering

techniques were used to diagnosed the diseases, insect-pest and physiological

disorders and control advice to user. Question - Answer interface was used to put

queries for the user and gives advice to user.

In addition to systems which have been cited in literature, a number of expert systems are

currently under development. A recent Current Research Information System (CRIS) search

shows that the main focus of agricultural expert system application research is on crop

management and plant disease and pest management. These areas are followed by soil

erosion, irrigation management, prediction and control, resource conservation, crop

management, general farm management and decision making.

3. Implementation and Testing

14

3.1 Overall description of the project

The basic idea behind designing an Expert System for Agriculture is to accelerate the rate of

good agricultural practices in India. Our Proposed Expert System would gather the

knowledge by both manual & automated methods. Knowledge will be extracted from variety

of sources including books, bulletins, photographs taken by expert/farmer and human experts,

concerning the identification & control of insect-pests/diseases, schedules for irrigation and

fertilization and selecting the right pulse crop for a specific field On the basis of knowledge

acquired, a knowledge-base will be developed in the form of production rules for pulse

agricultural activities.

3.1.1.1 Purpose

This section covers the requirements specifications for the web based agriculture expert

system. This document does not provide any references to the other component of the

System. All the external interfaces and the dependencies are also identified in this document.

This section is defined for the developers of the expert system that is being developed for the

pulse farmers across India.

3.1.1.2 Scope

1. Scope is to build a Web Based Agriculture Expert Decision support system.

2. Develop a knowledge-based system for pulse agricultural activities

3. On the basis of knowledge acquired, a knowledge-base will be developed in the form

of production rules for pulse agricultural activities.

4. Develop a user-friendly diagnostic system and suggest appropriate treatments

5. Propose proper irrigation and fertilization schedule

6. Propose most economic pulse crop based on cost benefit analysis

7. It will provide a detailed analysis of the soil profile & climatic conditions of a

particular agricultural region under critical study.

3.1.1.3 Definitions, acronyms, and abbreviations

DSS – Decision Support System - Expert System is a knowledge-based programme that

provides expert ‘quality’ solutions to problems in a specific domain.

3.1.2 Overall description

15

This section gives background information about specific requirements of the web based

Agriculture expert system service to be developed in brief. Although we will not describe

every requirement in detail, this section will describe the factors that affect the final product.

The implementation of the project starts with setting up knowledge acquisition by developing

knowledge base from manual and automated ways. Any further transaction like Pest/disease

identification, irrigation management, fertilizer management and crop economics will be

implemented for the farmer side.

3.1.2.1 Product perspective

This product is intended for the farmers across India who can interact with agriculture experts

concerning pulse crops. Product will be deployed to web site and all users of the product will

access by use of the website. Website will be main user interface where users can operate all

the provided functionality.

The product will have two sub parts (i) Knowledge base, which will be for the experts and

will take care of Knowledge acquisition and (ii) PulsExpert System, for the crop

farmers/user. Website will only be the interface for the user data and the execution of

provided functionalities. To use product, farmers are required to contact their nearby

education centre, where the authorities will help farmers interact with web interface.

Fig 1: Major components

3.1.2.1.1 System interfaces

16

The product will be readily available from any computer with access to internet.

This server is generally available for use around the clock.

Reliability will be ensured by a thorough verification and control plan devised for

registered users.

Future modifications and additions should then be easy to implement, since only

changes to certain modules will be necessary.

3.1.2.1.2 User interfaces

Allows user to view pictorial representation for the solution.

User will attain solutions from the experts based on confidence.

User can attain information regarding, irrigation and fertilizer management.

3.1.2.1.3 Hardware interfaces

Processor : Intel P-IV (or above)

RAM : 512 MB (or above)

Hard Disk : 20 GB (or above)

3.1.2.1.4 Software interfaces

Operating System : Windows 98, 2000, ME, XP, NT.

Web Server : IIS Server

Web Browser : IE 4 or Netscape 4x or upwards

Database : Microsoft SQL Server 2005

3.1.2.1.5 Communications interfaces

The users (Farmers) can interact with the experts through this web based product, by

contacting their nearest education resource centre, which has internet connections, if they

themselves aren’t equipped with. Basic windows are required with browser installed to access

this product.

3.1.2.2 Product functions

17

Our Proposed Expert System would gather the knowledge by both manual & automated

methods. Knowledge will be extracted from variety of sources including books, bulletins,

photographs taken by expert/farmer and human experts, concerning the identification &

control of insect-pests/diseases, schedules for irrigation and fertilization and selecting the

right pulse crop for a specific field. On the basis of knowledge acquired, a knowledge-base

will be developed in the form of production rules for pulse agricultural activities. Interpreting

& manipulating the knowledge by the reasoning methods.

It will provide a detailed analysis of the soil profile & climatic conditions of a particular

agricultural region under critical study.

Fig 2: Basic functioning and relationship

3.1.2.3 User characteristics

Users of this web based expert system will mainly be farmers across India and agriculture

experts, with whom the farmers can interact. Farmers can contact their nearby education

centre to access this product with the help of authorities, and we assume that our users will

already be informed about basic functionality of the product. Also clear documentation and

tutorials about the product feature will be provided.

3.1.2.4 Constraints

18

Developers of the product should be aware that main feature of the intended product

is portability. So they should use common libraries and tools that can work with all

the common internet browser application with no problem.

Developers should also be careful about the privacy of experts and the data. Since the

product will be web application, all the data will be kept on the database and

necessary precautions should be taken to protect data, since it authentic data provided

by IIPR, India.

3.1.2.5 Assumptions and dependencies

The farmers are connected to an education/learning center where they can access this

product.

Education resource centers have the basic hardware and software specifications for

the product to run with a reliable internet connection.

The authorities are aware of the functionality and requirements of the product.

Solutions to the issues will be generated on the basis of questionnaire, which is to be

filled by the user upon choosing a specific crop.

Microsoft SQL server 2005 as the back end which is supported by Windows.

3.1.2.6 Apportioning of requirements

Restricted to pulses crops and its different varieties only, could be extended for other

varieties of crops too.

Provision of the videoconferencing facility for a long distance face to face

communication between an expert and a user in order to advice the farmers and

extension workers at nick of time.

Provision of video solutions to the farmers for a better understanding.

3.1.3 Specific Requirements

This section will describe the requirements of the product in detail.

19

3.1.3.1 External interfaces

This group of requirements is related to external interaction of the workspace with outer

world. For user to interact with the workspace, product will provide a web based GUI

interface for the users.

Entry: In this module user can connect to the internet and access the web application

from any browser, and then select the desired field, for which help is required.

Issue: After selecting the field the user/farmer can select/enter options according to the

questionnaire designed to that particular field.

Return: This module on the basis of the entries from the farmers will generate solutions,

with confidence percentage, with the help of knowledge acquisition.

3.1.3.2 Functions

• Our Proposed Expert System would gather the knowledge by both manual &

automated methods from books, journals & Agricultural Scientists all over the

country.

• Providing an Expert’s View for post harvesting and irrigation techniques thus

providing with proper management of water & other resources. Capable enough to

handle uncertainty and is based on Fuzzy Logic Approach.

• Use of Inference Engine. It is essentially a computer program to process symbols that

represent objects. It can interpret knowledge in the knowledge base and also perform

logical deduction and manipulations.

• Use of the concept of Bioinformatics in agriculture, providing an expert decision &

diagnostics based on the input of DNA structure of a particular crop under a specific

diseases/condition.

3.1.3.3 Performance requirements

Since this software is going to be web – based, it does require a server machine with

internet access. Server machine should have a powerful CPU and high speed internet

access so that it can handle multiple users at the same time.

Another performance requirement is the storage space. Higher storage space means

more user and bigger workspace per user so higher the storage, better the

performance. Performance requirement by the user side is, web application should be

20

developed as a lightweight web app so that it can work on almost any platform even

with slower internet connections.

Expected number of simultaneous user should be at least 100. System should be able

to deal with 100 users at the same time. Also database of the system should handle at

least a thousand of users at any periods

The management system will work properly as long as the local machines meet the

application’s minimum hardware requirements.

3.1.3.4 Design constraints

The user are mainly farmers, who might not have access to internet connections or may be

residing in remote areas, so they need to contact their nearest education centre, where the

personnel responsible should be aware about how about of the system and be able to work as

per the system requirements and application efficiency.

Basic hardware and software specifications should be available with the resource

centers.

User interface to be easily readable and understandable, for the better understanding of

the farmers.

User should be able to express his/her problem or issues efficiently, for the proper

understanding of the experts to recognize the issue, and thereby providing a solution.

3.1.3.5 Software system attributes

3.1.3.5.1 Reliability

With a need for a reliable internet connection, other reliability requirements are:

Processor : Celeron 500 MHz or more, Intel Pentium III (or above)

RAM : 128 MB (or above)

Hard Disk : 1 GB (or above)

3.1.3.5.2 Availability

Operating System : Windows 98, 2000, ME, XP, NT.

Web Server : IIS Server

21

Web Browser : IE 4 or Netscape 4x or upwards

3.1.3.5.3 Security

System security will not be an issue, because the experts and authorised personnel will

only have access to the compiled version.

To allow only the experts to login, one time password or password encryption can be

used.

If the data is to be private and only viewable by an expert, then an encryption tool must

be built into the product.

3.1.3.5.4 Portability

Main purpose of developing web-based expert system is to improve the portability of

software development process. To improve portability, software should run on variety of

platforms and variety of connection speeds. As explained in the performance requirements

section, software should be lightweight so that it can run on a machine with slow internet

connection. Portability also means running on most number of different platform without an

additional effort. To achieve this, web application will be developed by using the common

technologies and tools which are provided by all common web browsers and operating

system such as HTML5, asp.net.

Since the product is web-based, so it can be accessed from anywhere, with an internet

connection and a browser. For the system to meet the requirements, the particular specified

processer and memory need to be ported.

3.2 Design

Expert System (ES) is one of the few branches of Artificial Intelligence (AI) that has

transitioned from research laboratories to the world of commercial and Industrial

applications. Expert systems incorporate human expertise in a computer program to allow

22

this program to perform tasks normally requiring a human expert. Expert System is one of the

most popular forms of AI-Based system which simulates characteristics of human

intelligence and its sensory capabilities. Expert System is special computer software capable

of carrying out analysis with reasoning and functions in narrowly defined domain at

proficiency levels of an expert. It currently offers promise for immediate applications in

solving problems based on computer programs that achieve a high level of performance in

some specialized problem domain. It can solve critical problems by making logical

deductions without taking much time. Hence Expert System is a computer advisory program

that attempts to imitate the reasoning processes of experts in solving difficult problems. Such

system can also be used by the expert as knowledgeable assistant. Expert Systems are used to

propagate scarce knowledge resources for improved, consistent results.

(Problems, Data, Pictures, Questions)

(Formalized

Structured

Knowledge)

(Knowledge, Experiences, Concepts, Solutions)

Knowledge Acquisition Knowledge Representation

Fig3: Structure of the proposed Knowledge base development

3.3 Implementation details and issues

23

Domain Experts (Plant pathologists) and Literatures (Books,

Journals, Bulletins, Diagnostic

Knowledge Engineer

Knowledge Base

(Facts & Rules)

Fig 4

Novelty/benefits

24

Designing a Web Based Expert System that helps in:-

• Advancing the farming techniques & practices.

• Using Information & Communication Technology for Sustainable Development.

• Analyzing the Agricultural Statistics & its effect on the Indian GDP.

• Developing a Knowledge Based System, that diagnoses the problems in a crop and

suggests appropriate treatment with proper irrigation and fertilization schedule.

• Analyzing the Crop Economics based on cost benefit analysis.

• If the farmers would be aware and know all the things the scope of selling would

increase and poverty would decrease.

3.3.1 Description of Modules with respect to design

The main components of an expert system are: Knowledge-base, Inference/Control

mechanism, User interface and Development tools, which are depicted systematically in Fig

Knowledge-base: It is a repository in some symbolic manner of the knowledge about facts,

judgments, rules, intuition and experience in connection with a particular problem. A

combination of symbolic representation of knowledge within the knowledge-base, various

kinds of knowledge-based structures, and relationship between the structures make it possible

to represent common-sense information.

In agricultural application, the knowledge base is usually a set containing collection of rules,

each of which captures some piece of knowledge about how to reason in a specific problem

area to be addressed by the expert system. The first step to build up a knowledge base system

is Knowledge acquisition [12]. In this process, experts from a certain field, e.g., pulse

production technology are consulted and the optimum method is discussed to grow and care

for popular pulse crops like chickpea, pigeonpea, mungbean and urdbean. They provide

detailed information on a successful production technology based on resources available with

the growers such as soil types, weather conditions and types of irrigation, pest control,

fertilization, disease treatment and economic evaluation. Knowledge engineers serve as

mediatory between the expert and the computer that will emulate their expertise. The

engineer acquires the expert’s knowledge through interviews and document analysis.

25Domain Experts

Domain Experts

Interact

Interview/Questionnaire Fetch & validate

Encode

Manual Knowledge Acquisition Interactive-Computer-based

Knowledge Acquisition

Fig. 5: Knowledge Acquisition methods

The second step is Knowledge representation to codify knowledge (e.g. encoding knowledge

as production rules) in rule-based form. In this form, knowledge about an area of expertise

(domain) is encoded as a set of condition/action links.

IF (Condition) THEN (Action)

The specified action is performed if the required condition(s) is present in the database. Some

of the following ways used to represent knowledge in a knowledge base are:

Rule-based: A rule is a conditional statement for a particular action that is supposed

to take place under certain set of conditions. In a rule-based system, the rules are

entered into the knowledgebase without programming. It usually works by applying

the rules, recording the results and implementing new rules based on the changed

situation.

Semantic net: It uses both predicates and attributes to represent objects, and shows

relationship between the objects. The method of choice depends on how the

knowledge engineer thinks about the knowledge.

26

Knowledge Engineer

Knowledge Base

Knowledge acquisition

system

Knowledge Engineer

Knowledge Base

Inference Engine: It is essentially a computer program to process symbols that

represent objects. It can interpret knowledge in the knowledge base and also perform

logical deduction and manipulations. Some methods of reasoning that are commonly

used in rule-based inference engines are:

Forward (data-driven) chaining: The system attempts to reason forward from the

given facts to a solution.

Backward (goal-driven) chaining: The system works backward from a hypothetical

solution (the goal) to find evidence supporting the solution.

First found strategy: It is known as conflict resolution strategy (e.g. more than one

rule has conditions that match the work space).

User Interface: This mechanism allows end-users to run the expert system and interact with

it. One of the most important interactions is with the system's explanation facility. This

interaction can occur both during the reasoning process and after its conclusion. A significant

user-interface is provided with the expert system to allow query, advice, explanation and

interaction.

Fig 6: Modules for agriculture expert system, with user interface

Development Tools: These are the means for building and testing the knowledge base. They

are designed primarily for use by knowledge engineer. The most common languages of AI

are PROLOG and LISP (LIST Processor) [12], which are a symbolic manipulation languages

27

and can handle predicate calculus logic. Rapid and efficient development of an Expert

System is enhanced if powerful development tools are available to meet the needs of the

developer. The development of an Expert System is almost always an interactive task

involving the cycle of expert query, database formation, development of the inference

strategy, verification of system performance and so on.

1.

2.

3.

4.

Fig. 7: The proposed system architecture

3.4 Testing

28

Identify types of domain specific

knowledge

Determine structure of the knowledge

Identify Knowledge acquisition process

Choose/design knowledge acquisition

features and customize

Knowledge sources ( Domain experts, books, journals,

bulletins, databases etc.)

Computer/Knowledge Engineer

3.4.1 Testing Plan

Type of Test Will Test

be

Performed

Comments/Explanation Component

Requirement Yes Requirements specification must contain

all the requirements that are to be solved

by our system.

Hardware and

working

Unit Yes Sets of one or more computer program

modules together with associated control

data, usage procedures, and operating

procedures are tested to determine if they

are fit for use.

Operating

procedures

Integration Yes Takes as its input modules that have

been unit tested, groups them in larger

aggregates, applies tests defined in an

integration test plan to those aggregates,

and delivers as its output the integrated

system

Individual

software modules

Performance Yes Redundancy and fail-over options should

be considered.

Identity Services

or network

connectivity

Stress No NA NA

Volume Yes Amount of data to be handled and

processed.

Data Entries by

experts

Load Yes Non-repudiation. Multiple logins

Security Yes Adequate security should be in place for

accessing identity and administration

interfaces

Login by experts

only and

protection of

database.

Table 2: Test Plan

3.4.2 Component decomposition and type of testing required

29

Test ID List of various Components Type of Testing Technique

1.

Links – internal and external

Unit Testing White Box – Lands

the user to the

required page.

2.

Forms – field validation,

Optional and Mandatory fields

Integration Testing White box –

validate if entries

correct, else show

error message.

3. Login Security Testing White Box – Only

the experts can

login with provided

credentials.

3. Database Integrity System Testing Black box – Only

experts update or

create the database.

4. Knowledge base creation/

update button

Performance Testing White Box – lands

onto the knowledge

acquisition page.

5. PulsExpert System button Performance Testing White Box – lands

onto the knowledge

retrieval page.

6. Multiple experts access

knowledge base at same time,

from different location.

Load / Volume

Testing

Black Box –

Database is handled

properly and

solution to only the

associated

questionnaire is

provided.

Table 3: Component Decomposition and type of test required

3.4.3 List of all test cases

30

Test case id Input Expected Output Status

1. Click on Links –

introduction, objectives etc.

Land onto the

associated page

Pass

2. Forms – feedback form Validate if fields are

correct else show an

error message

Pass

3. Login by non – expert for

knowledge base

Access denied Pass

3. Login by expert for

knowledge base

Access given Pass

4. Knowledge base creation /

update

Redirect to

Knowledge Retrieval

page

Pass

5. PulsExpert System Button Redirect to

Knowledge

acquisition page

Pass

6. Multiple login Experts can access

knowledge base

without interruption.

Pass

6. Solution provide for

associated questionnaire

Solution to the

specified issue, under

the specified crop is

provided.

Pass

Table 4: List of Test Cases

3.4.5 Limitations of the solution

31

For now in terms of feasibility study and planning phase we have restricted and

refined our knowledge to the northern part of the country. Concentrating basically on

the villages and outskirts of Uttar Pradesh & NCR region, we have the most

appropriate data available till date.

Can be a failure in regions/villages who have extremely poor communication facilities

and suffering through the electricity problems.

Restricted to pulses crops and its different varieties only.

No provision of the videoconferencing facility for a long distance face to face

communication between an expert and a user in order to advice the farmers and

extension workers at nick of time.

Requires high capital, integration on a large scale.

Lack of empowerment of women, youth and the economically weak farmers are also

a major constraint

Weak and even dysfunctional Research-Extension-Farmer linkages

Need for NGOs, who has led innovation in information systems for rural

development, to focus on sustainability and scalability of rural information systems

including for agriculture.

3.5 Risk Analysis

Any uncertain situation or condition causing a failure or threat to the software is called a

risk and to mitigate such risk efficient risk management & assessment is necessary.

1 2 3 4 5 6 7

Risk

id

Classification Description of risk Risk Area Probability

(P)

Impact

(I)

RE

(P*I)

1 Database/ User id 5 5 5 25

32

2 Management

effort required

Identity Information/ user

data

5 5 3 15

3 Monitor and

manage risk

migration to cloud

services

5 5 1 5

4 Must monitor

and manage

risks

End Users 3 3 5 15

5 Management

effort useful

Login 3 3 3 9

6 Accept risks

but monitor

them

Network Connectivity 3 3 1 3

7 Substantial

management

required

Privileged Login 1 1 5 5

8 May accept

risks but

monitor them

Vendor lock-in 1 1 3 3

Table 5: Risk Analysis

33

References

[1] Carrascal, M.J. and Pau, L.F. “A survey of expert systems in agriculture and food

processing.” AI Applications, 6(2): 27-49, 1992

[2] Chakraborty, P. and Chakrabarti, D.K. “A brief survey of computerized expert systems

for crop protection being used in India.” Progress in Natural Science, 18(4): 469-473, 2000.

[3] Chu YunChiang, Chen TenHong, Chu-YC, and Chen-TH “Building of an expert system

for diagnosis and consultation of citrus diseases and pests”. Journal of Agriculture and

Forestry, 48(4): 39-53, 1999

[4] Kolhe, S. and Gupta, G. K. “Web-based Soybean Disease Diagnosis and Management

System.” Fifth Conference of the Asian Federation for Information Technology in

Agriculture (AFITA, 553-559), 2006.

[5] Wang Zhi-Qiang, Chen Zhi-Chao “A Web-based Agricultural Decision Support System

on Crop Growth Monitoring and Food Security Strategies” Third International Symposium

on Computer Science and Computational Technology(ISCSCT ’10) Jiaozuo, P. R. China,

14-15, ISBN 978-952-5726-10-7, pp. 487-491, August 2010.

[7] Russell Yost, Tasnee Attanandana, Carol J. Pierce Colfer and Stephen Itoga, “Decision

Support Systems in Agriculture: Some Successes and a Bright Future”, University of Hawai`i

at Manoa, USA.

[8] D.J. Power “A Brief History of Decision Support Systems”, May 31, 2003.

[9] Laurens Klerkx , Andy Hall, and Cees Leeuwis , “Strengthening Agricultural Innovation

Capacity: Are innovation brokers the answer?”, International journal of agricultural

innovation and research, 2009.

[10] UNDP, “Promoting ICT based agricultural knowledge management”, 2102.

[11] I V Malhan, Shivarama Rao, “Agricultural Knowledge Transfer in India: a Study of

Prevailing Communication Channels” Library Philosophy and Practice, 2007.

[12] P.H. Heinemann, “Decision Support System for Food and Agriculture”, System Analysis

and Modelling in Food and Agriculture, 2012

34

Appendices:

A.

Table 6: Existing Agricultural Expert System

Specification Field of Application

COMAX

CALEX

CUPTEX

CITEX

NEPER WHEAT

TOMATEX

LIMEX

FPCES

VEGES

POMME

PLANT/ds

PLANT/cd

RID

CHICKBUG

SOYBUG

HURB

MANGODSP

Integrated crop management in cotton

An integrated expert decision support system for Agricultural

management

An Expert System for cucumber crop production

An Expert System for orange production

An Expert System for irrigated wheat management

An Expert System for Tomato diseases

A multimedia Expert System for lime production

A Fababean pest control Expert System

A multilingual expert system for the diagnosis of pests, diseases and

nutritional disorders of six greenhouse vegetables

Pest and orchard management of apple

Diagnosis of soybean diseases

Prediction of corn damage from the black cutworm

Prototype Diagnostic system for Rice insect-pests and diseases

Insect pest management in chickpea

An Expert System for soybean insect pest management

Expert System for Soybean weed management

Diagnosis system for insect-pests, diseases and physiological disorder

of Mango crop.

35

B. Gantt Chart

36