The document discusses expert systems for effective extension services in agriculture. It covers the history and development of extension services from the 19th century using personal contacts to the 21st century using electronic media. It also examines the human and financial resource challenges facing extension and outlines the need for an expert system to help extension workers and farmers make appropriate decisions.

1. 1

2. EXPERT SYSTEM FOR EFFECTIVE
EXTENSION SERVICE
2

3. EXTENSION SERVICE
- Extension workers
- Extension Teaching Methods
3

4. PERSONAL & GROUP CONTACT IN THE 19TH
CENTURY
4

5. USE OF PRINT MEDIA IN THE 20TH CENTURY
5

6. USE OF ELECTRONIC MEDIA IN THE 21ST
CENTURY
6

7. Poor
ratio of SMS
to agents
Lower level of
education of
extension agents
Short supply
of extension
agents
More number of
farmer per extension
worker
More area to
be covered by
agents
Less number
of female
extension
agents
Human Resource
of Extension
7

8. Poor
transportation
facility to agents
Lower pay to
extension agents
Less availability
of
programme cost
Poor housing
to extension
workers
Poor
communication
facility to agents
Very little
expenditure
per farmer
Financial Resource
of Extension
8

9. What should I do?
EXTENSION CLIENT
9

10. Market
5 km
Land
2 acres
Labour
5 members
Capital
Rs. 5000
Power
Pair of bullock
Source of
Irrigation
Pump set
My
resources
10

11. Which choice
is best?
Which choice
is income
generating?
Which choice
requires less
labour?
Which choice
requires less
land area ?
Which choice
is not much
affected by
season?
Which choice
requires less
input?
Fisheries
Crops
Fruits
Piggery
Flowers
Poultry
Dairy
Bee
keeping
11

12. Magazine
Television
Extension
worker
Neighbour
News paper
Friend
Who will help me
in appropriate
decision making?
The answer to all these questions is:
EXPERT
SYSTEM OF
EXTENSION
12

13. INTRODUCTION
13

14. To know the concept and meaning of Expert
system.
To know the need and objectives for developing
Expert system.
To review the studies related to Expert system.
OBJECTIVES
14

15. Concept OF Expert system
An expert system is software that attempts to provide an answer
to a problem, or clarify uncertainties where normally one or
more human experts would need to be consulted. Expert
systems are most common in a specific problem domain, and
is a traditional application and/or subfield of artificial
intelligence.
Expert systems were introduced by researchers in the Stanford
Heuristic Programming Project, Edward Feigenbaum with the
Dendral and Mycin systems.
15

16. Principal contributors to the technology were Bruce Buchanan,
Edward Shortliffe, Randall Davis, William vanMelle, Carli
Scott, and others at Stanford. Expert systems were among the
first truly successful forms of AI software.
Cont…
16

17. Is an intelligent computer program that uses knowledge
and inferences procedures to solve problems.
Is a system that employees human knowledge captured
in a computer to solve problems that ordinarily require
human expertise.
( Daniel Hunt,1986 )
EXPERT SYSTEM – MEANING
17

18. Cont….
Designed to stimulate the problem-solving behaviour of an
expert in a narrow domain or discipline.
(Bahal et al,2006)
An expert system is simply a computer software
programme that mimics the behavior of human experts.
(Ahmed Rafea, 2002)
18

19. DIFFERENCE BETWEEN CONVENTIONAL
AND EXPERT SYSTEM OF EXTENSION
Sl.
no.
Conventional Extension Expert System of Extension
1. Universal approachability of
same information is a problem.
Universal approachability of same
information is possible.
2. Information is given what ever
is available without
considering needs and
resources.
Information is chosen based on their
needs and resources.
3. No Cost benefit analysis Cost benefit analysis
4. Information flow depends on
availability of agent
Information through Cyber Cafe at any
place at any time.
5. Require users to draw their
own conclusion from facts.
Conclusion is drawn based on the
decision given by the expert
Bahal et.al.,2004
19

20. FLOW OF INFORMATION IN
EXPERT SYSTEM
DOMAIN EXPERT
KNOWLEDGE ENGINEERS
END USERS
(farmer, extension worker)
20

21. HOW EXPERT SYSTEM WORKS ?
 Components of an expert system
 Development tools
21

22. COMPONENTS OF EXPERT SYSTEM
Knowledge
acquisition
Knowledge
representation
User interface for
query, explanation,etc.
Inference/control
mechanism (e.g.
forward chaining.
Knowledge base
(Devraj et. al.,2001)
22

23. KNOWLEDGE
BASE
hypothesis
facts
processes
objects
attributes
definition
events
rules
KNOWLEDGE BASE MAY REALLY
INCLUDE MANY THINGS
(Berg,2002) 23

24. CONCEPTUAL DESIGN
Expert System
of Extension
Knowledge
Base
Domain
Expert
Knowledge
Engineer
Knowledge, Concepts, Solutions
Data, Problems, Question
Structured
Knowledge
Knowledge Acquisition Module
Technical &
Extension bulletins
Textbooks
Facts
Research Findings
Bahal et.al.,2004
24

25. KNOWLEDGE ACQUISITION FOR KNOWLEDGE
ENGINEER
 Structured interviews
 Unstructured interviews (tape recording,
video taping)
 Note-taking and memory
 Gestures
(Spangler et.al.,1989)
25

26. KR SYMBOLS INFERENCE
Logic Resolution principle
Rule based Backward (top- down, goal directed), forward
(bottom- up, data driven)
Semantic frames Inheritance and advanced reasoning
KNOWLEDGE REPRESENTATION
FORMALISMS & INFERENCE
(Berg,2002)
• A method to represent the knowledge about the domain
• Knowledge about an area of expertise is encoded
26

27. 27

28. INFERENCE ENGINE
 A computer program to process symbols that represents
objects.
 It can interpret knowledge in the knowledge base and
perform logical deduction and manipulation
28

29. USER INTERFACE
Allows the end-users to run the expert system and interact
with it.
Allows query, advice, explanation and interaction
29

30. DEVELOPMENT TOOLS
 Means for building and testing the knowledge base
 Designed primarily for use by the knowledge engineers
 Tools like FORTRAN , LISP etc.
30

31. ATICs Year of
establih
shment
Period of
service
Personal
visit
Through
letters
Telephone
help line
Farmers
field visit
Seminars/
Trainings
Total Benef
itted/
yr
KAUThrissur 1993 1999-
2003
985 531 2608 415 402 4941 988
ANGRAU, 1999 1999-
2005
2556 231 811 3 - 3601 514
RAU, 2000 2000-
2005
9300 - - - 100 9400 1566
SEKAUST, 2000 2000-
2005
376 - 438 35 204 1034 172
MPKV,
Rahuri
2001 2001-
2005
4675 626 3472 153 849 9774 1954
UAS,Dharwa
d
1996 1996 till
date
78,200 512 6420 562 321 86015 6616
Ahire et.al.,2008
Table 1:Dissemination of farm technologies by ATICs (Mode of service and no. of farmers benefitted)
31

32. Table 2: Distribution of internet subscribers in states and union territories
No State/ Union Territory As on 1.3.2002 As on 31.3.2003
1 Andaman & Nicobar 703 1112
2 Arunachal Pradesh 380 1010
3 Andhra Pradesh 234571 219218
4 Assam 9899 14440
5 Bihar 11 999 18895
6 Chandigarh 60228 38458
7 Chattisgarh 7827 9275
8 Goa 17494 19449
9 Gujarat 153515 195072
10 Haryana 12116 17015
11 Himachal Pradesh 3483 6410
12 Jammu & Kashmir - 10235
13 Jharkhand 11386 14199
14 Karnataka 263020 259121
15 Kerala 109170 136458
16 Mizoram 743 959
17 Manipur 630 1026
18 Meghalaya 1455 5285
19 Madhya Pradesh 65307 89501
20 Maharashtra 770634 948264
21 Nagaland 452 2536
22 Orissa 17303 22343
23 Pondicherry 8984 14275
24 Punjab 69499 69938
25 Rajasthan 102588 121322
26 Tripura 816 1194
27 Tamil Nadu 331840 329624
28 Uttaranchal 10902 19801
29 Uttar Pradesh 96828 120006
30 Sikkim 928 965
31 West Bengal 132013 142663
32 Delhi 732962 650209
Total 3239675 3500278
Source: NASSCOM and UNDP (2004: 23)
32

33. NEED OF EXPERT SYSTEM IN EXTENSION
 Agricultural technology is constantly changing day by day
 To cope with the overgrowing complexities of agricultural
technologies
 To make efficient and accurate decisions
GM
33

34. OBJECTIVES OF DEVELOPING
EXPERT SYSTEM
To enhance the performance of agricultural extension
personnel and farmer
To make farming more efficient and profitable
To reduce the time required in solving the problems
To help in performing the routine tasks thus leaving expert
for other important task
To maintain the expert system by continuously upgrading
the database.
(Hirevenkanagoudar et.al.,2005)
34

35. TRAITS FOR AN EXPERT IN PROBLEM-
SOLVING
 A rich knowledge base
 An organization of knowledge that is readily accessible
 Expert’s own knowledge and experience.
Spangler et.al.,1989
35

36. APPLICATION OF EXPERT SYSTEM IN
AGRICULTURE
• Crop production estimates
• Crop selection
• Soil management
• Plant diseases and pests mgt
• Weed management
36

37. MODULES OF EXPERT SYSTEM IN AGRICULTURE
Specification field of application
COMAX Integrated crop management in cotton
GRAIN MARKETING
ADVISOR
Determination of grain marketing alternatives
POMME Pest and insect management in apple
SOYEX Soybean oil extraction expert system
PLANT/ds Diagnosis of soybean diseases
MAIZE Maize expert system for field crop management
SEMAGI Weed control decision making in sunflowers
ESIM Expert system for irrigated management
Dept. of Agril.Processing,TNAU,2004
37

38. POTENTIAL ADVANTAGES OF
EXPERT SYSTEM
 Solves critical problems by making logical deductions without
taking much time
 It combines experimental and conventional knowledge with the
reasoning skills of specialists
 To enhance the performance of average worker to the level of an
expert 38

39. Training:
1. Conducted 64 training courses on usage of Expert
Systems for 465 researchers and engineers in the ARC,
extension agents, veterinary doctors and private sector growers
in the period from Dec. 1992 to March 2002.
2. Conducted 45 training courses to introduce Expert Systems
for 418 researchers and engineers in the ARC, Faculties of
Agriculture and Veterinary Medicine during the period from
May. 1995 to August 2001
ACHIEVEMENTS OF EXPERT SYSTEM
39

40. 3. Conducted 23 training courses on “Developing Expert
Systems” for 175 assistant researchers and engineers in the Lab
during the period from Oct.1994 to Nov. 2001.
4. Conducted 42 training courses on “Computer
Literacy” for 374 researchers and engineers in the ARC, and
young graduates from universities and institutes during the
period from Nov. 1994 to June 2002.
40

41. Research
1. The impact on enhancing the performance of extension
workers when using the expert system was measured. A
tangible enhancement was observed which ranges from
80% to 157% in different expert systems.
2. Experiments were conducted to measure the economic and
environmental impact of using expert system in the field. The
experiments showed that net production has increased by
approximately 25%.
41

42. 3. The impact on environmental conservation was assessed
using two measures: water saving and chemicals usage reduction. It
was found that fields managed by expert systems used less water
by approximately 35% and less fertilizers by approximately 16%.
4. Established a Virtual Extension and Research Communication
Network in order to strengthen linkages among the research and
extension components of the national agricultural knowledge and
information system.
CLAES,2002
42

43. LIMITATIONS OF EXPERT SYSTEM
 Expensive computer program
 Mostly developed not in regional languages
 Requires AC power and internet connection all the time
 Complex software requires computer skilled personnel
43

44. CASE STUDY
Expert system for effective extension
Bahal et.al.,2004
Objective : to uplift the socio-economic and information needs of the
farmers for sustainable agriculture
Methodology :
conducted in 7 agro-eco-region-IV identified by the ICAR covering
seven states. Out of these states,11 districts were purposively selected
from where total 7 crops were selected
44

45. Sub domain
Season
Rabi Kharif
Cereals ----- Paddy
Pulses Pea -----
Oilseeds Pea -----
Vegetables Mustard -----
Flowers Gladiolus -----
Agribusiness Mushroom -----
Agribusiness PH technology of
mango
-----
SELECTED CROPS OF EXPERT SYSTEMS OF
EXTENSION BY SEASON
45

46. SELECTED STATES, DISTRICTS AND CROPS
Sl.
No.
State Districts Crops
1. Punjab Ludhiana Paddy, Mustard, Pea
2. Haryana Karnal
Gurgaon
Hisar
Paddy
Mustard
Tomato
3. Delhi Delhi Gladiolus, Tomato,
Mushroom, Pea
4. Rajasthan Bharatpur Mustard
5. Gujarat Anand Mustard
6. M.P. Datia Musatrd
7. U.P. Kanpur
Lucknow
Varansi
Tomato, Gladiolus
Mango
Pea and Tomato
46

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Online Agriculture Expert that Works!
It has been experienced that many times
the extension workers who are less
educated are not in a position to advice
the farmers according to their needs and
available resources for maximum profit.
It is also not possible that one can adopt
the same practical ……
This site is visited 3405 times since
10.12.2004.
Farmer/extension worker Click Here To
Enter In The System
Lead Institute
IARI
Core Groups
Collaborating Institute
IASRI
Resource Institutes
IIPR
NRC Rapeseed &
Mustard
IIVR
CCS HAU
IRRI
CIMMYT
47

48. 48

49. 49

50. Design of Expert system
50

51. Research studies
51

52. 1. An experiment study with post test design was
conducted by V.K. Jayaraghavendra Rao et al 1999
on purposefully sampled 40 visitors who showed
interest on the personnel computer and expert
system displayed in the IIHR staff at KISAN 93
exhibition at Pune.
.
52

53. Cont…..
Some of the findings of the study are:
• Perceived utility:
Study revealed that regarding awareness of expert system and
its probable problem use in transfer of technology.
The majority of all categories of potential clients expressed that,
they were not aware of expert system
2. Regarding usefulness:
Majority of the respondents expressed that, the expert systems
are very much helpful.
3. Perception of complexity:
Majority of the respondents felt that, expert systems are
relatively easy to handle and use.
53

54. 2. CLES(central library for agricultural expert system 1995
in Egypt) five years study conducted by Rafea et al 1995
regarding usage of expert system technology by Egyptian
ministry of agriculture.
Objective:
To Develop Methodologies, Tools To Facilitate Building
Expert System For Different Crops And To Study The Impact
Of Expert System Usage On Social And Economic Aspects.
54

55. • Findings
Study revealed that, there were the improvement of
knowledge engineer performance, the optimization of
agriculture production and the improvement of extension
worker performance.
Expert system integrated with other information
technologies can be used to strengthening the link between
research and extension.
55

56. CONCLUSION
56

57. 57

58. Thank you for your attention
58

59. SLIDE- INTRO
60

60. SLIDE OPENIN PAGE
61

61. 62

62. SLIDE OBJ
63

63. SLIDE DESIGN
64

64. Requirement and Quality of Compost
Composting
Compost can be prepared by using any one of the formulae given for the ingredients of the compost. Plant residues are mainly composed of celluose, hemicellulose and
lignin, which are not readily available for the mushroom growth. During composting plant materials are modified so that nutrients are made available to mushroom.
Cellulose, hemicellulose and lignin are partly decomposed and inorganic nitrogen is converted into microbial protein. Mushroom compost production is highly
complex process under aerobic conditions, involving succession of mesophillic and thermophillic microorganisms, because of which temperature inside the heap rises
upto 75-800 C. During this process lignoprotein complex is formed which favours the growth of A. bisporus. It narrows down C /N ratio due to addition of nitrogen
sources.
Compost can be prepared by two methods.
(i) Long method of composting
(ii) short method or pasteurization methods
Long method
Spread the wheat straw in a thin layer of 8-10 inches thickness over floor of the composting yard. Sprinkle water over the straw. Wetting of straw is done repeatedly at
least 2-3 times a day for 2 days. Now, 14-16 hours before mixing the ingredient in the straw all the ingredients i.e. Urea, CAN, wheat bran, etc. (except insecticides and
gypsum) are thoroughly mixed and wetted with water then covered with damp gunny bag. Next morning all these ingredients (except gypsum and insecticide) are
thoroughly mixed in the prewetted straw. Thoroughly mixed straw is heaped into a pile with the help of stack mould of the size of 1.25 m width.x1-1.25 m height x
adjustable length depending upon the quantity of straw. But the minimum length should be at least 1 m. When poultry manure / horse dung /molasses, etc. are used the
pile size is kept 5 feet x 5 feet x adjustable length. The size of the straw also depends upon the climatic conditions in which composting is being done. In cool climatic
conditions pile size is bigger than the pile made during hot climate. The straw should be firmly but not compactly compressed into the mould.
The entire pile is opened and spread over composting yard on 3rd or 4th day for at least 45-60 minutes. If straw appears to be dried, spray water over it, then mix the
straw thoroughly and make the pile once again. This process is called turning and repeated every 3rd or 4th day. During 3rd turning half of the total amount of gypsum
is added. Remaining gypsum is added during 4th turning. During 5th turning insecticide is added. In each turning uniform and thorough mixing of the straw is very
essential. After insecticide mixing pile is opened and if the smell of ammonia still persists remake the pile and leave it for another 2-3 days. This way compost is
prepared by long method in 18-21 days.
Short or pasteurization
method
This is done in 2 phases: Phase I and II. Phase I is done on the composting yard while phase II inside a closed chamber called pasteurization tunnel or chamber (bulk
chamber) with the help of steam for conditioning of the compost.
Phase I
Involves pre-wetting of the straw and mixing of ingredients in the straw as in the long method. But in this case turning is given after every 48 hrs (2 days). During 3rd
turning or on 6th day total amount of gypsum is added in the compost. After 4th turning on 8th day, the compost is filled in pasteurization tunnel on 10th day. In
pasteurization tunnel temperature of 48-500C is maintained for next 2-3days. Then with the flow of steam, temperature of the tunnel is raised to 58-600C and
maintained for 6 hrs. Fresh air is then allowed to come in through ventilation. Once the temperature of tunnel comes down to 50-520C it is maintained for 3 days. Fresh
air is then inserted in the tunnel to cool down the temperature of the compost to 25-280C. By this method compost is prepared in 19-20 days.
Compost Requirement
Why composting is required-M/b>
1. It softens the straw and thus increases the bulk density. Wet weight of bulk density compost is about 550-600 kg/m3, this favours better aeration.
2. It helps in changing the compost ingredients into nutritional substrate, which are readily required for mushroom growth. Free ammonia release
polysaccharides from lignin, thus making them available to mushroom.
3. During phase I composting bacterial growth readily utilizes available nutrients of the compost, this avoids overheating and competitor growth during
phase II.
4. It builds up appropriate biomass and variety of microbial products. Some of them serve as nutrition for mushroom growth.
5. It favours the growth of button mushroom over other microorganism.
6. It modifies compost structure, which increases its water holding capacity.
7. It converts nitrogen into stable organic form making it available to mushroom. As long as pH of compost is less than 7, ammonium ions are present instead
of free ammonia. Free ammonia is toxic to A. bisporus while ammonium ions are non-toxic.
Quality of Compost
Quality of good compost
1. Use less than one-year-old straw, which is not exposed to weathering. Chopped size of 8-10 cm is ideal. Smaller straw causes compactness resulting reduction in air
space and water logging followed by contamination in compost.
2. Fully prepared compost dark brown in colour, have no trace of ammonia, no unpleasant odour but smells like fresh hay.
3. The pH of the straw should be neutral or nearly neutral (between 7-7.5 pH is ideal). In any case it should not be more than 8, which is toxic to mushroom mycelium
growth.
65

65. ECONOMIC ATTRIBUTE SECTION
66

66. 67

67. 68

68. 69

69. 70

70. SOME BASIC CONCEPTS IN KNOWLEDGE
REPRESENTATION
 deals with the formal modeling of expert knowledge in a
computer program.
 Important questions in this respect concern the given degree of
structuralization of the domain under consideration,
 completion of the respective knowledge domain.
71

71. 4. Conducting 42 training courses on “Computer Literacy” for 374 researchers,
engineers in the ARC, and young graduates from universities and institutes during the
period from Nov. 1994 to June 2002.
Research
5- The impact on enhancing the performance of extension workers when using
the expert system was measured. A tangible enhancement was observed which
ranges from 80% to 157% in different expert systems.
6- Experiments were conducted to measure the economic and environmental
impact of using expert system in the field. The experiments showed that net
production has increased by approximately 25%. The impact on environmental
conservation was assessed using two measures: water saving and chemicals usage
reduction. It was found that fields managed by expert systems used less water by
approximately 35 % and less fertilizers by approximately 16%.
8- Establishing a Virtual Extension and Research Communication Network in
order to strengthen linkages among the research and extension components of
the national agricultural knowledge and information system.
72

72. 1. Conducting of 64 training courses on usage of Expert Systems for
465 researchers, and engineers in the ARC, extension agents,
veterinary doctors and private sector growers in the period from Dec.
1992 to March 2002.
2. Conducting 45 training courses to introduce Expert Systems for 418
researchers and engineers in the ARC, Faculties of Agriculture and
Veterinary Medicine during the period from May. 1995 to August 2001
3. Conducting 23 training courses on “Developing Expert Systems” for
175 assistant researches and engineers in the Lab during the period
from Oct. 1994 to Nov. 2001.
73

73. ATICs Year of
establis
hment
Period of
service
Personal
visit
Through
letters
Telephone
help line
Farmers field
visit
Seminars/
Trainings
Total
KAUThrissur 1993 1999-2003 985 531 2608 415 402 4941
ANGRAU,
Hyderabad
1999 1999-2005 2556 231 811 3 - 3601
BSKKV,
Dapoli
1999 1999-2005 - - - - - -
RAU,
Bikaner
2000 2000-2005 19300 - - - 100 9400
SEKAUST,
Srinagar
2000 2000-2005 376 - 438 35 204 1034
CIFT,
Cochin
2000 2000-2005 - - - - - -
MPKV,
Rahuri
2001 2001-2005 4675 626 3472 153 849 9774
CIFA,
Chennai
2002 2002-2005 - - - - - -
UAS,Dharwad 1996 1996 till
date
78,200 512 6420 562 321 86015
Table 1:Dissemination of farm technologies by ATICs (Mode of service and no. of farmers benefitted)
Ahire et.al.,2008
74

74. 1. Gujarat
2. Rajasthan
3. Madhya Pradesh
4. Punjab
5.Haryana
6. Uttar Pradesh
7. Delhi
75

75. Research:
1- A methodology for building expert systems has been developed.
2- Software tools to assist engineers in building knowledge bases, automatic translation of these knowledge bases from
English to Arabic, and acquiring knowledge from experts, have been developed
3- Twelve expert systems have been developed for field and horticulture crops: wheat, rice, faba beans, cucumber,
tomato, citrus, beans, grapes, strawberry, mango, melon and artichoke.
4- Two expert systems have been developed for animal health: cows and buffaloes, and sheep and goats.
5- The impact on enhancing the performance of extension workers when using the expert system was measured. A
tangible enhancement was observed which ranges from 80% to 157% in different expert systems.
6- Experiments were conducted to measure the economic and environmental impact of using expert system in the field.
The experiments showed that net production has increased by approximately 25%. The impact on environmental
conservation was assessed using two measures: water saving and chemicals usage reduction. It was found that fields
managed by expert systems used less water by approximately 35 % and less fertilizers by approximately 16%.
7- Three expert systems have been updated for wheat, citrus, and cucumber.
8- Establishing a Virtual Extension and Research Communication Network in order to strengthen linkages among the
research and extension components of the national agricultural knowledge and information system.
Training:
1. Conducting of 64 training courses on usage of Expert Systems for 465 researchers, and engineers in the ARC, extension
agents, veterinary doctors and private sector growers in the period from Dec. 1992 to March 2002.
2. Conducting 45 training courses to introduce Expert Systems for 418 researchers and engineers in the ARC, Faculties of
Agriculture and Veterinary Medicine during the period from May. 1995 to August 2001
3. Conducting 23 training courses on “Developing Expert Systems” for 175 assistant researches and engineers in the Lab
during the period from Oct. 1994 to Nov. 2001.
4. Conducting 42 training courses on “Computer Literacy” for 374 researchers, engineers in the ARC, and young
graduates from universities and institutes during the period from Nov. 1994 to June 2002.
76

76. EXTENSION SERVICE –
- Extension workers
- Extension Teaching Methods
77

77. Identify source of domain-specific expertise (expert)
Determine key concepts and structure of experts knowledge
Choose/design AI system structure (e.g. Rule-based,frames,blackboard,etc)
Attempt to structure of expert’s reasoning strategies (decision,heuristics,relative importance)
Choose or design AI system inference strategy (e.g. forward backward chaining)
Consult expert and develop structured AI database
Consult expert and develop automated inference mechanisms
Implementation system
Test system with sample cases and compare with expert’s response
Acceptable performance
Requires AI system modification78
Done
Fig: EXAMPLE OF A METHODOLOGY FOR EXPERT
SYSTEM DEVELOPMENT
No
Yes
78

78. FLOW OF INFORMATION IN EXPERT SYSTEM
EXPERT
KNOWLEDGE ENGINEERS
END USERS
(farmer, extension worker)
79

79. vvLife Cycle for Developing Expert
Systems
• Problem Definition
• Knowledge Acquisition
• Knowledge Representation
• Prototype system
• Operational system
• Knowledge base maintenance
Knowledge Acquisition
• " the transfer and transformation of potential
problem-solving expertise from some knowledge
source to a program.”
- Buchanan 1983. • machine learning - building capabilities into the
system that allow it to learn from what it is doing.
– the problem of induction - how many instances must be
observed before it can be added to the knowledge base
as "true“ knowledge elicitation - extract the
knowledge from the human expert, through
some means
– direct - interaction with the human expert
interviews, protocol analysis, direct
observation, etc.– indirect - utilize statistical techniques to analyze
of data and draw conclusions about the 80

80. vvv
81

81. Benefits to farmers
•Maximization of benefit
•Efficient use of available resources and infrastructure
•Awareness of cost benefit ratio before actual adoption
•Appropriate Decision making
•Encouraging for diversification
•Encouraging for quality production
Benefits to Private Agencies
*Creating scope for developing infrastructure
* Generating Rural Employment
82

82. METHODOLOGY FOR EXPERT SYSTEM DEVELOPMENT
Identify source of domain-specific expertise (expert)
Determine key concepts and structure of experts knowledge
Choose/design AI system structure (e.g. Rule-based,frames,blackboard,etc)
Attempt to structure of expert’s reasoning strategies (decision,heuristics,relative
importance)
Choose or design AI system inference strategy (e.g. forward backward chaining)
Consult expert and develop structured AI database
Consult expert and develop automated inference mechanisms
Implementation system
Test system with sample cases and compare with expert’s response
Acceptable performance
Requires AI system modification
 Done 83

83. INTRODUCTION OF EXTENSION
• 1866 Great Famine of Bengal & Orissa
• 1861-1941 Rabindranath Tagore-Self help and Mutual help
• 1869-1948 Mahatma Gandhi-Improvement in their inner
man
• 1880 Famine Commission
• 1901 Famine Commission
• 1928 Royal Commission
84

84. Medical
Airways
Railways
Communication
Industries
Financial
Institutions
Agriculture &
Farmer ?
85

85. Fig: COMPONENTS OF EXPERT SYSTEM
Knowledge
acquisition
Knowledge representation
Structured / Intuitive
User interface for
query, explanation,etc.
Inference/control
mechanism (e.g. forward
chaining.
knowledge base
86