Expert system for effective extension service,1
- 1. bismillah Expert system for Extension Service Master seminar-1 Rehan Malik PGS16AGR7062
- 2. Flow of Presentation •Introduction •Expert system and its concept •Components of Expert System •Application of Expert System in Agriculture •Working of Expert System •Research Studies •Conclusion
- 3. Introduction
- 4. EXTENSION SERVICE - Extension workers - Extension clients
- 5. Genesis of Extension Service Individual effortsBefore 1947s Extension Workers Multilevel workers Not specialist Individual contacts Demo Small group meetings Community approach Area approach Clientele approach Demonstrators Field Supervisors Specialists (VLWs, BDOs) Dealers Friends & Relatives Individual contacts Group Mass 1947 - 1979 T&V ATMA ICT initiatives AEOs Scientists, SAUs Input Agency Dealers Friends & relatives Print media Individual contacts Group Mass / ICT 1980s to 2011…..
- 6. Source of Information Before 1947s Traditional Farming Fore farmers knowledge Small knowledge from unskilled workers Community workers Demonstrators Friends & Relatives VLWs, BDOs, Print Media 1947 - 1979 AEOs, AAOs, Scientists, SAUs Private players, Dealers Friends & Relatives Mass Media 1980 to present Green Revolution Subsistence Farming Commercial Agriculture
- 7. Limitation of existing extension service system
- 8. 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
- 9. 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
- 10. EXTENSION CLIENT What should I do?
- 11. Market 5 km Land 2 acres Labour 5 members Capital Rs. 5000 Power Pair of bullock Source of Irrigation Pump set My resources
- 13. 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?
- 14. Private Agencies & NGOs Agril. Departments Extension worker Friends & Neighbour News paper television & Magazines SAUs Who will help me in appropriate decision making? The answer to all these questions is: EXPERT SYSTEM OF EXTENSION
- 15. Concept OF Expert system Expert systems were introduced by researchers under Stanford Heuristic Programming Project. Principal contributors to the technology were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanMelle, Carli Scott, and others at Stanford. 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.
- 16. Is an intelligent computer program that uses knowledge, procedures and inferences 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 ) An expert system is simply a computer software programme that mimics the behavior of human experts. (Ahmed Rafea, 2002) EXPERT SYSTEM – MEANING
- 17. 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)
- 18. KNOWLEDGE BASE hypothesis facts processes objects attributes definition events rules Knowledge base may really include many things (Berg,2002)
- 19. 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
- 20. Knowledge acquisition for knowledge engineer Structured interviews Unstructured interviews (tape recording, video taping) Note-taking and memory Gestures Knowledge Representation •A method to represent the knowledge about the domain • Knowledge about an area of expertise is encoded
- 21. 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
- 22. USER INTERFACE Allows the end-users to run the expert system and interact with it. Allows query, advice, explanation and interaction
- 23. APPLICATION OF EXPERT SYSTEM IN AGRICULTURE • Crop production estimates • Crop selection • Soil management • Plant diseases and pests mgt • Weed management
- 24. EARLIER MODULES OF EXPERT SYSTEM IN AGRICULTURE Specification Field of application COMAX Integrated crop management in cotton SOYEX Soybean oil extraction expert system PLANT/ds Diagnosis of soybean diseases SEMAGI Weed control decision making in sunflowers RICE-CROP Diagnose pest and disease for rice crop and suggest preventive/ curative measures COTFLEX Cotton crop management coupled with SOYGRO model CVSES Wheat crop variety selection
- 25. EARLIER MODULES OF EXPERT SYSTEM IN HORTICULTURE Specification Field of application POMME Pest and insect management in apple CUPTEX Cucumber expert system CITEX Citrus expert system LIMEX A multimedia Expert system for Lime production TOMATEX Tomato expert system
- 26. Web based expert system Maize Agri Daksh Wheat expert system RICE Doctor by IRRI TNAU AgriTECH PORTAL Digital mandi for the Indian kisan mKisan Agri portal Barley expert system Rice knowledge management portal Expert system for Agriculture and Animal husbandry by DWCRA, Bhubaneshwar Developed by IASRI
- 27. Mobile based expert system : Crop insurance mobile app by Ministry of Agriculture,GOI Agrimarket mobile app mKissan app RainbowAgri app Manditrades app Mpower social app IFFICO kissan app eSAP app….etc
- 28. Web based and mobile based Expert systems Agriculture and horticulture Paddy expert system Banana expert system Sugarcane expert system Ragi expert system Coconut expert system Animal husbandry Cattle and Buffalo Sheep and Goat Poultry Source: http://www.agritech.tnau.ac.in •Developed by TNAU collaboratively with ICAR •Available in 4 languages English Kannada Tamil Malayalam
- 29. Working of a Mobile Based Expert system
- 40. 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
- 41. Research studies
- 42. An information technology enabled Poultry Expert system: Perceptions of veterinarians and veterinary students Karuppasamy and Sriram (2013) Methodology: Study Area : Nizamabad district, Hyderabad Method of Sampling : Random Sampling Sample Size: 30+30
- 43. Table 1: Response of veterinarians and students on Utility of PES Item Veterinarians( n=30) Students( n=30) Agree undecide d Disagree Agree undecid ed Disagree F % F % F % F % F % F % Very much useful 30 100. 0 0 0.0 0 0 0.00 27 90.0 0 0 0.0 0 3 10.0 0 Handy to use 26 86.6 6 2 6.6 6 2 6.66 26 86.6 7 2 6.6 6 2 6.66 Saves time and money 30 100. 0 0 0.0 0 0 0.00 23 76.6 7 4 13. 33 3 10.0 0 Advantageous over the traditional methods 30 100. 0 0 0.0 0 0 0.00 28 93.3 3 2 6.6 6 0 0.00
- 44. Table 2:Response of veterinarians and students on Complexity of PES Item Veterinarians( n=30) Students( n=30) Agree undecide d Disagree Agree undecid ed Disagree F % F % F % F % F % F % Simple to operate 26 86.6 6 2 6.6 6 2 6.66 19 63.3 3 6 20. 00 5 16.6 7 Simple language 25 83.3 3 5 16. 67 0 0.00 24 80.0 0 5 16. 67 1 3.33 Easy navigation 27 90.0 0 3 10. 00 0 0.00 18 60.0 0 10 33. 33 2 6.66 Simple to understand 29 96.6 7 1 3.3 3 0 0.00 30 100. 0 0 0.0 0 0 0.00
- 45. Table 3:Response of veterinarians and students on Compatibility of PES Item Veterinarians( n=30) Students( n=30) Agree undecide d Disagree Agree undecid ed Disagree F % F % F % F % F % F % Replacement of expert 9 30.0 0 15 50. 00 6 20.0 0 11 36.6 7 9 30. 00 10 33.3 3 Supplement to the existing practice 23 76.6 7 7 23. 33 0 0.00 22 73.3 3 7 23. 33 1 3.33 Substitution of an expert 12 40.0 0 11 36. 77 7 23.3 3 13 43.3 3 9 30. 00 8 26.6 7
- 46. Table 4:Response of veterinarians and students on Technicality of PES Item Veterinarians( n=30) Students( n=30) Agree undecide d Disagree Agree undecid ed Disagree F % F % F % F % F % F % Credibility 25 83.3 3 5 16. 67 0 0.00 18 60.0 0 11 36. 67 1 3.33 Accuracy 23 76.6 7 7 23. 33 0 0.00 18 60.0 0 6 20. 00 6 20.0 0 In line with the agreement of experts 21 70.0 0 8 26. 67 1 3.33 22 73.3 3 6 20. 00 2 6.67 No discrepancy in the message 20 66.6 7 7 23. 33 3 10.0 0 17 56.6 7 8 26. 67 5 16.6 7
- 47. Table 5:Response of veterinarians and students on Feasibility of PES Item Veterinarians( n=30) Students( n=30) Agree undecide d Disagree Agree undecid ed Disagree F % F % F % F % F % F % Suitable to existing information needs of farmers 26 86.6 7 3 10. 00 1 3.33 25 83.3 3 5 16. 67 0 0.00 Affordability / cost effective 20 66.6 7 6 20. 00 4 13.3 3 13 43.3 3 8 26. 67 9 30.0 0 New aid for transfer of technology 26 86.6 7 3 10. 00 1 3.33 28 93.3 3 1 3.3 3 1 3.33 Can be used at farmers level 18 60.0 0 8 26. 67 4 13.3 3 16 53.3 3 2 6.6 7 12 40.0 0
- 48. Table 6:Response of veterinarians and students on Design of PES Item Veterinarians( n=30) Students( n=30) Agree undecide d Disagree Agree undecid ed Disagree F % F % F % F % F % F % User friendly 24 80.0 0 5 16. 67 1 3.33 27 90.0 0 2 6.6 7 1 3.33 Aesthetic 24 80.0 0 6 20. 00 0 0.00 25 83.3 3 3 10. 00 2 6.67 User centered design 22 73.3 3 8 26. 67 0 0.00 24 80.0 0 5 16. 67 1 3.33 User centered interactiveness 25 83.3 3 5 16. 67 0 0.00 25 83.3 3 2 6.6 7 3 10.0 0
- 49. INFORMATION EFFICIENCY OF AGRICULTURAL EXPERT SYSTEM Helen and Kaleel (2009) Methodology: Study Area : Pallakkad district , Kerala Method of Sampling : Purposive Sampling Sample Size: 60
- 50. Table 1: Treatment wise Information Efficiency Index of AES as assessed by extension personnel SI NO DIMENSIONS AES alone T1 (n=30) AES+HES T2 (n=30) 1 Retrievability 61.76 68.16 2 Relevancy 79.33 80.00 3 Practicability 84.00 86.00 4 Information content 68.74 78.21 5 Knowledge gain 49.35 60.44 IEI=obtained total score X 100/Maximum possible score
- 51. Perception of Prospective Users about the Performance of Agricultural Expert System Helen and Kaleel (2009) Methodology: Study Area : Pallakkad district , Kerala Method of Sampling : Purposive Sampling Sample Size: 60
- 52. Table 1: Perception of TOT researchers about the Performance of Agricultural Expert System SI NO Performance related attributes Researchers in TOT n=40 MEAN RANK 1 Settings in the AES 9.45 I 2 Retrievability of information 6.30 IV 3 Serviceability of the system 6.18 V 4 Relevancy of information 2.86 VII 5 Practicability of information 8.23 III 6 Information content 2.81 VIII 7 Mode of presentation 2.54 IX 8 Information treatment 2.37 X 9 Provision for updating information 5.05 VI 10 Future Prospects 9.27 II
- 53. Effectiveness of Paddy Expert System in terms of Knowledge Gain, Skill Acquisition and Symbolic Adoption Behaviour among the Paddy Growers of Thoothukudi District in South Tamil Nadu Karuppasamy and Sriram (2013) Methodology: Study Area : Thoothukudi district, (Tamil Nadu) Method of Sampling : Purposive Sampling Sample Size: 105
- 54. Table 1 :Effectiveness of treatment towards knowledge gain due to exposure to PES Sl no Treatments Mean knowledge gain Mean knowledge gain Percentag e (%) Before exposure Immediately after exposure 1 Marginal & Small Farmers (T1) 5.11 9.94 4.83 13.80 2 Medium Farmers (T2) 4.71 10.23 5.52 15.77 3 Large Farmers (T3) 4.97 10.28 5.31 15.17 Total 14.79 30.45 15.66 44.74
- 55. Table 2 :Effectiveness of treatment towards knowledge related due to exposure to PES Sl no Treatments Mean Skill Acquisition Mean Skill Acquistio n Percentag e (%) Before exposure Immediatel y after exposure 1 Marginal & Small Farmers (T1) 3.40 10.17 6.77 19.34 2 Medium Farmers (T2) 3.28 9.80 6.52 18.63 3 Large Farmers (T3) 3.86 9.46 5.60 16.00 Total 10.54 29.43 18.89 53.97
- 56. Table 3 :Effectiveness of the treatment PES in terms of Symbolic Adoption behaviour Sl no Treatments No . of Respondents Percentage (%) Marginal & Small Farmers 1 Low 4 11.43 2 Medium 23 65.71 3 High 8 22.86 Total 35 100.00 Medium Farmers 1 Low 4 11.43 2 Medium 29 82.86 3 High 2 5.71 Total 35 100.00 Large Farmers 1 Low 5 14.28 2 Medium 19 54.28 3 High 11 31.44
- 57. Conclusion