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TITLE OF PROPOSED SOFTWARE/SYSTEM
“PulsExpert”
Diagnostic Expert System for Pulses Crop
“Pulses for Nutritional Security &...
WHAT IS AN EXPERT SYSTEM ?
 “Special computer software capable of carrying out analysis with reasoning in narrowly
define...
 Develop a knowledge-based system for pulse agricultural
activities.
 Develop a user-friendly diagnostic system and sugg...
 Unavailability of domain experts at the nick of time
 Expert’s knowledge is a scarce & expensive resource
 Static info...
Utility of expert systems in diagnosing pulse crop
diseases
 Timely diagnosis and control of crop diseases leading cost e...
Expert System Development Process
 Knowledge acquisition
 Knowledge acquisition involves the acquisition of knowledge fr...
Process of Expert System Development
9
Knowledge Sources
 Documented (books, reports, manuals, internet etc.)
 Undocumented (experts, farmers, extension work...
Knowledge Acquisition Methods
 Manual
 Interviews
 Experts’ Reports and Questionnaires
 Observation
 Interactive comp...
11
Manual Method of Knowledge Acquisition
Knowledge
base
Documented
knowledge
Experts
Coding
Knowledge
engineer
12
Interactive Computer-based Knowledge Acquisition
Knowledge
base
Knowledge
engineer
Expert Coding
Computer-aided
(intera...
Knowledge Acquisition for
Pulse Crop Disease Identification
 Develop a knowledge base about the plant damage symptoms due...
System Architecture
Knowledge types identification
Total five parameters were identified for describing all the plant damage symptoms which ma...
Knowledge Structure
 Relational database structure is used to store the knowledge in the
form of tables and relations
 T...
Knowledge Representation
Crop_ Name Disease_ Name Question Certainty_criteria Answers
P1 D1 Q1
Q2
Q4
Q6
Q7
Q9
100%
75-100%...
Knowledge Representation
Knowledge can be simply expressed in the following rule format:
P1D1 (Q1(a11 or a12) and Q2(a21) ...
Knowledge Acquisition Process
 A domain expert enters the knowledge disease-by-disease for a pulse
crop in the form of qu...
Knowledge Acquisition Process (Contd)
 Knowledge base will have the different sets of questions for the
identification of...
Home Page
Snapshot for Knowledge
Acquisition System
Snapshot for Knowledge Acquisition System
CONCLUSIONS
 A task specific knowledge acquisition tool has been developed for agriculture domain to
solve the disease id...
FUTURE WORK
 Extension of the system for other pulse agriculture areas (i.e. insect-pests
management, fertilizer manageme...
Major project 2   presentation
Major project 2   presentation
Major project 2   presentation
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Major project 2 presentation

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Agriculture Expert Advice System - Major Project 2

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Major project 2 presentation

  1. 1. TITLE OF PROPOSED SOFTWARE/SYSTEM “PulsExpert” Diagnostic Expert System for Pulses Crop “Pulses for Nutritional Security & Agricultural Diversity”
  2. 2. WHAT IS AN EXPERT SYSTEM ?  “Special computer software capable of carrying out analysis with reasoning in narrowly defined domain at proficiency levels of an expert.” (Mckinion & Lemmon, 1985)  “Computer program which apply expert knowledge to solve complex problems, mimicking the reasoning skill of a human expert.”(Linko, 1998)
  3. 3.  Develop a knowledge-based system for pulse agricultural activities.  Develop a user-friendly diagnostic system and suggest appropriate treatments.  Propose proper irrigation and fertilization schedule.  Propose most economic pulse crop based on cost benefit analysis. OBJECTIVES
  4. 4.  Unavailability of domain experts at the nick of time  Expert’s knowledge is a scarce & expensive resource  Static information  Integration of knowledge and experiences of different area experts  Representation of all types of integrated knowledge/information sources such as images and/or textual  The undocumented experience and knowledge of farmers and extension workers Difficulties in increasing yield in pulse crops
  5. 5. Utility of expert systems in diagnosing pulse crop diseases  Timely diagnosis and control of crop diseases leading cost effectiveness  New and more complex models to be used for proper identification of diseases  More accurate disease identification using updated knowledge  Environmentally and socially balanced control measures  Assists farmers and extension workers as a Decision Support System
  6. 6. Expert System Development Process  Knowledge acquisition  Knowledge acquisition involves the acquisition of knowledge from sources (I.e. human experts, books, documents, sensors, or computer files) and its transfer to the knowledge base and sometimes to the inference engine  Knowledge may be specific to the problem domain or to the problem-solving procedures, it may be general knowledge, or it may be metaknowledge  Knowledge representation  Organized knowledge  Knowledge validation and verification  Inferences  Software designed to pass statistical sample data to generalizations  User interface
  7. 7. Process of Expert System Development
  8. 8. 9 Knowledge Sources  Documented (books, reports, manuals, internet etc.)  Undocumented (experts, farmers, extension workers etc.)  Existing databases
  9. 9. Knowledge Acquisition Methods  Manual  Interviews  Experts’ Reports and Questionnaires  Observation  Interactive computer-based  Computer program that elicits knowledge from experts  Automatic knowledge base with minimal help from knowledge engineer
  10. 10. 11 Manual Method of Knowledge Acquisition Knowledge base Documented knowledge Experts Coding Knowledge engineer
  11. 11. 12 Interactive Computer-based Knowledge Acquisition Knowledge base Knowledge engineer Expert Coding Computer-aided (interactive) interviewing
  12. 12. Knowledge Acquisition for Pulse Crop Disease Identification  Develop a knowledge base about the plant damage symptoms due to major diseases in Pulses  Develop an interactive knowledge acquisition system having facilities of adding, viewing, modifying and deleting both types of knowledge (i. e. textual and pictorial)  Assist the domain expert(s) and extension workers to feed knowledge in the knowledge base in a structured form maintaining consistency of the encoded knowledge
  13. 13. System Architecture
  14. 14. Knowledge types identification Total five parameters were identified for describing all the plant damage symptoms which may occur in the crop/plant at different stages In pulse crops disease diagnosis domain  crop parameters (crop name, disease type, disease name, crop location, crop stage affected, planting space, crop sowing time, disease status in area, and disease status in the previous crop)  field parameters (temperature, humidity and soil moisture)  symptom parameters (colours, shapes and sizes changes in leaves and other plant parts)  Visible pictorial parameters (spot/holes in leaves, pods, flowers, stems, seeds etc.)  Treatment parameters (resistant varieties, cultural practices & chemical controls including fungicide name, dose & time of application etc.)
  15. 15. Knowledge Structure  Relational database structure is used to store the knowledge in the form of tables and relations  The database has eight different tables viz., Login, Pulsemaster, Disease, Pulse_question, Pulse_answer, Fig, Control_measures and Diagnosis “Categories of knowledge” Textual knowledge  Knowledge is actually a set of questions along with multiple answers  Question is based on the identified parameters  Each question having a certainty criteria (Confirmatory, 50-75% certain, 25-50% certain, 0-25% certain) for representing the importance of question in identifying the particular disease Pictorial knowledge  Knowledge contains pictures as well as full details of the required symptoms  Options (“Yes”, “No” or “Unknown”) are used for answering the particular question
  16. 16. Knowledge Representation Crop_ Name Disease_ Name Question Certainty_criteria Answers P1 D1 Q1 Q2 Q4 Q6 Q7 Q9 100% 75-100% 50-25% 50-25% 0-25% 0-25% a11, a12 a21 a42, a43, a44 a62 a72, a73 a91 P1 D2 Q1 Q2 Q3 Q5 Q6 Q8 Q9 100% 100% 75-100% 50-25% 50-25% 0-25% 0-25% a13 a22, a23 a31 a53, a54 a61, a63 a81, a83 a91, a92 P1 D3 Q1 Q2 Q3 Q7 Q8 Q9 100% 75-100% 75-100% 50-25% 50-25% 0-25% a14 a22, a24 a32 a71 a82 a91, a93
  17. 17. Knowledge Representation Knowledge can be simply expressed in the following rule format: P1D1 (Q1(a11 or a12) and Q2(a21) and Q4(a42 or a43 or a44) and Q6(a62) and Q7(a72 or a73) and Q9(a91) ) P1D2 (Q1(a13) and Q2(a22 or a23) and Q3(a31) and Q5(a53 or a54) and Q6(a61 or a63) and Q8(a81 or a83) and Q9(a91or a92) ) P1D3 (Q1(a14) and Q2(a22 or a24) and Q3(a32) and Q7(a71) and Q8(a82) and Q9(a91 or a93) )
  18. 18. Knowledge Acquisition Process  A domain expert enters the knowledge disease-by-disease for a pulse crop in the form of questions and their expected answers and also enters a certainty criteria for or a particular disease of a selected pulse crop “ how important is a question in diagnosing a particular disease?”  While entering the knowledge for other diseases of same crop, he/she may select either form the entered set of questions through question bank or enter new question  After entering the knowledge from an expert to build a structured knowledge base, all the possible answers of a question are stored together by the system
  19. 19. Knowledge Acquisition Process (Contd)  Knowledge base will have the different sets of questions for the identification of each type of diseases in pulses  Some of the questions may be common but their possible answers may or may not be  An option “Unknown” is added automatically by the system to give the user facility in case he/she does not know the answer of particular question
  20. 20. Home Page
  21. 21. Snapshot for Knowledge Acquisition System
  22. 22. Snapshot for Knowledge Acquisition System
  23. 23. CONCLUSIONS  A task specific knowledge acquisition tool has been developed for agriculture domain to solve the disease identification and control problems mainly for pulses. However, it may be used for other crops also  The system provides user-friendly interface to domain expert for entering the knowledge  System enables the experts to input, view, modify and delete the information contained in the database  It captures the knowledge of an expert through interactive sessions, distills the knowledge and automatically generates tables used in decision making  Direct involvement of the domain expert increases the accuracy of the resulting knowledge base eliminating the errors of communication and understanding  The system maintains the consistency and avoids redundancy by recognizing/showing similar types of knowledge/information  Knowledge base of the system contains knowledge about the disease identification symptoms & remedies of 19 diseases of major pulse crops (Chickpea, Pigeonpea, Mungbean & Urdbean)
  24. 24. FUTURE WORK  Extension of the system for other pulse agriculture areas (i.e. insect-pests management, fertilizer management, variety selection, irrigation management & cost benefit analysis)  Building a complete Expert System for identification and control of pulse crops diseases having a knowledge base created using the proposed acquisition tool  Facility to incorporate knowledge from multiple experts and refine it based on feedback both from the expert and from potential users (i.e. farmers and extension workers)  Finally, extension of the system for other crops (I.e. wheat, rice, maize etc.)

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