Skip to page content
USDA Forest Service
  
Treesearch

Research & Development Treesearch

 
Treesearch Home
About Treesearch
Contact Us
Research & Development
Forest Products Lab
International Institute of Tropical Forestry
Northern
Pacific Northwest
Pacific Southwest
Rocky Mountain
Southern Research Station
Help
 

Science.gov - We Participate


USA.gov  Government Made Easy


Global Forest Information Service

US Forest Service
P.O. Box 96090
Washington, D.C.
20090-6090

(202) 205-8333

You are here: Home / Search / Publication Information
Bookmark and Share

Publication Information

View PDF (218 KB bytes)

Title: Development and Evaluation of an Expert System for Diagnosing Pest Damage of Red Pine

Author: Schmoldt, Daniel L; Martin, George L.;

Date: 1989

Source: Forest Science. June, 1989: 364-387.

Publication Series: Miscellaneous Publication

Description: An expert system for diagnosing pest damage of red pine stands in Wisconsin, PREDICT, runs on IBM or compatible microcomputers and is designed to be useful for field foresters with no advanced training in forest pathology or entomology. PREDICT recognizes 28 damaging agents including species of mammals, insects, and pathogens, as well as two types of abiotic damage. Two separate development tools (EXSYS and INSIGHT2+) were used. Each employs a rule-based method for representing knowledge, which was obtained from the literature and from human experts in the fields of forest pathology and entomology. The pest-inference rule blocks, for each damaging agent, are based on the abduction model of diagnosis and include rules for eliminating a pest from further consideration, diagnosing a pest as certain, and compiling evidence in favor of a pest. Both development tools employ a backward-chaining control strategy; however, it was necessary to modify this approach by designing a special block of rules to approximate the mixed strategy used by the human experts. A logic and completeness rule block was also constructed to deduce facts omitted by the user and to minimize the need for questioning. Input to PREDICT is obtained from pest damage reports containing specific information about stand/site conditions, tree symptoms, and signs. Diagnoses from PREDICT take the form of a list of one or more possible agents with corresponding confidence values. Actual and hypothetical test cases were used to refine the knowledge base, then a separate set of 20 actual cases was used as a basis for testing and evaluating the completed system. It was necessary to develop special procedures for refining and evaluating the system to accommodate the often vague and uncertain nature of pest damage information. Two versions of PREDICT (developed with the EXSYS and INSIGHT2+ tools, respectively) were evaluated and compared with three recognized experts and two field foresters. No significant differences were found between the performances of PREDICT and the experts; however, PREDICT performed significantly better than the two foresters, even though they both have training and experience in forest pest diagnosis. It was concluded that PREDICT is able to improve the diagnoses of field foresters to a level comparable with recognized experts.

Publication Notes:

  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
  • You may send email to pubrequest@fs.fed.us to request a hard copy of this publication. (Please specify exactly which publication you are requesting and your mailing address.)

XML: View XML

Citation:


Schmoldt, Daniel L; Martin, George L. 1989. Development and Evaluation of an Expert System for Diagnosing Pest Damage of Red Pine. Forest Science. June, 1989: 364-387.

 


 [ Get Acrobat ]  Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

USDA logo which links to the department's national site. Forest Service logo which links to the agency's national site.