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HIDES: A Computer-Based Herbicide Injury Diagnostic Expert System

Published online by Cambridge University Press:  20 January 2017

Jingkai Zhou*
Affiliation:
Department of Plant Sciences, North Dakota State University, Fargo, ND 58105
Calvin G. Messersmith
Affiliation:
Department of Plant Sciences, North Dakota State University, Fargo, ND 58105
Janet D. Harrington
Affiliation:
Department of Plant Sciences, North Dakota State University, Fargo, ND 58105
*
Corresponding author's E-mail: jing.zhou@ndsu.nodak.edu

Abstract

Diagnosis of herbicide injury can be complex because of the large number and interaction of factors leading to herbicide injury. Computer-based expert systems have great potential to assist users, particularly nonexperts, in accurate diagnosis of herbicide injury. Rule-based and case-based reasoning are the most widely used forms of expert systems, and each system has strengths and limitations. Approaches that integrate rule-based and case-based reasoning may augment the positive aspects of the two reasoning methods and simultaneously minimize their negative aspects. The Herbicide Injury Diagnostic Expert System (HIDES) integrates rule-based and case-based reasoning and uses field-specific information, injury symptoms, herbicide use history, and herbicide information to diagnose crop injury from herbicides. The HIDES program uses a set of rules to identify suspect herbicide(s) that is the candidate for causing the observed injury and possible sources of the suspect herbicide(s). Case-based reasoning is used to propose a probable cause of injury by making an analogy to previously solved cases. A four-step procedure is followed when using HIDES: information collection, suspect herbicide identification, suspect herbicide source determination, injury reason suggestion, and knowledge accumulation.

Type
Education/Extension
Copyright
Copyright © Weed Science Society of America 

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