Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T22:10:58.006Z Has data issue: false hasContentIssue false
  • English
  • Français

HIDES: A Computer-Based Herbicide Injury Diagnostic Expert System

Published online by Cambridge University Press:  20 January 2017

Jingkai Zhou ,
Calvin G. Messersmith  and
Janet D. Harrington
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
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

Case-based reasoningknowledge-baserule-based reasoningHIDES, herbicide injury diagnostic expert system
Type
Education/Extension
Information
Weed Technology , Volume 19 , Issue 2 , June 2005 , pp. 486 - 491
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Bennett, A. C., Price, A. J., Sturgill, M. C., Buol, G. S., and Wilkerson, G. G. 2003. HADSS, pocket HERB, and WebHADSS: decision aids for field crops. Weed Technol. 17:412–420.CrossRefGoogle Scholar
Branting, L. K. 1999. Reasoning with Rules and Precedents. Dordrecht, Holland: Kluwer. Pp. 8–28.Google Scholar
Cercone, N., An, A., and Chan, C. 1999. Rule-induction and case-based reasoning: hybrid architectures appear advantageous. IEEE Trans. Knowledge Data Eng. 11:164–174.CrossRefGoogle Scholar
Domingos, P. 1996. Unifying instance-based and rule-based induction. Machine Learning 24:144–168.CrossRefGoogle Scholar
Gunsolus, J. L. and Curran, W. S. 1999. Herbicide Mode of Action and Injury Symptoms. St. Paul, MN: University of Minnesota North Central Regional Extension Publ. 377. 17 p.Google Scholar
Jackson, P. 1998. Introduction to Expert Systems. Boston, MA: Addison-Wesley. Pp. 3–58.Google Scholar
Kolodner, J. 1993. Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann. Pp. 5–95.Google Scholar
Leake, D. B. 1995. Combining Rules and Cases to Learn Case Adaptation. in Moore, J. D. and Lehman, J. F., eds. Proc. 17th Annual Conf. Cognitive Science Society. Hillsdale, NJ: Lawrence Zilbaum. Pp. 434–442.Google Scholar
Linker, H. M., York, A. C., and Wilhite, D. R. Jr. 1990. WEEDS—a system for developing a computer-based herbicide recommendation program. Weed Technol. 4:380–385.CrossRefGoogle Scholar
Marling, C. R., Petot, G. J., and Sterling, L. S. 1999. Integrating case-based and rule-based reasoning to meet multiple design constraints. Computational Intelligence 15:308–332.CrossRefGoogle Scholar
Mihram, G. A. 1974. Simulation: Statistical Foundations and Methodology. New York: Academic. Pp. 209–260.Google Scholar
Mitchell, T. M. 1997. Machine Learning. New York: McGraw-Hill. Pp. 140–170.Google Scholar
Monaco, T. J., Weller, S. C., and Ashton, F. M. 2002. Diagnosis of herbicide injury. in Weed Science Principles and Practices. 4th ed. New York: J. Wiley. Pp. 573–591.Google Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5:445–452.CrossRefGoogle Scholar
Neeser, C., Dille, J. A., Krishman, G., Mortensen, D. A., Rawlinson, J. T., Martin, A. R., and Bills, L. B. 2004. WeedSOFT: a weed management decision support system. Weed Sci. 52:115–122.CrossRefGoogle Scholar
Prostko, E. P. and Baughman, T. A. 1999. Peanut Herbicide Injury Symptomology Guide. College Station, TX: Texas A&M University. Texas Agricultural Extension Service SCS-1999-05. Pp. 3–8.Google Scholar
Renner, K. A. and Black, J. R. 1991. SOYHERB-a computer program for soybean herbicide decision making. Agron. J. 83:921–925.CrossRefGoogle Scholar
Stefik, M. 1995. Introduction to Knowledge Systems. San Francisco, CA: Morgan Kaufmann. Pp. 25–38.Google Scholar
Stigliani, L. and Resina, C. 1993. SELOMA: expert system for weed management in herbicide-intensive crops. Weed Technol. 7:550–559.CrossRefGoogle Scholar
Watson, I. 1997. Applying Case Based Reasoning: Techniques for Enterprise Systems. San Francisco, CA: Morgan Kaufmann. Pp. 3–32.Google Scholar
[WSSA] Weed Science Society of America. 2002. Herbicide Handbook. 8th ed. Lawrence, KS: Weed Science Society of America. 493 p.Google Scholar
Zhou, J. K., Harrington, J. D., and Messersmith, C. C. 2003. WeedIT—weed information transfer. Weed Sci. Soc. Am. Abstr. 43:194.Google Scholar