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Diagnostic expert systems in US agricultural extension 1
Author(s) -
ADAMS S. S.,
STEVENSON W. R.
Publication year - 1990
Publication title -
eppo bulletin
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.327
H-Index - 36
eISSN - 1365-2338
pISSN - 0250-8052
DOI - 10.1111/j.1365-2338.1990.tb01212.x
Subject(s) - expert system , knowledge base , identification (biology) , agriculture , computer science , microbiology and biotechnology , agroforestry , agricultural engineering , engineering , artificial intelligence , biology , ecology
Acurate identification of plant pest problems requires expertise in the agronomic or horticulture aspects of crop production and a sophisticated level of knowledge of the many problems (diseases, insects, toxicities, and deficiencies) that may affect crops. Diagnostic expertise developed over many years is lost almost overnight through changes in career or program direction or retirement. Expert systems which simulate the behavior of diagnostic consultants in interaction with users, offer the chance to capture this expertise before it is lost. Each system is composed of a knowledge base and an expert system shell. The knowledge base contains the collective diagnostic expertise (often encoded in the form of IF‐THEN rules) of interviewed specialists in plant pathology, entomology, and agronomy. The shell performs the reasoning and problem‐solving tasks (including searching the knowledge base for applicable rules) while directing questions and issuing diagnostic reports to the user. There are currently a large number of expert systems being developed, evaluated, and placed into use in the USA. These include systems for diagnosing disease and insect problems of apple, cotton, muskmelon, peach, peanut, potato, red pine, soybean, tomato, and turfgrass. Weed identification systems are also in progress. It will be necessary, if not crucial, for extension professionals to be actively involved in the ongoing development, evaluation, and implementation of such systems.