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Artificial Intelligence, Cognitive Science, and Measurement Theory Applied in Soil Classification¹

ABSTRACT

Recent advances in artificial intelligence and related fields have relevance to problems encountered in soil classification. Expert systems can be used to strengthen efforts to update and revise soil classification, to maintain large data bases such as Soils-5, and to involve field personnel in technical evaluation programs. Expert systems may also be applied to other areas of soil science, where judgement and practical experience by experts can be computerized, such as soil management recommendations on irrigation, fertilizer use, and erosion control. Cognitive science research aimed at better understanding of the fundamental aspects of mental representation and categorization may provide stimuli to research on improvements in soil classification. Understanding of the theory of measurement can lead to more efficient, less arbitrary use of quantification techniques in the description and interpretation of soils. The purpose of this paper is to draw to the attention of pedologists and other soil scientists the potential usefulness of recent advances in artificial intelligence, cognitive science, and measurement theory. Following an introduction to these fields, we provide suggestions and specific examples regarding their application to soil classification. Our hope is that this introduction will encourage soil scientists to learn more about these disciplines and consider additional applications to soil science.

¹ Contribution from the Soil Conservation Service (SCS), United States Dep. of Agric. (USDA), P.O. Box 2890, Washington, DC 20013, and Ames Research Center, National Aeronautics and Space Administration (NASA), MS 242-4, Moffett Field, CA, 94035.

² Deputy Chief, Assessment and Planning, SCS-USDA, and Senior Research Scientist, Technicolor Government Services, Inc.-NASA, respectively.

Received for publication September 23, 1985.