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Study on a pattern classification method of soil quality based on simplified learning sample dataset

By:
, , ,
DOI: 10.1109/ICICTA.2011.339

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Abstract

Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

Additional Publication Details

Publication type:
Conference Paper
Publication Subtype:
Conference Paper
Title:
Study on a pattern classification method of soil quality based on simplified learning sample dataset
ISBN:
9780769543536
DOI:
10.1109/ICICTA.2011.339
Volume
2
Year Published:
2011
Language:
English
Larger Work Title:
Proceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011
First page:
194
Last page:
197
Number of Pages:
4
Conference Title:
2011 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011
Conference Location:
Shenzhen, Guangdong
Conference Date:
28 March 2011 through 29 March 2011