Textural analysis of soil images to quantify and characterize the spatial variation of soil properties using a real-time soil sensor

Textural analysis of soil images to quantify and characterize the spatial variation of soil... The primary aim of this work was to predict soil moisture content and soil organic matter using soil image texture statistics. Co-occurrence method texture statistics were used to characterize Andisol soils to extend the possibility of using RGB color space in representing composite soil color. Four co-occurrence method textural features; angular second moment (ASM), contrast (CON), correlation (COR) and inverse difference moment (IDM) calculated from generalized matrix for image texture representation were used to describe soil moisture content variation under laboratory conditions. It was found that CON and COR had negative responses to moisture content (MC) and ASM had positive response to MC. The same were also observed in direct captured field soil images in terms of textural indices against MC and soil organic matter (SOM). The correlations were significant for ASM and COR in fertilizer and combined (fertilizer-manure) plots and insignificant in manure plots. To relate sub-surface image textural indices and soil properties for individual years, stepwise multiple linear regression (SMLR) and supervised feed-forward neural networks (NN) were investigated in an attempt to provide minimal prediction errors. The improvements achieved by NN with minimal prediction errors were better than SMLR in different years. It was assumed that several years of data sets with a much larger number of observations could be used to differentiate fundamental soil properties. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Textural analysis of soil images to quantify and characterize the spatial variation of soil properties using a real-time soil sensor

Loading next page...
 
/lp/springer_journal/textural-analysis-of-soil-images-to-quantify-and-characterize-the-vLt0NaOhkr
Publisher
Kluwer Academic Publishers-Plenum Publishers
Copyright
Copyright © 2006 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-006-9018-5
Publisher site
See Article on Publisher Site

Abstract

The primary aim of this work was to predict soil moisture content and soil organic matter using soil image texture statistics. Co-occurrence method texture statistics were used to characterize Andisol soils to extend the possibility of using RGB color space in representing composite soil color. Four co-occurrence method textural features; angular second moment (ASM), contrast (CON), correlation (COR) and inverse difference moment (IDM) calculated from generalized matrix for image texture representation were used to describe soil moisture content variation under laboratory conditions. It was found that CON and COR had negative responses to moisture content (MC) and ASM had positive response to MC. The same were also observed in direct captured field soil images in terms of textural indices against MC and soil organic matter (SOM). The correlations were significant for ASM and COR in fertilizer and combined (fertilizer-manure) plots and insignificant in manure plots. To relate sub-surface image textural indices and soil properties for individual years, stepwise multiple linear regression (SMLR) and supervised feed-forward neural networks (NN) were investigated in an attempt to provide minimal prediction errors. The improvements achieved by NN with minimal prediction errors were better than SMLR in different years. It was assumed that several years of data sets with a much larger number of observations could be used to differentiate fundamental soil properties.

Journal

Precision AgricultureSpringer Journals

Published: Oct 20, 2006

References

  • On-the-go soil sensors for precision agriculture
    Adamchuk, VI; Hummel, JW; Morgan, MT; Upadhyaya, SK

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off