Delineation of site-specific productivity zones using soil properties and topographic attributes with a fuzzy logic system

A delineation procedure for site-specific productivity zones was developed with a fuzzy logic system using soil properties obtained from on-the-go electrical conductivity (EC) and organic matter (OM) sensors and topographic attributes. EC, OM, slope and curvature were used as input variables, and pr...

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Bibliographic Details
Published in:Biosystems engineering Vol. 112; no. 4; pp. 261 - 277
Main Author: Kweon, Giyoung
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.08.2012
Elsevier
Subjects:
Agricultural machinery and engineering
Agronomy. Soil science and plant productions
algorithms
Biological and medical sciences
Coefficients
consultants
Curvature
Delineation
electrical conductivity
Fundamental and applied biological sciences. Psychology
Fuzzy
Fuzzy logic
Fuzzy set theory
Generalities. Biometrics, experimentation. Remote sensing
grain yield
Kansas
Mathematical analysis
organic matter
Productivity
soil properties
temperature
yield mapping
Kansas
Productivity
Bioengineering
Fuzzy system
Attribute
Zone
Delineation
Property of soil
Fuzzy logic
Site specificity
Engineering
Characteristics
Topography
Agriculture
ISSN:1537-5110, 1537-5129
Online Access:Get full text
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Summary:A delineation procedure for site-specific productivity zones was developed with a fuzzy logic system using soil properties obtained from on-the-go electrical conductivity (EC) and organic matter (OM) sensors and topographic attributes. EC, OM, slope and curvature were used as input variables, and productivity was set as an output variable. The fuzzy rules were developed with grower's knowledge for typical central Kansas upland fields; areas within the field having high OM, low EC and low slope have the highest productivity potential, and areas within the field with low OM, high EC and high slope have the lowest productivity potential. The fuzzy logic system performed properly and generated productivity as designed by the fuzzy logic and inference scheme. To validate the system, an adjacent field with 5 years of wheat yield data was selected. The spatial agreement between productivity and yield showed as high as 0.57 and 0.35 for overall accuracy and kappa coefficient. The level of agreement is promising, considering there were many other yield-limiting factors such as precipitation, temperature and management effects. From comparison of the productivity map with the map generated by a fuzzy c-means clustering algorithm (FCM map), agreement between the productivity and yield exhibited generally higher in overall accuracy and Kappa coefficient than the agreement between FCM map and yield. Results of this study can benefit producers and consultants who utilise site-specific management by delineating productivity zones using EC, OM, slope and curvature from the on-the-go sensors. ► Fuzzy logic with four input variables and one output variable (productivity). ► System generated productivity maps with low, medium and high productivity zones. ► Spatial agreement between productivity and yield was high. ► Validation was promising considering the many other yield-limiting factors.
Bibliography:http://dx.doi.org/10.1016/j.biosystemseng.2012.04.009
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ISSN:1537-5110
1537-5129
DOI:10.1016/j.biosystemseng.2012.04.009