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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Vydáno v:	Biosystems engineering Ročník 112; číslo 4; s. 261 - 277
Hlavní autor:	Kweon, Giyoung
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.08.2012 Elsevier
Témata:	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
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Abstract	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.
AbstractList	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. 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. 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.
Author	Kweon, Giyoung
Author_xml	– sequence: 1 givenname: Giyoung surname: Kweon fullname: Kweon, Giyoung email: kweong@veristech.com, gkweon@gmail.com organization: Veris Technologies, Inc., 601 N. Broadway, Salina, KS 67401, USA
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Snippet	A delineation procedure for site-specific productivity zones was developed with a fuzzy logic system using soil properties obtained from on-the-go electrical...
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SubjectTerms	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
Title	Delineation of site-specific productivity zones using soil properties and topographic attributes with a fuzzy logic system
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