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An intelligent system based on kernel methods for crop yield prediction

This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal wit...

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Main Authors: Majid Awan, A., Md. Sap, Mohd. Noor
Format: Conference or Workshop Item
Language:English
Published: 2006
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/7573/1/Sap_M_N_Md_2006_Intelligent_System_Based_Kernel_Methods.pdf
http://eprints.utm.my/id/eprint/7573/
http://dx.doi.org/10.1007/11731139_98
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id my.utm.7573
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spelling my.utm.75732017-08-24T04:24:16Z http://eprints.utm.my/id/eprint/7573/ An intelligent system based on kernel methods for crop yield prediction Majid Awan, A. Md. Sap, Mohd. Noor QA75 Electronic computers. Computer science This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. For this purpose, a robust weighted kernel k-means algorithm incorporating spatial constraints is presented. The algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield. 2006-03-10 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/7573/1/Sap_M_N_Md_2006_Intelligent_System_Based_Kernel_Methods.pdf Majid Awan, A. and Md. Sap, Mohd. Noor (2006) An intelligent system based on kernel methods for crop yield prediction. In: Lecture Notes in Computer Science(including subseries Lecture Notes in Artificial Intelligent and Lecture Notes in Bioinformatics) . http://dx.doi.org/10.1007/11731139_98
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Majid Awan, A.
Md. Sap, Mohd. Noor
An intelligent system based on kernel methods for crop yield prediction
description This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. For this purpose, a robust weighted kernel k-means algorithm incorporating spatial constraints is presented. The algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.
format Conference or Workshop Item
author Majid Awan, A.
Md. Sap, Mohd. Noor
author_facet Majid Awan, A.
Md. Sap, Mohd. Noor
author_sort Majid Awan, A.
title An intelligent system based on kernel methods for crop yield prediction
title_short An intelligent system based on kernel methods for crop yield prediction
title_full An intelligent system based on kernel methods for crop yield prediction
title_fullStr An intelligent system based on kernel methods for crop yield prediction
title_full_unstemmed An intelligent system based on kernel methods for crop yield prediction
title_sort intelligent system based on kernel methods for crop yield prediction
publishDate 2006
url http://eprints.utm.my/id/eprint/7573/1/Sap_M_N_Md_2006_Intelligent_System_Based_Kernel_Methods.pdf
http://eprints.utm.my/id/eprint/7573/
http://dx.doi.org/10.1007/11731139_98
_version_ 1643644800059047936
score 13.211869

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