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
格式: Conference or Workshop Item
语言:English
出版: 2006
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在线阅读: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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总结: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.