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: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2006
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| Subjects: | |
| 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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| Summary: | 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. |
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