Abstract
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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Awan, A.M., Sap, M.N.M. (2006). An Intelligent System Based on Kernel Methods for Crop Yield Prediction. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_98
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DOI: https://doi.org/10.1007/11731139_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
Online ISBN: 978-3-540-33207-7
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