An Intelligent System Based on Kernel Methods for Crop Yield Prediction

  • A. Majid Awan
  • Mohd. Noor Md. Sap
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


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.


Kernel Function Kernel Method Spatial Constraint Palm Produce Cluster Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Majid Awan
    • 1
  • Mohd. Noor Md. Sap
    • 1
  1. 1.Faculty of Computer Sci. & Information SystemsUniversity Technology MalaysiaSkudai, JohorMalaysia

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