Mohd. Noor Md. Sap and A. Majid Awan
A Software Framework for Predicting OilPalm Yield from Climate Data
144 - 149
2007
1
11
International Journal of Computer and Systems Engineering
https://publications.waset.org/pdf/2779
https://publications.waset.org/vol/11
World Academy of Science, Engineering and Technology
Intelligent systems based on machine learning
techniques, such as classification, clustering, are gaining wide spread
popularity in real world applications. This paper presents work on
developing a software system for predicting crop yield, for example
oilpalm yield, from climate and plantation data. At the core of our
system is a method for unsupervised partitioning of data for finding
spatiotemporal patterns in climate data using kernel methods which
offer strength to deal with complex data. This work gets inspiration
from the notion that a nonlinear data transformation into some high
dimensional feature space increases the possibility of linear
separability of the patterns in the transformed space. Therefore, it
simplifies exploration of the associated structure in the data. Kernel
methods implicitly perform a nonlinear mapping of the input data
into a high dimensional feature space by replacing the inner products
with an appropriate positive definite function. In this paper we
present a robust weighted kernel kmeans algorithm incorporating
spatial constraints for clustering the data. The proposed algorithm
can effectively handle noise, outliers and autocorrelation in the
spatial data, for effective and efficient data analysis by exploring
patterns and structures in the data, and thus can be used for
predicting oilpalm yield by analyzing various factors affecting the
yield.
Open Science Index 11, 2007