An intelligent system based on kernel methods for crop yield prediction

• Main Authors: Majid Awan, A., Md. Sap, Mohd. Noor
• Format: Conference or Workshop Item
• Language: English
• Published: 2006
• Subjects: QA75 Electronic computers. Computer science
• 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

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.