A new algorithm on delineation of management zone

Abstract

The delineation of management zones is an economical and effective measure for the variable-rate application in precision agriculture. The methods of empirical and unsupervised classification have been used by many researchers in the delineation of management zones, but these methods are only built upon the information of attributes in every spatial cell, and the spatial relationships and their spatial interaction between cells are not considered. AS a result, there are many isolated cells or patches in the zoned map, this is not advantageous for the operation of the variable-rate application. Based on the traditional k-means cluster (K-M) and the spatial autocorrelation, a new method, spatial contiguous k-means clustering algorithm (SC-KM), was developed in this study. According to the spatial variability of wheat growth under within-field level extracted from OMIS image of the key growth stage, management zones were delineated by using K-M and SC-KM methods. Two evaluation indices were employed to evaluate the zoned results of the above mentioned two methods .The results showed that the sum of the weighted variance of the corresponding within-zones based on the two methods appeared no significant difference, and that the SC-KM method could remove lots of isolated cells or patches and improved the continuity of the corresponding management zone map, compared with the K-M method. The zoned result based on the SC-KM method can be used as the variable management unit for precision agriculture and can be used to advise the sampling of subsequent soil or crop.