Delineating precision agriculture management zones based on spatial contiguous clustering algorithm

Based on the traditional K-Means cluster (KM) and the spatial autocorrelation, a new method, Spatial Contiguous K-Means clustering algorithm (SCKM), 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, precision agriculture management zones were delineated by using the SCKM method and the traditional methods such as KM, Equal Interval, Quantile and Natural Breaks method. Two evaluation indices were employed to evaluate the zoned results of the above mentioned methods .The results showed that the sum of the weighted variance of the corresponding within-zones based on these methods appeared no significant difference, and that the SCKM method could remove a lot of isolated cell or patch and improved the continuity of the corresponding management zone map, compared with the traditional methods. The zoned result based on the SCKM 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.