Computers and Electronics in Agriculture
Determination of management zones for a tobacco field based on soil fertility
Computers and Electronics in Agriculture
A segmentation algorithm for the delineation of agricultural management zones
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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Understanding spatial variation within a field is essential for site-specific crop management, which requires the delineation of management areas. Several soil and terrain variables are used to classify the field points into classes. Fuzzy k-means cluster analysis is a widely used tool to delineate management classes in the multivariate context. However, this clustering method does not consider the presence of spatial correlations in the data. The MULTISPATI-PCA algorithm is an extension of principal component analysis that considers spatial autocorrelation in the original variables to produce synthetic variables. We propose and illustrate the implementation of a new method (KM-sPC) for subfield management class delineation based on the joint use of MULTISPATI-PCA and fuzzy k-means cluster. To assess the performance of KM-sPC, we performed clustering of the original soil variables and of both spatial and classical principal components on three field data sets. KM-sPC algorithm improved the non-spatial clustering in the formation of within-field management classes. Mapping of KM-sPC classification shows a more contiguous zoning. KM-sPC showed the highest yield differences between delineated classes and the smallest within-class yield variance.