Subfield management class delineation using cluster analysis from spatial principal components of soil variables

By Mariano Córdoba, C. Bruno, J. Costa, Mónica Balzarini. 2013

81 citations recorded by the source corpus.

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.

Subfields: Environmental Chemistry, Management, Monitoring, Policy and Law, Plant Science

View Paper: https://www.semanticscholar.org/paper/bc8fc4acae7a5d66e0aeff256642ac50afe9fe75

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1. Jaemyung Shin
2. Jian Jin
3. Md. Sultan Mahmud
4. Young K. Chang
5. Tanzeel U. Rehman
6. D. Saha
7. Rohit Sharma

Citations in the corpus

• Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, methodology): “Whilst the GMM consider the covariance between different input variables using covariance matrix, therefore it might be a good alternative to fuzzy clustering and/or MULTISPATI-PCA for generating more contagious soil management zones as indicated by Córdoba et al. (2013) (Table 7).”