Precision Agriculture (PA) comprises a set of tools to understand and manage inherent spatial variability within crop fields. PA relies on a variety of techniques to collect, analyze, process, and synthesize voluminous georeferenced data. However, prior to large-scale practice, PA requires a successful experimentation stage, which is the present stage of PA for the sugarcane system. This paper presents a data analysis workflow for PA experiments, including workflow application to a case study in a sugarcane area where an appreciable diversity of soil and plant attributes has been measured. Our data analysis workflow has basis on: i) removal of outliers; ii) representation of different data acquisition techniques on a common spatial grid; iii) estimation of typical "noise" level in each measured attribute; iv) spatial autocorrelation analysis for each attribute; v) correlation analysis to identify related attributes; and vi) principal component analysis to reduce the dimensionality of the attribute space. By treating the diversity of measured attributes on a common ground, the proposed analysis workflow guides further experimentation as well as selection of data acquisition technologies suitable for large-scale sugarcane PA.
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