Identification of patterns for increasing production with decision trees in sugarcane mill data

Keywords: data mining, yield variability, regression tree, knowledge discovery

Abstract

Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments.

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Published
2019-04-16
How to Cite
Peloia, P. R., Bocca, F., & Rodrigues, L. H. (2019). Identification of patterns for increasing production with decision trees in sugarcane mill data. Scientia Agricola, 76(4), 281-289. https://doi.org/10.1590/1678-992x-2017-0239
Section
Biometry, Modeling and Statistics