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

Author(s)

Peloia, Paulo Rodrigues ; Boccae, Felipe Ferreira ; Antunes Rodrigues, Luiz Henrique

• Document type: Journal article
• Source: CIENTIA AGRICOL; v. 76, n. 4, p. 9-pg., 2019-07-01.

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 (R-2 = 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. (AU)

• FAPESP's process: 12/50049-3 - Data Mining Techniques Applied to the Analysis and Prediction of Sugarcane Yield
• Grantee: Luiz Henrique Antunes Rodrigues
• Support Opportunities: Program for Research on Bioenergy (BIOEN) - Research Partnership for Technological Innovation (PITE)