Data science for pattern recognition in agricultural large time series data: A case study on sugarcane sucrose yield

Abstract

among others. Each of the phases allowed the elimination of variables that obscured the analysis process by considering parameters such as Pearson correlation, exploratory analysis, and modeling. Important variables that offered value in the analysis were obtained, considering those variables related to the soil as those of minor contribution, and climatic variables as the most informative. Our results present an alternative to traditional analyzes in the agricultural sector, based on a step-by-step protocol for the responsible use of DS in the search to understand the behavior and temporal historical patterns of sucrose in sugarcane.

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