Use of the Decision Tree Technique to Estimate Sugarcane Productivity Under Edaphoclimatic Conditions

Abstract

A number of biometric evaluations are performed during harvest for measuring the growth and development of the sugarcane crop. From these evaluations, hundreds of data values are generated, containing certain information on the productivity of the culture in that crop and edaphoclimatic region. Accordingly, the objective of this work was to identify, using a decision tree classification technique, the biometric attribute having the greatest effect on the productivity of the plant cane in different planting configurations and edaphoclimatic conditions. To accomplish this, data were evaluated from four experiments with sugarcane, located within the São Paulo municipalities of Teodoro Sampaio, Guaíra, Iracemápolis, and Lençóis Paulista. The classification model was generated using the decision tree technique, a type of intuitive learning that creates a hypothesis based on particular instances that results in general conclusions. The decision trees applied to the data of the four sites showed that the population of plants per hectare has the highest information gain (split attribute) on the class attribute (productivity). Using the “Chi-square” method of attribute selection, the population of plants per hectare was observed to have the largest correlation with the final productivity of the culture. Therefore, the decision tree indicates that the attribute “plant population per area” should be used as the method to evaluate the productive potential of the culture during its growth cycle. It has the best correlation with the final productivity of the crop, in addition to being an attribute easy to measure in the field.