Investigating the quantitative and qualitative factors affecting the penetration resistance of Sugarcane cultivated soils using decision tree

Investigating the state of penetration resistance of sugarcane fields and identifying the affecting factors is a very critical issue. In this regard, a broad range of methods can be applied. In this study, effective models and algorithms were explored using two decision tree algorithms (CART and CHAID). After introducing the variables of soil depth, the number of traffics, soil moisture, soil texture, forward speed, and age of the plant as predictor variables of the decision tree as well as soil cone index as a dependent variable, the two algorithms were entered. The algorithms were evaluated using two statistical indicators of coefficient of determination (R2) and absolute mean error (MAE). Based on the two statistical indices, the CART tree model with R2= 0.952 and MAE= 0.504 had better performance in predicting penetration resistance of soil in sugarcane fields which presented as a proposed algorithm to Amir Kabir agro-Industry. The results of the model showed that the tree decision-making method can predict the penetration resistance of soil with high precision using effective variables. Moreover, given the simple interpretation of the decision tree and the comprehensibility of the rules for extracting it for agricultural and agro-industrial experts, it can be used at various levels.