The environmental factors, soil and productivity mapping of a certain culture produces a great amount of data, which may contain important information to be used in the decision making processes concerning actions in the field. The extraction of implicit and potentially useful information from databases is the main objective of the area denominated Data Mining. This area uses Artificial Intelligence techniques such as Decision Tree and Genetic Algorithms, in order to accomplish its tasks. The objective of this work was to compare results obtained through those techniques in data mining about soil physiochemical characteristics and soy productivity, obtained experimentally. The Decision Tree and Genetic Algorithm were implemented in Prolog and Borland Delphi® Professional languages, respectively. The results were presented in the form of production rules, being the goal to obtain rules to predict productivity indexes above 2t/ha, 100% reliable. The rules generated by the algorithm of the Decision Tree used the operators < or >= to relate a certain value to each of the analyzed attributes. The Genetic Algorithm, due to the easiness in handling continuous values, enabled the use of more operators, also accepting the adoption of values interval for an attribute in the rules. As for the composition of the rules, AD presented larger variety of attributes, while in AG the variability was noticed more specifically in the values, once the rules pondered around certain attributes, considered the most important. Therefore, the conclusion is that the Genetic Algorithm enabled better treatment of the data due to the diversity of operators, global search and pre-process simplification by manipulating continuous values.
Keywords: Genetic Algorithm, Decision Tree, soil physical-chemical properties