Application of Granular Calculations Based on Pythagorean Fuzzy in the Classification of Flowering Plants

In the science of botany, the classification of plants is very important. Classification is also an effective way of organizing data, helps us to understand plants better, and get more information about them. By classifying plants, we can identify more patterns and relationships between species. This information helps us in choosing the best methods of growing and maintaining plants. Knowledge of various properties and characteristics of plants is essential for classification. The multiplicity of effective features in classification makes it more accurate. But the increase of parameters challenges the decision maker and the role of uncertainty in decision-making becomes prominent. To manage uncertainty, a flexible structure is needed. In this article, a flexible structure for classification of flowering plants is presented using granular calculations based on Pythagorean fuzzy. Reviewing and comparing the classification of iris flowers based on the presented model with intuitive classification shows that the proposed model has an acceptable accuracy in the clustering of flowering plants.