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

Subject Areas : Sustainable production technologies

Abdolreza Zarandi Baghini ¹ , Hojat Babaei ² , Ramin Tabatabaei Mirhosseini ^{3 *}

1 - , Department of Mathematics Islamic Azad University, Kerman Branch, Kerman, Iran
2 - Department of Mathematics, Kerman Branch, Islamic Azad University, Kerman, Iran
3 - Department of Civil Engineering Islamic Azad University, Kerman Branch, Kerman, Iran

Received: 2024-06-09 Accepted : 2024-06-20 Published : 2024-06-20

Keywords: Fuzzy, Pythagorean fuzzy, Classification, Granule,

Abstract :

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.

References:

Arun, C. H., Emmanuel, W. R. S., & Durairaj, D. C. (2013). Texture feature extraction for identification of medicinal plants and comparison of different classifiers. International Journal of Computer Applications, 62(12), 1–9.
Beghin, T., Cope, J. S., Remagnino, P., & Barman, S. (2010). Shape and texture based plant leaf classification. Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part II 12, 345–353.
De Angelis, L., & Dias, J. G. (2014). Mining categorical sequences from data using a hybrid clustering method. European Journal of Operational Research, 234(3), 720–730.
Ding, S., Du, M., & Zhu, H. (2015). Survey on granularity clustering. Cognitive Neurodynamics, 9(6), 561–572.
Kadir, A., Nugroho, L. E., Susanto, A., & Santosa, P. I. (2013). Leaf classification using shape, color, and texture features. ArXiv Preprint ArXiv:1401.4447.
Pedrycz, W. (2018). Granular computing: analysis and design of intelligent systems. CRC press.
Rashad, M. Z., El-Desouky, B. S., & Khawasik, M. S. (2011). Plants images classification based on textural features using combined classifier. AIRCC’s International Journal of Computer Science and Information Technology, 93–100.
Sumathi, C. S., & Kumar, A. V. S. (2012). Edge and texture fusion for plant leaf classification. International Journal of Computer Science and Telecommunications, 3(6), 6–9.
Yager, R. R. (2013). Pythagorean fuzzy subsets. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 57–61.
Yao, Y. Y. Y., Liau, C.-J., & Zhong, N. (2003). Granular computing based on rough sets, quotient space theory, and belief functions. International Symposium on Methodologies for Intelligent Systems, 152–159.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zhang, X., & Xu, Z. (2014). The TODIM analysis approach based on novel measured functions under hesitant fuzzy environment. Knowledge-Based Systems, 61, 48–58.