Performance Evaluation of Fuzzy Logic System for Dendrobium Identification Based on Leaf Morphology

Abstract

Dendrobium is the second-largest family of flowering plants in the world. There are several classes of Dendrobium, which usually identify by its, including leaves and flowers. Due to the similarity of its characteristics, identifying orchid types is complicated and usually can only be done by an expert. Moreover, those characteristics are typically non-deterministic; examining the orchid species is very challenging. This research aims to develop a novel fuzzy-based system to identify the species of orchid based on unprecise existing leaf characteristics. We used the main characteristics of Dendrobium leaves, including shape, length, width, and tips of the leaves. Based on the information from the expert, we develop the membership for each class of Dendrobium. By adopting this knowledge, we develop the system by using compatible programming with this case, and Borland Delphi as complex application development. The experiment is done by using 200 real datasets from the Liwa Botanical Gardens, West Lampung Regency, Lampung Province, Indonesia. The results are compared with those given by a Dendrobium expert. A confusion matrix is a valuable evaluation tool for measuring the performance of classification models. From the above results, we can determine the confusion matrix and calculate the TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative). The confusion matrix given from the experiments is shown in Table 6. This indicates that the system can provide the same results as experts recommended. It is shown that the system can identify orchid types with an accuracy value of 94,6 %.  Thus, this system will be beneficial for automatically determining the orchid genus.