Geleneksel Tıbba Teknolojik Bir Bakış: Bitki Türlerinin Makine Öğrenimi ile Sınıflandırılması

Abstract

Objective: The aim of this study is to determine the morphological characteristics of any plant; that is, to classify it with the method of image processing and machine learning by defining it with features such as leaf shape, color or odor. Method: In this study, plant images obtained from an open access database called kaggle were used as a source for machine learning. After the image learning process, the leaf images of the plants were classified by the Convolutional Neural Network (CNN) method. To verify that the system was working, 100 images of leaves and flowers were taken for each of two different plants, and the number of statistical data was increased to 700 with the ImageData Generator algorithm. Results: It was concluded that the system identified plants with 97% accuracy. The performance of the machine learning algorithm can also be understood from the confusion matrix. In the method followed in this study, diagonal elements 98 and 79 of the confusion matrix were obtained. This indicates that the method we applied is statistically significant. Conclusion: Thanks to the algorithm used in this study, the identification of plants used in traditional and complementary medicine could be made with an accuracy of 97%. With this algorithm, plants containing harmful chemicals can be identified to the user and their use can be prevented. Transferring the algorithm from the computer system to mobile applications by covering more plant varieties will be a guide for future studies.