Orchid types classification using supervised learning algorithm based on feature and color extraction

Author:

Andono Pulung Nurtantio,Rachmawanto Eko Hari,Herman Nanna Suryana,Kondo Kunio

Abstract

Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.

Publisher

Institute of Advanced Engineering and Science

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Instrumentation,Information Systems,Control and Systems Engineering,Computer Science (miscellaneous)

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Use of remote sensing and image processing for identification of wild orchids;Frontiers in Environmental Science;2024-04-26

2. An Improved Classification Model Based on Feature Fusion for Orchid Species;Journal of Electrical Engineering & Technology;2023-11-14

3. Efficiency in Orchid Species Classification: A Transfer Learning-Based Approach;International Journal of Computational Intelligence and Applications;2023-11-03

4. A Comprehensive Review of Flower Classification Techniques Using Deep Learning;2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS);2023-11-03

5. Deep neural networks for explainable feature extraction in orchid identification;Applied Intelligence;2023-08-21

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