| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560601 |
| ISSN: | 1405-5546 |
| Autores: | Abdulazeez, Adnan Mohsin1 Zeebaree, Diyar Qader2 Zebari, Dilovan Asaad2 Hameed, Thamer Hassan3 |
| Instituciones: | 1Presidency of Duhok Polytechnic University, Duhok. Iraq 2Research Center of Duhok Polytechnic University, Iraq 3University of Duhok, College of Agricultural Engineering Sciences, Iraq |
| Año: | 2021 |
| Periodo: | Jul-Sep |
| Volumen: | 25 |
| Número: | 3 |
| Paginación: | 617-631 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | The importance of the plant for the human being and the environment led to deeply been studied and classified in detail. The advancement of the technology is the main factor in finding many ways for plant identification process. Some kind of initial intelligence systems in order to identify plant, followed by many theories and concepts using methods like; Moment Invariant (MI), Zernike Moments (ZM) and Polar Fourier Transform (PFT), and technologies for classification like; Neural Network (NN), K-Nearest Neighbor Classifier (KNN) and Support Vector Machine (SVM), were used by many researchers through past years. In this paper is Centroid-Radii (C-R) combined with geometric features of the leaves, in order to cover most of shape feature of the leaves, color moments and Grey-Level Co-occurrence Matrix (GLCM) to improve the accuracy of the system identification. in addition to the above features, Veins also involved in the method been used plus Principal Component Analysis (PCA), which is used to convert features into orthogonal features and the results were inputted to the classifiers that used Probabilistic Neural Network (PNN). Two datasets have been used for test, first dataset is created especially for this work and collected from 24 kinds of plants and second dataset is called Flavia, which contains 32 kinds. The results were clearly improved to identify the plants. The maximum accuracy reached up to 98.50%, when using the first data set and 98.16% for the second dataset. |
| Keyword: | Centroid-Radii, Geometric feature extraction, Principal component analysis, Probabilistic neural network |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
