Sukemi, Sukemi (2019) Fruit Plant Leaf Identification Feature Extraction Using Zernike Moment Invariant (ZMI) and Methods Backpropagation. IEEE xplore, - (193393). pp. 225-229. ISSN 978-1-7281-2930-3
Preview |
Text
Proceeding-ICIMCIS.pdf Download (1MB) | Preview |
Preview |
Text
Cover-Pengelola-Daftar isi.pdf Download (602kB) | Preview |
Abstract
The concept of pattern recognition is often used to identify a wide range of objects. Due to the ability to recognize objects is needed by humans. One of them is for pattern recognition on the leaves as identification in determining the types of leaves. However, in the acquisition, very frequent disturbances called noise. Noise in the image is a region of pixel image intensity of unwanted or deemed to disturb the segmentation process until the introduction. The impact of noise can degrade the image quality when the segmentation process. Therefore, in this study, the researchers added a preprocessing stage to reduce noise modest invisible when the acquisition using the camera. Gaussian filter used as a technique to tackle the problem at last preprocessing. Aside from the noise, constraints at the time of feature extraction of natural researchers also because the study took shape characteristic based on the area of the image. So if the object changes the coordinates of the start pixel image was unrecognizable. Based on these problems do research to identify the leaves by using Zernike Moment invariant feature extraction (ZMI) and backpropagation algorithm. Based on the testing that was done on 100 test data success rate Based on these problems do research to identify the leaves by using Zernike Moment invariant feature extraction (ZMI) and Backpropagation algorithm. Based on the testing that was done on 100 test data success rate 78%.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
| Depositing User: | Dr. Sukemi Sukemi |
| Date Deposited: | 18 Jan 2022 05:59 |
| Last Modified: | 18 Jan 2022 05:59 |
| URI: | http://repository.unsri.ac.id/id/eprint/60641 |
Actions (login required)
 |
View Item |