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Early Plant Disease Detection Using Gray-level Co-occurrence Method with Voting Classification Techniques. |
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| รหัสดีโอไอ | |
| Creator | Alaa O. Khadidos |
| Title | Early Plant Disease Detection Using Gray-level Co-occurrence Method with Voting Classification Techniques. |
| Contributor | - |
| Publisher | TuEngr Group |
| Publication Year | 2564 |
| Journal Title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| Journal Vol. | 12 |
| Journal No. | 13 |
| Page no. | 12A13H: 1-15 |
| Keyword | Computer Vision Systems, Agriculture 4.0, Smart agriculture, Precision agriculture, Digital agriculture, Color image segmentation, Voting Classification. |
| URL Website | http://TuEngr.com/Vol12_13.html |
| Website title | ITJEMAST V12(13) 2021 @ TuEngr.com |
| ISSN | 2228-9860 |
| Abstract | An Early detection of plant disease is a primary challenge in smart agriculture. Image processing can be used for detecting the plant disease. When it comes to detecting plant disease, a variety of algorithms are built around these four stages. The performance of earlier designed algorithms is computed with regard to different parameters such as accuracy, recall, etc. In this paper, we propose a machine learning approach that will process images captured from an IoT camera-based approach that periodically send photos. The proposed approach uses a voting classifier for determining if a plan is healthy or not. The voting classifier was compared against the SVM and provided 26% better accuracy and precision and 27% better recall. |
