Tee, Clarence Augustine T. H. and Teoh, Yun Xin and Yee, Por Lip and Tan, Boon Chin and Lai, Khin Wee (2021) Discovering the ganoderma boninense detection methods using machine learning: A review of manual, laboratory, and remote approaches. IEEE Access, 9. pp. 105776-105787. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3098307.
Full text not available from this repository.Abstract
Ganoderma disease is a kind of infection that actuates oil palm death. Early detection of Ganoderma disease is the most recommended strategy for proper treatment and disease control plan to be taken promptly. In this paper, the detection methods for Ganoderma disease were reviewed and categorized accordingly. It was found that the combination of remote sensors and machine learning techniques could identify the disease up to four severity levels, including the early stage of infection. It also significantly reduced the labor and time costs compared to the traditional visual inspection and lab-based approaches. In terms of machine learning, support vector machine (SVM) using the idea of finding a hyperplane was suggested as the best classifier in several studies. Despite only one research was done on ANN and no research evaluating CNN and GAN in Ganoderma disease detection; ANN, CNN and GAN were recognized as the potential machine learning techniques that could enhance the detection system.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Basal stem rot; Ganoderma; Machine learning; Oil palm; Remote sensors |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Engineering Deputy Vice Chancellor (Research & Innovation) Office > Centre for Research in Biotechnology for Agriculture |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 31 Mar 2022 03:40 |
| Last Modified: | 31 Mar 2022 03:40 |
| URI: | http://eprints.um.edu.my/id/eprint/27908 |
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