Name
Title | ||
|---|---|---|
Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy. |
Abstract | ||
|---|---|---|
•Electrical properties were investigated in early detection of BSR disease in oil palms.•SVM-FS selection model indicates the best statistical indicators.•SVM classifier shows better performance as compared to ANN classifier.•The impedance values were highly classified by Ganoderma disease.•Electrical properties have significant potential for detection of BSR diseases in oil palm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compag.2017.11.012 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Basal stem rot disease,Dielectric properties,Oil palm,Support vector machine,Artificial neural network | Computer vision,Palm,Support vector machine,Artificial intelligence,Dielectric spectroscopy,Engineering,Classifier (linguistics),Statistics,Artificial neural network,Random forest,Stem rot,Palm oil | Journal |
Volume | ISSN | Citations |
144 | 0168-1699 | 1 |
PageRank | References | Authors |
0.35 | 19 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alfadhl Yahya Khaled | 1 | 1 | 0.35 |
| Samsuzana Abd Aziz | 2 | 1 | 1.37 |
| Siti Khairunniza-Bejo | 3 | 2 | 1.57 |
| Nazmi Mat Nawi | 4 | 1 | 1.03 |
| Idris Abu Seman | 5 | 1 | 0.69 |