MARC details
| 000 -LEADER |
| fixed length control field |
04021ntm a2200373 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
| control field |
20251125110752.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
| fixed length control field |
230718t20232023my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
| International Standard Book Number |
THE0009479 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKEE .M37 2023 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
| Personal name |
Mas Ira Syafila Mohd Hilmi Tan, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
| Title |
Terahertz sensing analysis for early detection of ganoderma boninense disease using near infrared (NIR) spectrometer / |
| Statement of responsibility, etc. |
Mas Ira Syafila Mohd Hilmi Tan |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Place of production, publication, distribution, manufacture |
Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
| Extent |
xix, 279 pages : |
| Other physical details |
Illustration ; |
| Dimensions |
30 cm.+ |
| Accompanying material |
1 CD ROM |
| 336 ## - CONTENT TYPE |
| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
| Source |
rdamedia |
| Media type term |
unmediated |
| 337 ## - MEDIA TYPE |
| Source |
rdamedia |
| Media type term |
computer |
| 338 ## - CARRIER TYPE |
| Source |
rdacarrier |
| Carrier type term |
volume |
| 338 ## - CARRIER TYPE |
| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
| General note |
Faculty of Electrical & Electronics Engineering Technology |
| 502 ## - DISSERTATION NOTE |
| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
| Bibliography, etc. note |
Includes bibliographical reference |
| 520 3# - SUMMARY, ETC. |
| Summary, etc. |
Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a significant impact on the economic viability of oil palm plantations. Early detection is critical for the effective management of this disease since there is no effective treatment that can stop the spread of this disease. The proposed system uses integrated hand-held near-infrared spectroscopy (NIRS) for early detection of G. boninense on asymptomatic oil palm seedlings and classification of spectral data using machine learning (ML) techniques. The non-destructive method using NIRS with ML and predictive analytics has the potential to be a highly sensitive and reliable method for the early detection of G. boninense. Spectral data are collected from 6 samples of inoculated and non-inoculated oil palm samples at nursery stages using an integrated NIRS sensor. Chemometrics is performed by implementing principal component analysis (PCA), derivatives and partial least square (PLS) regression to extract the vital information of the spectra. The significant wavelengths are at 1310 nm and 1450 nm which are attributable to ergosterol and water content, respectively. Furthermore, the SG derivatives spectra peaks corresponded to specific functional groups that could be utilized for the detection of G. boninense. These functional groups encompass the third overtone of N-H stretching, the second overtone of C-H stretching, and a combination band involving both C-H stretching and O-H stretching. High-performance liquid chromatography (HPLC) analysis is performed to identify the ergosterol content in oil palm sample. Ergosterol can be used as a biomarker for the detection of G. boninense since it can only be found in the fungal-infested plant. In classification, four different ML algorithms: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested to classify healthy and infected oil palm samples. DT algorithm on leaves spectra achieves a satisfactory overall performance compared to the other classifiers with high accuracy up to 93.1% and an F1-score of 92.6%. Therefore, a DT-based predictive analytic on leaves NIR spectral reference data is developed for real-time detection of G. boninense infection. A portable smart G. boninense detection system prototype is developed by implementing the Internet of Things (IoT) into the system which enables the integration of sensors and server to perform prediction of healthy or infected oil palm seedlings. This working prototype showed that this proposed approach is reliable and practical for the early detection of G. boninense in oil palm seedlings. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
| Corporate name or jurisdiction name as entry element |
Faculty of Electrical & Electronics Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Thesis |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |