Terahertz sensing analysis for early detection of ganoderma boninense disease using near infrared (NIR) spectrometer / (Record no. 99755)

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
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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
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
  Not lost Library of Congress Classification   Not for loan Reference UMPLIB PEKAN UMPLIB PEKAN 18/07/2023   FTKEE .M37 2023 r Thesis T000002444 18/07/2023 1 18/07/2023 Thesis
  Not lost Library of Congress Classification     Reference UMPLIB PEKAN UMPLIB PEKAN 18/07/2023   CD13387 T000002445 18/07/2023 1 18/07/2023 Thesis

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