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Pongprada Kingwattanakul. Determination Lactone composition in Andrographis paniculata (burmf) Wall ex Nees using hyperspectral imaging. Master's Degree(Artificial Intelligence and Internet of Things). Kasetsart University. Office of the University Library. : Kasetsart University, 2021.
Determination Lactone composition in Andrographis paniculata (burmf) Wall ex Nees using hyperspectral imaging
Name: Pongprada Kingwattanakul
Classification :.LCCS: TA1637
LCSH:
Hyperspectral imaging
LCSH:
Image processing
LCSH:
Image segmentation
LCSH:
Image analysis
LCSH:
Lactones
LCSH:
Andrographis paniculata
Abstract:
Nowadays, hyperspectral images (HSI) have been widely used in the chemometrics field. Partial least square regression (PLSR) and principal component regression model (PCR) were dimensional reductions applied to solve the large p small n problem that is commonly found in HSIs. In this study, the main objective is to establish a prediction model in order to estimate the lactone content in Andrographis paniculata. Spectral reflectance of HSIs within wavelengths between 355 - 1702 nm. was extracted from pixels of plants that are separated from the background by the normalized difference vegetation index (NDVI). The preprocessing method including the first and the second derivatives of the SavitzkyGolay filter was used to eliminate the noise that comes along with raw spectral data. To select optimal wavelength regression coefficient (RC), and backward variable elimination (BVE) were employed in this study. This research was divided into two experiments. The first one trained the model with all samples, on the other hand, the second experiment will group samples by stage of the plant before training them separately. The result showed that more than 85% of spectral variables were eliminated after feature selection. Models were performed in this study indicated the model in the second experiment that pre-processing spectral with the second derivative and variable selection by BVE yields better performance than another algorithm with Rp value of 0.980, RMSEP value of 1.168, RCV value of 0.975, and RMSECV value of 1.302 (Averaged from 3 models in the second experiment). The results show that using hyperspectral images combinate with the PLSR model can perform a reliable and accurate prediction.
Kasetsart University. Office of the University Library
Address:
Bangkok
Email:
tdckulib@ku.ac.th
Name: Teerasit Kasetkasem
Role:
Thesis Advisor
Name: Teera Phatrapornnant
Role:
Thesis CO-Advisor
Name: Masahiro Yamaguchi
Role:
Thesis CO-Advisor
Created:
2021
Modified:
2025-07-20
Issued:
2025-07-20
วิทยานิพนธ์/Thesis
application/pdf
URL: https://www.lib.ku.ac.th/KUthesis/2564/pongprada-kin-all.pdf
CallNumber:
TA1637 .P67
eng
DegreeName: Master of Engineering
Level: Master's Degree
Descipline: Artificial Intelligence and Internet of Things
Grantor: Kasetsart University
©copyrights Kasetsart University
RightsAccess:
ใช้เวลา
0.036235 วินาที
Pongprada Kingwattanakul
| Title | Contributor | Type |
|---|---|---|
| Determination Lactone composition in Andrographis paniculata (burmf) Wall ex Nees using hyperspectral imaging
มหาวิทยาลัยเกษตรศาสตร์ Pongprada Kingwattanakul | Teerasit Kasetkasem | วิทยานิพนธ์/Thesis |
Teerasit Kasetkasem
Teera Phatrapornnant
| Title | Creator | Type and Date Create |
|---|---|---|
| The SIP-Based radio over internet protocol interoperability communication system for emergency situation
มหาวิทยาลัยเกษตรศาสตร์ Mongkol Raksapatcharawong;Teera phatrapornnant;Yamaoka, Katsunori | Sirinya Tabsombat | วิทยานิพนธ์/Thesis |
Masahiro Yamaguchi
| Title | Creator | Type and Date Create |
|---|