Title
Moisture content prediction in tealeaf with near infrared hyperspectral imaging
Abstract
Texture and spectrum are used for moisture content prediction of tea.TDGF is adopted to analyze hyperspectral image.PCPLS and SPAPLS are built for laboratory research and industrial application. Near infrared (NIR) hyperspectral imaging has been used as a rapid non-destructive technique to predict moisture content of tea. To improve the performance of predicting, we first find and validate the fact that the texture near the veins is continues and directional. And then we propose Three-Dimension Gabor Filter (TDGF) and its corresponding filterbank to describe the textures of tealeaf. After that we construct two types of models based on partial least squares (PLS) regression. Experiments are conducted to predict the moisture content of Longjing tea, and different regression models based on different types of features are built for comparison. The results show that the proposed filterbank is able to detect the optimal direction of water flow and the model combining the spectrum and TDGF textures outperform the other comparative models.
Year
DOI
Venue
2015
10.1016/j.compag.2015.08.014
Computers and Electronics in Agriculture
Keywords
Field
DocType
Hyperspectral image,Moisture content prediction,Three-Dimension Gabor Filter,Longjing tea,Partial-least-square regression
Computer vision,Water flow,Regression analysis,Filter bank,Remote sensing,Near-infrared spectroscopy,Partial least squares regression,Gabor filter,Hyperspectral imaging,Artificial intelligence,Engineering,Water content
Journal
Volume
Issue
ISSN
118
C
0168-1699
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
Shuiguang Deng1107283.66
Yifei Xu2141.95
Xiaoli Li3325.79
Yong He44415.57