Title
Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors.
Abstract
The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis.
Year
DOI
Venue
2016
10.3390/s16040437
SENSORS
Keywords
Field
DocType
winter wheat,leaf chlorophyll content,visible and near infrared sensors,agricultural information acquisition,partial least squares
Correlation coefficient,Remote sensing,Partial least squares regression,Near-infrared spectroscopy,Mean squared error,Smoothing,Engineering,Spectroscopy,Wavelength,Chlorophyll
Journal
Volume
Issue
ISSN
16
4.0
1424-8220
Citations 
PageRank 
References 
0
0.34
7
Authors
6
Name
Order
Citations
PageRank
Jianfeng Zhang141.85
Wenting Han222.77
Lvwen Huang311.03
Zhiyong Zhang4248.79
Yimian Ma510.82
Yamin Hu600.34