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.
authors.affiliation: College of Information Engineering, Northwest A&amp;F University, Yangling 712100, China. zjf@nwsuaf.edu.cn.
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Institute of Water Saving Agriculture in Arid Area of China, Northwest A&amp;F University, Yangling 712100, China. hanwt2000@126.com.
authors.forename: Wenting
authors.initials: W
authors.lastname: Han
authors.affiliation: College of Information Engineering, Northwest A&amp;F University, Yangling 712100, China. springchilly@gmail.com.
authors.forename: Lvwen
authors.initials: L
authors.lastname: Huang
authors.affiliation: College of Information Engineering, Northwest A&amp;F University, Yangling 712100, China. zzy@nwsuaf.edu.cn.
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: College of Information Engineering, Northwest A&amp;F University, Yangling 712100, China. 15339142186@163.com.
authors.forename: Yimian
authors.initials: Y
authors.lastname: Ma
authors.affiliation: College of Information Engineering, Northwest A&amp;F University, Yangling 712100, China. yaminexc@gmail.com.
authors.forename: Yamin
authors.initials: Y
authors.lastname: Hu
mesh_terms: D015374:Biosensing Techniques; D002734:Chlorophyll / Q000302:isolation & purification* / Q000378:metabolism; D018515:Plant Leaves / Q000737:chemistry* / Q000378:metabolism; D019265:Spectroscopy, Near-Infrared; D014908:Triticum / Q000737:chemistry* / Q000378:metabolism
chemical_list: D002734:Chlorophyll
keywords: agricultural information acquisition; leaf chlorophyll content; partial least squares; visible and near infrared sensors; winter wheat