2018
DOI: 10.1016/j.chemolab.2017.12.001
|View full text |Cite
|
Sign up to set email alerts
|

Determining optimum wavelengths for leaf water content estimation from reflectance: A distance correlation approach

Abstract: This paper proposes a method to estimate leaf water content from reflectance in four commercial vineyard varieties by estimating the local maxima of a distance correlation function. First, it applies four different functional regression models to the data and compares the models to test the viability of estimating water content from reflectance. It then applies our methodology to select a small number of wavelengths (optimum wavelengths) from the continuous spectrum, which simplifies the regression problem. Fi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
4
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 7 publications
(4 citation statements)
references
References 45 publications
0
4
0
Order By: Relevance
“…Involving the re-centering of Euclidean distance, first, the distance correlation between two random variables are calculated, and then the calculated value is compared to the distance correlations of many shuffles of the data. Being X ∈ R p and Y ∈ R q two random variables, the DC is defined as (Ordóñez et al, 2018):…”
Section: Model Validationmentioning
confidence: 99%
“…Involving the re-centering of Euclidean distance, first, the distance correlation between two random variables are calculated, and then the calculated value is compared to the distance correlations of many shuffles of the data. Being X ∈ R p and Y ∈ R q two random variables, the DC is defined as (Ordóñez et al, 2018):…”
Section: Model Validationmentioning
confidence: 99%
“…This ratio generally changes during the vegetative cycle, one of the main factors for change being the available illumination, which has variations as the vegetative season advances (SESTAK, 1963;BENERAGAMA & GOTO, 2011) with a trend towards smaller ratios. Determination of chlorophyll levels are conventionally done by laboratory techniques applied on field-collected samples; however, as the characteristic plant green shades are due to the reflected light after interaction of illuminating radiation with leaf photosynthesizing pigments, chlorophyll amounts can be estimated by non-destructive methods (FASSNACHT et al, 2015;ORDÓÑEZ et al, 2018;STEELE et al, 2008) like reflectance analysis at hyperspectral resolutions, allowing to map with improved performance the spectral properties of plant leaves at visible and near infrared wavelengths, studying color changes, hemispherical reflectance and subtle variations in leaf tissues (ZHAO et al, 2014). Chlorophyll levels are also sensitive to water stress and to soil type (MITRA et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…However, traditional linear classification methods may encounter serious challenges and fail to explore the complex structure of spectral data. In practice, some chemical systems and problems often are nonlinear or affected by the outliers points and heavy‐tailed variables. The nonparametric ranking method, the sum of ranking differences, is used to analyse these chemical data, but some aforementioned methods are not suitable in the multiclass chemometrics datasets .…”
Section: Introductionmentioning
confidence: 99%
“…After the feature screening, some nonparametric classification models, such as Random forest, Boosting, kernel support vector machine (SVM), and functional data methods, are more suitable to analyse the data because these models also do not give some parametrical assumption between the responses and predictors. Among them, Random forest can handle different types of datasets with the low dimension, high dimension, binary classification, multiple classification, and so on.…”
Section: Introductionmentioning
confidence: 99%