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. 2023 Mar 18;24(6):5825. doi: 10.3390/ijms24065825

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© 2023 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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SPA feature band selection and PCA dimension reduction. (A,B) Spectral reflectance and first derivative bands extracted by SPA; (C,D) 3D spatial distribution of the first three principal components of spectral reflectance and the first derivative; (E,F) the first three principal components’ loadings of spectral reflectance and the first derivative. Python 3.6 was used to extract SPAD characteristic bands. Prism 9 was used to calculate PCA loadings. The percentage is the proportion of the variance explained by each principal component. SPA, successive projections algorithm; PCA, principal component analysis. Obj, first calibration object; Var, selected variables. CK, control; DS, drought stress.