Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Simultaneous Quantification and Visualization of Photosynthetic Pigments in Lycopersicon Esculentum Mill under Different Levels of Nitrogen Application with Visible-near Infrared Hyperspectral Imaging Technology

Jiangui Zhao
ORCID logo ,
Ning Chen
,
Tingyu Zhu
,
Xuerong Zhao
,
Ming Yuan
,
Zhiqiang Wang
,
Guoliang Wang
,
Zhiwei Li
* ,
Huiling Du
*
Version 1 : Received: 30 June 2023 / Approved: 3 July 2023 / Online: 3 July 2023 (09:33:01 CEST)

A peer-reviewed article of this Preprint also exists.

Zhao, J.; Chen, N.; Zhu, T.; Zhao, X.; Yuan, M.; Wang, Z.; Wang, G.; Li, Z.; Du, H. Simultaneous Quantification and Visualization of Photosynthetic Pigments in Lycopersicon esculentum Mill. under Different Levels of Nitrogen Application with Visible-Near Infrared Hyperspectral Imaging Technology. Plants 2023, 12, 2956. Zhao, J.; Chen, N.; Zhu, T.; Zhao, X.; Yuan, M.; Wang, Z.; Wang, G.; Li, Z.; Du, H. Simultaneous Quantification and Visualization of Photosynthetic Pigments in Lycopersicon esculentum Mill. under Different Levels of Nitrogen Application with Visible-Near Infrared Hyperspectral Imaging Technology. Plants 2023, 12, 2956.

Abstract

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.

Keywords

hyperspectral imaging; photosynthetic pigments; leaf position; characteristic variable selection; visualization

Subject

Biology and Life Sciences, Horticulture

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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