Unverified paper record
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 (Basel, Switzerland) · 16 Aug 2023 · 10.3390/plants12162956
Abstract
Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to rapidly and accurately obtain 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), total chlorophylls (Chls) and total carotenoids (Cars) as indicators to study the variations in the leaf positions 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 using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the 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 (R 2 ), root mean square error (RMSE) and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43 and 2.38 for Chla; 0.8391, 0.53 and 2.49 for Chlb; 0.7899, 2.24 and 2.18 for Chls; and 0.7577, 0.27 and 2.03 for Cars, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis and fertilization decisions in plant growth management.
Plant phenotyping relevance
VNIR-HSIと波長選択・回帰モデルにより、トマト葉の光合成色素量を定量・可視化する手法が研究の中心であるため。
abstractusing visible-near infrared hyperspectral imaging (VNIR-HSI)
abstracta "coarse-fine" screening strategy was proposed using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the characteristic wavelengths.
abstractsimultaneous and quantitative models were established using partial least squares regression (PLSR).
abstractThe combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment.
Code and data availability
The paper's hyperspectral images, pigment measurements, and analysis code are not publicly deposited: the Data Availability Statement says 'Available upon request from the corresponding author.' The MDPI supplementary file contains only result tables (preprocessing comparisons, screened wavelengths, fertilizer/EC/pH) —
No evidence-backed public reproduction asset is currently recorded.
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