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. 2023 Dec 7;23(24):9678.
doi: 10.3390/s23249678.

An Aquaphotomics Approach for Investigation of Water-Stress-Induced Changes in Maize Plants

Daniela Moyankova  1 , Petya Stoykova  1 , Petya Veleva  2 , Nikolai K Christov  1 , Antoniya Petrova  2 , Stefka Atanassova  2
Affiliations

An Aquaphotomics Approach for Investigation of Water-Stress-Induced Changes in Maize Plants

Daniela Moyankova et al. Sensors (Basel). .

Abstract

The productivity of plants is considerably affected by various environmental stresses. Exploring the specific pattern of the near-infrared spectral data acquired non-destructively from plants subjected to stress can contribute to a better understanding of biophysical and biochemical processes in plants. Experiments for investigating NIR spectra of maize plants subjected to water stress were conducted. Two maize lines were used: US corn-belt inbred line B37 and mutant inbred XM 87-136, characterized by very high drought tolerance. After reaching the 4-leaf stage, 10 plants from each line were subjected to water stress, and 10 plants were used as control, kept under a regular water regime. The drought lasted until day 17 and then the plants were recovered by watering for 4 days. A MicroNIR OnSite-W Spectrometer (VIAVI Solutions Inc., Chandler, AZ, USA) was used for in vivo measurement of each maize leaf spectra. PLS models for determining drought days were created and aquagrams were calculated separately for the plants' second, third, and fourth leaves. Differences in absorption spectra were observed between control, stressed, and recovered maize plants, as well as between different measurement days of stressed plants. Aquagrams were used to visualize the water spectral pattern in maize leaves and how it changes along the drought process.

Keywords: NIR spectra; aquagrams; maize plant; water stress.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Scheme of experimental design.
Figure 2
Figure 2
Changes in leaf RWC (a) and plant growth (b) of inbred maize B37 and XM 87-136 during drought stress and recovery. Data are presented as the mean values ± standard deviation. (a) Relative water content (RWC) of tested maize lines; (b) plant growth of tested maize lines.
Figure 3
Figure 3
Phenotype response of inbred maize line B37 and XM 87-136 on the 17th day of drought stress treatment. c—control plants; d—maize plants on the 17th day of drought stress. (a) B37—day 17; (b) XM 87-136—day 17.
Figure 4
Figure 4
Average second derivative spectra (2D) of leaves of B37 and XM 87-136 maize lines in process of water stress. (a) B37; (b) XM 87-136.
Figure 5
Figure 5
Differences between second derivative spectra of water-stressed leaves, measured at day 3, and measured at 7, 10, 12, 14, and 17 days. (a) B37, 3rd leaf; (b) XM 87-136, 3rd leaf.
Figure 6
Figure 6
Average second derivative spectra of control, water-stressed, and recovery second, and fourth leaves of B37 (a) and XM 87-136 (b) maize lines.
Figure 6
Figure 6
Average second derivative spectra of control, water-stressed, and recovery second, and fourth leaves of B37 (a) and XM 87-136 (b) maize lines.
Figure 7
Figure 7
Aquagrams of the different leaves of inbred maize lines—B37 and XM 87-136—during water stress, recovery, and control conditions.
Figure 7
Figure 7
Aquagrams of the different leaves of inbred maize lines—B37 and XM 87-136—during water stress, recovery, and control conditions.
Figure 8
Figure 8
Aquagrams for water-stressed inbred maize lines—B37 and XM 87-137—at different days of water deprivation. (a) B37; (b) XM 87-136.
Figure 9
Figure 9
PLSR model for determination of days of drought of XM 87-136 plants.
Figure 10
Figure 10
Regression vector (a) and correlation spectra (b) of PLS models for determination of days of drought.
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