Yoon et al. (2025) Non-Destructive Drone-Based Multispectral and RGB Image Analyses for Regression Modeling to Assess Waterlogging Stress in Pseudolysimachion linariifolium

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Identification

Journal: Horticulturae
Year: 2025
Date: 2025-09-18
Authors: T. S. Yoon, Tae Wan Kim, Sung Yung Yoo
DOI: 10.3390/horticulturae11091139

Research Groups

Not explicitly mentioned in the provided text. The study implies research in plant physiology, horticulture, or environmental science.

Short Summary

This study evaluated the waterlogging stress responses of Pseudolysimachion linariifolium using non-destructive drone-based multispectral imagery, finding that vegetation indices like NDVI and GNDVI effectively quantify stress and correlate strongly with soil moisture content.

Objective

To evaluate the physiological responses and stress responses of Pseudolysimachion linariifolium to waterlogging conditions using non-destructive drone-based multispectral imagery.

Study Configuration

Spatial Scale: Individual plants (Pseudolysimachion linariifolium) within a garden setting, observed at a plot or plant level.
Temporal Scale: Experimental study observing stress responses "over time" following waterlogging treatment.

Methodology and Data

Models used:
- Statistical analysis: Correlation analysis, Principal Component Analysis (PCA), Hierarchical Clustering, Regression models.
- Software: R (ver. 4.3.2), Quantum Geographical Information System (QGIS ver. 3.42.1).
- Vegetation Indices calculated: Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Green Leaf Index (GLI), Normalized Green Red Difference Index (NGRDI), Blue Green Pigment Index (BGI), Visible Atmospherically Resistant Index (VARI).
Data sources:
- Drone-based multispectral imagery.
- Sensor-measured Cumulative volumetric Soil Moisture content (SM_Cum).

Main Results

Waterlogging treatment resulted in a 21% decrease in NDVI and over 34% decrease in GNDVI, indicating reduced photosynthetic activity and chlorophyll content.
Correlation analysis, principal component analysis, and hierarchical clustering successfully distinguished stress responses over time.
Regression models utilizing NDVI and GNDVI explained 89.7% of the variance in SM_Cum.
Drone-based vegetation index analysis is an effective method for quantifying waterlogging stress in garden plants.

Contributions

First application of non-destructive drone-based multispectral imagery to evaluate waterlogging stress responses in Pseudolysimachion linariifolium.
Demonstrates the efficacy of drone-based vegetation indices (NDVI, GNDVI) as reliable indicators for quantifying waterlogging stress in urban garden plants.
Provides a valuable tool for improved moisture management and growth monitoring strategies in urban garden environments.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Yoon2025NonDestructive,
  author = {Yoon, T. S. and Kim, Tae Wan and Yoo, Sung Yung},
  title = {Non-Destructive Drone-Based Multispectral and RGB Image Analyses for Regression Modeling to Assess Waterlogging Stress in Pseudolysimachion linariifolium},
  journal = {Horticulturae},
  year = {2025},
  doi = {10.3390/horticulturae11091139},
  url = {https://doi.org/10.3390/horticulturae11091139}
}

Original Source: https://doi.org/10.3390/horticulturae11091139