The onset of stomatal closure reduces transpiration during drought. In seed plants, drought causes declines in plant water status which increases leaf endogenous abscisic acid (ABA) levels required for stomatal closure. There are multiple possible points of increased belowground resistance in the soil–plant atmospheric continuum that could decrease leaf water potential enough to trigger ABA production and the subsequent decreases in transpiration. We investigate the dynamic patterns of leaf ABA levels, plant hydraulic conductance and the point of failure in the soil–plant conductance in the highly embolism‐resistant species We show that decreases in transpiration and ABA biosynthesis begin before any permanent decreases in predawn water potential, collapse in soil–plant hydraulic pathway and xylem embolism spread. We find that a dynamic but recoverable increases in hydraulic resistance in the soil in close proximity to the roots is the most likely driver of declines in midday leaf water potential needed for ABA biosynthesis and the onset of decreases in transpiration.
Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras.
In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions.
Two different classifiers were trained to predict the plant treatment—random forest and multilayer perceptron neural networks—finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll
Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.
- Award ID(s):
- 2102120
- PAR ID:
- 10527951
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Plant Science
- Volume:
- 14
- ISSN:
- 1664-462X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fpls.2023.1305292
