An official website of the United States government
Abstract By regulating carbon uptake and water loss by plants, stomata are not only responsible for productivity but also survival during drought. The timing of the onset of stomatal closure is crucial for preventing excessive water loss during drought, but is poorly explained by plant hydraulics alone and what triggers stomatal closure remains disputed. We investigated whether the hormone abscisic acid (ABA) was this trigger in a highly embolism‐resistant tree speciesUmbellularia californica. We tracked leaf ABA levels, determined the leaf water potential and gravimetric soil water content (gSWC) thresholds for stomatal closure and transpiration decline during a progressive drought. We found thatU. californicaplants have a peaking‐type ABA dynamic, where ABA levels rise early in drought and then decline under prolonged drought conditions. The early increase in ABA levels correlated with the closing of stomata and reduced transpiration. Furthermore, we found that transpiration declined before any large decreases in predawn plant water status and could best be explained by transient drops in midday water potentials triggering increased ABA levels. Our results indicate that ABA‐mediated stomatal regulation may be an integral mechanism for reducing transpiration during drought before major drops in bulk soil and plant water status.
more »
« less
AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of Gossypium hirsutum L.
IntroductionDrought 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. MethodsIn 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. ResultsTwo 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 chlorophyllafluorescence, to provide more insights and possible explanations about the classification outputs. DiscussionInterestingly, 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.
more »
« less
- 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
More Like this
-
-
Summary 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 speciesCallitris tuberculatausing continuous dendrometer measurements of leaf water potential during drought.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.more » « less
-
Abstract Metrics to quantify regulation of plant water status at the daily as opposed to the seasonal scale do not presently exist. This gap is significant since plants are hypothesised to regulate their water potential not only with respect to slowly changing soil drought but also with respect to faster changes in air vapour pressure deficit (VPD), a variable whose importance for plant physiology is expected to grow because of higher temperatures in the coming decades. We present a metric, the stringency of water potential regulation, that can be employed at the daily scale and quantifies the effects exerted on plants by the separate and combined effect of soil and atmospheric drought. We test our theory using datasets from two experiments where air temperature and VPD were experimentally manipulated. In contrast to existing metrics based on soil drought that can only be applied at the seasonal scale, our metric successfully detects the impact of atmospheric warming on the regulation of plant water status. We show that the thermodynamic effect of VPD on plant water status can be isolated and compared against that exerted by soil drought and the covariation between VPD and soil drought. Furthermore, in three of three cases, VPD accounted for more than 5 MPa of potential effect on leaf water potential. We explore the significance of our findings in the context of potential future applications of this metric from plant to ecosystem scale.more » « less
-
Abstract Under drought conditions, arbuscular mycorrhizal (AM) fungi may improve plant performance by facilitating the movement of water through extensive hyphal networks. When these networks interconnect neighboring plants in common mycorrhizal networks (CMNs), CMNs are likely to partition water among many individuals. The consequences of CMN-mediated water movement for plant interactions, however, are largely unknown. We set out to examine CMN-mediated interactions amongAndropogon gerardiiseedlings in a target-plant pot experiment, with watering (watered or long-term drought) and CMN status (intact or severed) as treatments. Intact CMNs improved the survival of seedlings under drought stress and mediated positive, facilitative plant interactions in both watering treatments. Watering increased mycorrhizal colonization rates and improved P uptake, particularly for large individuals. Under drought conditions, improved access to water most likely benefited neighboring plants interacting across CMNs. CMNs appear to have provided the most limiting resource within each treatment, whether P, water, or both, thereby improving survival and growth. Neighbors near large, photosynthate-fixing target plants likely benefited from their establishment of extensive hyphal networks that could access water and dissolved P within soil micropores. In plant communities, CMNs may be vital during drought, which is expected to increase in frequency, intensity, and length with climate change.more » « less
-
Maize (Zea mays L.) is one of the three major cereal crops in the world. Leaf angle is an important architectural trait of crops due to its substantial role in light interception by the canopy and hence photosynthetic efficiency. Traditionally, leaf angle has been measured using a protractor, a process that is both slow and laborious. Efficiently measuring leaf angle under field conditions via imaging is challenging due to leaf density in the canopy and the resulting occlusions. However, advances in imaging technologies and machine learning have provided new tools for image acquisition and analysis that could be used to characterize leaf angle using three-dimensional (3D) models of field-grown plants. In this study, PhenoBot 3.0, a robotic vehicle designed to traverse between pairs of agronomically spaced rows of crops, was equipped with multiple tiers of PhenoStereo cameras to capture side-view images of maize plants in the field. PhenoStereo is a customized stereo camera module with integrated strobe lighting for high-speed stereoscopic image acquisition under variable outdoor lighting conditions. An automated image processing pipeline (AngleNet) was developed to measure leaf angles of nonoccluded leaves. In this pipeline, a novel representation form of leaf angle as a triplet of keypoints was proposed. The pipeline employs convolutional neural networks to detect each leaf angle in two-dimensional images and 3D modeling approaches to extract quantitative data from reconstructed models. Our study demonstrates the feasibility of using stereo vision to investigate the distribution of leaf angles in maize under field conditions. The proposed system is an efficient alternative to traditional leaf angle phenotyping and thus could accelerate breeding for improved plant architecture.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fpls.2023.1305292
