A simple system for phenotyping of plant transpiration and stomatal conductance response to drought

Driever, Steven M.; Mossink, Leon; Ocaña, Diego Nuñez; Kaiser, Elias

doi:10.1016/j.plantsci.2023.111626

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…Non-destructive methods, such as infrared gas exchange analyzers (Garen et al ., 2022) and infrared imaging (Jones et al ., 2002), infer stomatal activities through infrared gas sensors or leaf surface temperature. Infrared gas exchange analyzers are often considered the ‘gold standard’ for determining gaseous fluxes (Driever et al ., 2023). Destructive techniques, such as stomatal imprints (Scarpeci et al ., 2017), chemical dye staining (Eisele et al ., 2016), and scanning electron microscopy (Tsai et al ., 2022), provide direct visualization of stomatal traits such as stomatal density, stomatal shape, stomatal pore size, and stomatal distribution.…”

Section: Introductionmentioning

confidence: 99%

StomaVision: stomatal trait analysis through deep learning

Wu,

Chen,

et al. 2024

Preprint

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StomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate. It provides insights into plant responses to environmental cues, streamlining the analysis of micrographs from field-grown plants across various species, including monocots and dicots. Enhanced by a novel collection method that utilizes video recording, StomaVision increases the number of captured images for robust statistical analysis. Accessible via an intuitive web interface at <https://stomavision.streamlit.app/> and available for local use in a containerized environment at <https://github.com/YaoChengLab/StomaVision>, this tool ensures long-term usability by minimizing the impact of software updates and maintaining functionality with minimal setup requirements. The application of StomaVision has provided significant physiological insights, such as variations in stomatal density, opening rates, and total pore area under heat stress. These traits correlate with critical physiological processes, including gas exchange, carbon assimilation, and water use efficiency, demonstrating the tool's utility in advancing our understanding of plant physiology. The ability of StomaVision to identify differences in responses to varying durations of heat treatment highlights its value in plant science research.

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Section: Introductionmentioning

confidence: 99%

StomaVision: stomatal trait analysis through deep learning

Wu,

Chen,

et al. 2024

Preprint

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Botanic Signal Monitor: Advanced Wearable Sensor for Plant Health Analysis

Xu,

Chen,

et al. 2024

Adv Funct Materials

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Recently, the decline in plant species, loss of crop yields, and reduced efficacy of herbal medicines due to environmental issues and biotic stresses have garnered significant attention. Developing smart devices for plant health monitoring is essential for early intervention, timely adjustment of the growing environment to combat environmental and pest stresses, and to promote robust plant growth, biodiversity, ecological balance, and sustainable agriculture. Flexible wearable sensors with the advantages of superior shape adaptation, excellent biocompatibility, and high integration have emerged as one of the most promising avenues for plant health monitoring. Here, recent advances in flexible wearable sensors based on monitoring different types of plant signals are summarized. The discussion focuses on the constituent materials, fabrication methods, and sensing mechanisms of each type of wearable sensor. In addition, the challenges and potential strategies are summarized for plant sensing development, including energy supply, materials and preparation, signal transmission, and data analysis.

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