StomaVision: stomatal trait analysis through deep learning

Ting-Li Wu, Po-Yu Chen,Xiaofei Du, Heiru Wu, Jheng-Yang Ou, Po-Xing Zheng,Yu-Lin Wu, Ruei-Shiuan Wang, Te-Chang Hsu, Chen-Yu Lin,Wei-Yang Lin, Ping-Lin Chang, Chin-Min Kimmy Ho,Yao-Cheng Lin

crossref(2024)

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摘要
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 <> and available for local use in a containerized environment at <>, 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. Plain language summary StomaVision is a tool that automatically counts and measures tiny openings on plant leaves, helping us learn how plants deal with their surroundings. It is easy to use and works well with various plant species. This tool helps scientists see how plants change under stress, making plant research easier and more accurate. ### Competing Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. XD and PLC are the cofounders of Instiall AI, Ltd. PYC, XD, HW and PLC are employed by Instiall AI Ltd. The remaining authors declare no competing interests.
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