An Integrated Method for Tracking and Monitoring Stomata Dynamics from Microscope Videos

Abstract

Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.

An Affordable Image-Analysis Platform to Accelerate Stomatal Phenotyping During Microscopic Observation

“…Thus, real-time image analysis during observation enables quality assurance of the analyzed data by the operator. In addition, high-speed image analysis enables a time-course analysis, which reveals the dynamics of stomatal aperture when living cells are observed ( Sun et al, 2021 ). There is another potential benefit of real-time image processing for the development of further automatic high-throughput phenotyping systems.…”

“…We focused on the detection of stomata from wheat-related species, which have the dumbbell-shaped stomata typical of grass plants. Similarly, to the increasing reports of stomata detection of kidney shaped stomata of dicots, detection of dumbbell shaped stomata of grass are also starting to be established, exemplified by that of wheat (Sun et al, 2021). In the present study, the training dataset was prepared from only hexaploid wheat cultivars with some augmentation, thus the variations in SS and morphology of diploid and tetraploid species were not included.…”

Rapid non-destructive method to phenotype stomatal traits

“…The 400× lens offers higher resolution of stomata and allows accurate quantification of stomatal size and aperture. The high resolution of the 400× lens makes it possible to record videos of the leaf surface in wheat and to observe and quantify the dynamic changes of stomatal aperture in response to environment, a technique successfully tested in wheat (21). The 200× lens offers a satisfactory compromise between field of view and resolution and can be used to phenotype all stomatal traits in wheat using the same image, which further reduces the time of image acquisition.…”

“…Use of nail polish methods are impractical when working with large populations and having to take thousands of measurements from greenhouse or field grown plants, which require higher throughput image acquisition and analysis. To overcome indirect leaf imaging, approaches of directly taking images of leaves by bringing harvested plant material or whole plants to a microscope have been trialled (21)(22)(23). Direct leaf imaging further accelerates stomatal phenotyping and demonstrates the potential of high-throughput stomata phenotyping tools in revealing novel insights on the genetic basis of stomata-related traits (24).…”

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

“…Ground-based platforms are the most common platforms for data acquisition and can be further divided into indoor and outdoor platforms depending on the working environment. Indoor platforms are usually under controlled conditions and are oriented toward phenotyping at the level of the individual plant ( Bi et al., 2021 ), organ ( Sarkar et al., 2021 ), or cell ( Sun et al., 2021 ), which is particularly advantageous for acquiring comparable phenotypic data. Ground-based outdoor platforms such as gantries, handheld or backpack instruments, and mobile vehicles focus on plant- to canopy-scale phenotyping.…”