A Stomata Classification and Detection System in Microscope Images of Maize Cultivars
by Aono A. H., Nagai J. S., Dickel G. S. M., Marinho R. C., de Oliveira P. E. A. M., Faria F. A. (2019)
Alexandre H. Aono a, James S. Nagai a, Gabriella da S. M. Dickel b, Rafaela C. Marinho b, Paulo E. A. M. de Oliveira b, Fabio A. Faria a,∗
a Instituto de Ciencia e Tecnologia, Universidade Federal de S˜ao Paulo – UNIFESP 12247-014, S˜ao Jos´e dos Campos, SP – Brazil b Instituto de Biologia, Universidade Federal de Uberlˆandia Uberlndia, MG, Brazil
Stomata are morphological structures of plants that have been receiving constant attention. These pores are responsible for the interaction between the internal plant system and the environment, working on different processes such as photosynthesis process and transpiration stream. As evaluated before, understanding the pore mechanism play a key role to explore the evolution and behavior of plants. Although the study of stomata in dicots species of plants have advanced, there is little information about stomata of cereal grasses. In addition, automated detection of these structures have been presented on the literature, but some gaps are still uncovered.
This fact is motivated by high morphological variation of stomata and the presence of noise from the image acquisition step. Herein, we propose a new methodology of an automatic stomata classification and detection system in microscope images for maize cultivars. In our experiments, we have achieved an approximated accuracy of 97.1% in the identification of stomata regions using classifiers based on deep learning features.
Stomatal function can be used effectively to monitor plant hydraulics, photosensitivity, and gas exchange. Current approaches to measure single stomatal aperture, such as mold casting or fluorometric techniques, do not allow real time or persistent monitoring of the stomatal function over timescales relevant for long term plant physiological processes, including vegetative growth and abiotic stress. Herein, we utilize a nanoparticle-based conducting ink that preserves stomatal function to print a highly stable, electrical conductometric sensor actuated by the stomata pore itself, repeatedly and reversibly for over 1 week. This stomatal electro-mechanical pore size sensor (SEMPSS) allows for real-time tracking of the latency of single stomatal opening and closing times in planta, which we show vary from 7.0 ± 0.5 to 25.0 ± 0.5 min for the former and from 53.0 ± 0.5 to 45.0 ± 0.5 min for the latter in Spathiphyllum wallisii. These values are shown to correlate with the soil water potential and the onset of the wilting response, in quantitative agreement with a dynamic mathematical model of stomatal function. A single stoma of Spathiphyllum wallisii is shown to distinguish between incident light intensities (up to 12 mW cm−2) with temporal latency slow as 7.0 ± 0.5 min. Over a seven day period, the latency in opening and closing times are stable throughout the plant diurnal cycle and increase gradually with the onset of drought. The monitoring of stomatal function over long term timescales at single stoma level will improve our understanding of plant physiological responses to environmental factors.
Stomata, functionally specialized micrometer-sized pores on the epidermis of leaves (mainly on the lower epidermis), control the flow of gases and water between the interior of the plant and atmosphere. Real-time monitoring of stomatal dynamics can be used for predicting the plant hydraulics, photosensitivity, and gas exchanges effectively. To date, several techniques offer the direct or indirect measurement of stomatal dynamics, yet none offer real-time, long-term persistent measurement of multiple stomal apertures simultaneously of an intact leaf in a field under natural conditions. Here, we report a high-resolution portable microscope-based technique for in situ real-time field imaging and monitoring of stomata. Our technique is capable of analyzing and quantifying the multiple lower epidermis stomal pore dynamics simultaneously and does not require any physical or chemical manipulation of a leaf. An upward facing objective lens in our portable microscope allows the imaging of lower epidermis stomatal opening of a leaf while upper epidermis being exposed to the natural environment. Small depth of field (~ 1.3 μm) of a high-magnifying objection lens assists in focusing the stomatal plane in highly non-planar tomato leaf having a high density of trichome (hair-like structures). For long-term monitoring, the leaf is fixed mechanically by a novel designed leaf holder providing freedom to expose the upper epidermis to the sunlight and lower epidermis to the wind simultaneously. In our study, a direct relation between the stomatal opening and the intensity of sunlight illuminating on the upper epidermis has been observed in real-time. In addition, real-time porosity of leaf (ratio between the areas of stomatal opening to the area of a leaf) and stomatal aspect ratio (ratio between the major axis and minor axis of stomatal opening) along with stomatal density have been quantified.
The stomata of plants mainly regulate gas exchange and water dispersion between the interior and external environments of plants and play a major role in the plants’ health. The existing methods of stomata segmentation and measurement are mostly for specialized plants. The purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants. The proposed method utilizes level set theory and image processing technology and can outperform the existing stomata segmentation and measurement methods based on threshold and skeleton in terms of its versatility.
Stomatal movement mediates plant gas exchange, which is essential for photosynthesis and transpiration. Stomatal opening and closing are accomplished by a significant increase and decrease in guard cell volume, respectively. Because shuttle transport of ions and water occurs between guard cells and larger neighboring epidermal cells during stomatal movement, the spaced distribution of plant stomata is considered an optimal distribution for stomatal movement. Experimental systems for perturbing the spaced pattern of stomata are useful to examine the spacing pattern’s significance. Several key genes associated with the spaced stomatal distribution have been identified, and clustered stomata can be experimentally induced by altering these genes. Alternatively, clustered stomata can be also induced by exogenous treatments without genetic modification. In this article, we describe a simple induction system for clustered stomata in Arabidopsis thaliana seedlings by immersion treatment with a sucrose-containing medium solution. Our method is easy and directly applicable to transgenic or mutant lines. Larger chloroplasts are presented as a cell biological hallmark of sucrose-induced clustered guard cells. In addition, a representative confocal microscopic image of cortical microtubules is shown as an example of intracellular observation of clustered guard cells. The radial orientation of cortical microtubules is maintained in clustered guard cells as in spaced guard cells in control conditions.
Segmenting High-quality Digital Images of Stomata using the Wavelet Spot Detection and the Watershed Transform
by Duarte K. T. N., de Carvallo M. A.G., Martins P. S. (2017)
Kaue T. N. Duarte, Marco A. G. de Carvalho, Paulo S. Martins,
University of Campinas (UNICAMP), School of Technology, R. Paschoal Marmo, 1888, 13484 Limeira, Brazil
In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 540-547 – ISBN: 978-989-758-225-7 – DOI: 10.5220/0006168105400547 –
Stomata are cells mostly found in plant leaves, stems and other organs. They are responsible for controlling the gas exchange process, i.e. the plant absorbs air and water vapor is released through transpiration. Therefore, stomata characteristics such as size and shape are important parameters to be taken into account. In this paper, we present a method (aiming at improved efficiency) to detect and count stomata based on the analysis of the multi-scale properties of the Wavelet, including a spot detection task working in the CIELab colorspace.
We also segmented stomata images using the Watershed Transform, assigning each spot initially detected as a marker. Experiments with real and high-quality images were conducted and divided in two phases. In the first, the results were compared to both manual enumeration and another recent method existing in the literature, considering the same dataset. In the second, the segmented results were compared to a gold standard provided by a specialist using the F-Measure. The experimental results demonstrate that the proposed method results in better effectiveness for both stomata detection and segmentation.