Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited discoveries about the genetic basis of stomatal patterning. A high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays). The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 0.97) and stomatal complex area (SCA; R2 0.71) were strongly correlated with human measurements. Leaf gas exchange traits were genetically correlated with the dimensions and proportions of stomatal complexes (rg 0.39-0.71) but did not correlate with SCD. Heritability of epidermal traits was moderate to high (h2 0.42-0.82) across two field seasons. Thirty-six QTL were consistently identified for a given trait in both years. Twenty-four clusters of overlapping QTL for multiple traits were identified, with univariate versus multivariate single marker analysis providing evidence consistent with pleiotropy in multiple cases. Putative orthologs of genes known to regulate stomatal patterning in Arabidopsis (Arabidopsis thaliana) were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a C4 model species where these processes are poorly understood.

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic and biological studies... (truncated for brevity)

Author contributions: SBC project conception, acquisition of stomatal peels and microscopy images, image annotation for machine learning training set, statistical analysis and biological advice for HS. HS generation of automated stomatal counting algorithm and software. EHM and SM project conception, guidance and advice.. CC-BY-NC-ND 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 3, 2021. ; https://doi. Abstract Stomata are dynamic structures that control the gaseous exchange of CO2 from the external to internal environment and water loss through transpiration. The density and morphology of stomata have important consequences in crop productivity and water use efficiency, both are integral considerations when breeding climate change resilient crops. The phenotyping of stomata is a slow manual process and provides a substantial bottleneck when characterising phenotypic and genetic variation for crop improvement. There are currently no open-source methods to automate stomatal counting. We used 380 human annotated micrographs of O. glaberrima and O. sativa at x20 and x40 objectives for testing and training. Training was completed using the transfer learning for deep neural networks method and R-CNN object detection model. At a x40 objective our method was able to accurately detect stomata (n = 540, r = 0.94, p<0.0001), with an overall similarity of 99% between human and automated counting methods. Our method can batch process large files of images. As proof of concept, characterised the stomatal density in a population of 155 O. glaberrima accessions, using 13,100 micrographs. Here, we present developed Stomata Detector; an open source, sophisticated piece of software for the plant science community that can accurately identify stomata in Oryza spp., and potentially other monocot species.. CC-BY-NC-ND 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 3, 2021. ; https://doi.

Stomata are pores in the epidermal tissue of leaf plants formed by specialised cells called guard cells, which regulate the stomatal opening. Stomata facilitate gas exchange, being pivotal in the regulation of processes such as pho-tosynthesis and transpiration. The analysis of the number and behaviour of stomata is a task carried out by studying microscopic images; and, nowadays, this task is mainly conducted manually, or using programs that can count and determine the position of stomata but are not able to determine their morphology. In this paper, we have conducted a study of 10 deep learning algorithms to segment stom-ata from several species. The model that achieves the best Dice score, with a value of 96.06%, is obtained with the DeepLabV3+ algorithm, whereas the model that provides the best trade-off between inference time and Dice score was trained using the ContextNet architecture. This is a first step towards improving the measurements provided by stomata analysis tools, that will in turn help plant biologists to advance their understanding of dynamics in plants.

Stomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. The stomata states (open and close) play a significant role in describing the plant's health. Moreover, stomata counting is important for scientists to investigate the numbers of stomata that are open and those that are closed to measure their density and distribution on the surface of leaves through different sampling techniques. Although a few techniques for stomata counting have been proposed, these approaches do not identify and classify the stomata based on their states in leaves. In this research, we have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning (neural network model Single Shot Detector) approach. In leaf imprint, the state of stomata has been determined by measuring the correlation between the area of stomata and the aperture of each detected stoma in the image. The stomata states have been classified through the Support Vector Machine (SVM) algorithm. The overall identification and classification accuracy of the proposed system are 98.6% and 97%, respectively, helping researchers to obtain accurate stomatal state information for leaves in an efficient and simple way.

Digital image processing is commonly used in plant health and growth analysis, aiming to improve research efficiency and repeatability. One focus is analysing the morphology of stomata, with the aim to better understand the regulation of gas exchange, its link to photosynthesis and water use and how they are influenced by climatic conditions. Despite the key role played by these cells, their microscopic analysis is largely manual, requiring intricate sample collection, laborious microscope application and the manual operation of a graphical user interface to identify and measure stomata. This research proposes a simple, end-to-end solution which enables automatic analysis of stomata by introducing key changes to imaging techniques, stomata detection as well as stomatal pore area calculation. An optimal procedure was developed for sample collection and imaging by investigating the suitability of using an automatic microscope slide scanner to image nail polish imprints. The use of the slide scanner allows the rapid collection of high-quality images from entire samples with minimal manual effort. A convolutional neural network was used to automatically detect stomata in the input image, achieving average precision, recall and F-score values of 0.79, 0.85, and 0.82 across four plant species. A novel binary segmentation and stomatal cross section analysis method is developed to estimate the pore boundary and calculate the associated area. The pore estimation algorithm correctly identifies stomata pores 73.72% of the time. Ultimately, this research presents a fast and simplified method of stomatal assay generation requiring minimal human intervention, enhancing the speed of acquiring plant health information.

Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.

Plant leaf stomata are the gatekeepers of the atmosphere–plant interface and are essential for transpiration and photosynthesis... (truncated for brevity)

Stomata are pores in the epidermal tissue of leaf plants formed by specialised cells called guard cells, which regulate the stomatal opening. Stomata facilitate gas exchange, being pivotal in the regulation of processes such as photosynthesis and transpiration. The analysis of the number and behaviour of stomata is a task carried out by studying microscopic images, and that can serve, among other things, to better manage crops in agriculture or to better understand how plants fix CO2 and lose water under different conditions. However, quantifying the number of stomata in an image traditionally has been a labor intensive and thus expensive process since an image might contain dozens of stomata. Several automatic stomata detection models have been developed and presented in the literature, but they fail to generalise to images from species different to those employed to train the model; and, in addition, they lack a simple interface to employ them. In this work, we tackle these problems by training a YOLO model. Such a model achieves a F1-score of 0.91 in images from the species employed for training it, and similar F1-score for datasets containing images of different species. Moreover, in order to facilitate the use of the model, we have developed LabelStoma, an open-source and simple-to-use graphical user interface that employs the YOLO model. In addition, this tool provides a simple method to adapt the YOLO model to the users’ images, and, therefore, customising the model to the users’ needs. Thanks to this work, the analysis of plant stomata of different species will be more reliable and comparable; and, the developed tools will help to advance our understanding of CO2 and H2O dynamics in plants, such as photosynthesis and transpiration, and ecosystems related processes, such as carbon and water cycles.

There are so many types of herbal plants that come from the same genus. The similarity of features in herbal plants makes it difficult to distinguish. Especially in the pharmaceutical field, which is very risky for making mistakes. The part of plants that is often used as ingredients for herbal medicines is the leaves, so research on leaves and their constituent organelles is very important for the pharmaceutical world. Therefore, an approach is needed to identify the types of organelles present in the leaves, where the organelles most frequently studied are the stomata. So, a neural network approach is needed to distinguish plant characteristics in the same genus. In this research there are 2 species of plants in the Curcuma genus, namely turmeric and ginger. This research was conducted by implementing Deep Learning using Convolutional Neural Network (CNN) with the help of the Gabor filter process and feature extraction using the Gray Level Co-occurrence Matrix (GLCM). The study was conducted using 160 microscopic images of Curcuma herbal plants as training data with training accuracy of 93.1% and test data of 40 images with an accuracy of 92.5%.