Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection

Sakoda, Kazuma; Watanabe, Tomoya; Sukemura, Shun; Kobayashi, Shunzo; Nagasaki, Yuichi; Tanaka, Yu; Shiraiwa, Tatsuhiko

doi:10.1038/s41598-019-44127-0

Cited by 39 publications

(39 citation statements)

References 24 publications

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“…Meanwhile, Fetter et al (2019) have utilized a convolutional neural network (CNN), a deep learning architecture, to identify stomata from a variety of microscopic images taken from various plant species. Other deep learning models, e.g., YOLO, SSD, and Mask R-CNN, have been proposed as useful adjuncts in stomata detection and trait measurement ( Sakoda et al, 2019 ; Casado-García et al, 2020 ; Jayakody et al, 2021 ). As exemplified by those studies, deep learning has been demonstrated to be efficacious in the quantification of stomatal traits.…”

Section: Introductionmentioning

confidence: 99%

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

Toda

Tameshige

Tomiyama³

et al. 2021

Front. Plant Sci.

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Recent technical advances in the computer-vision domain have facilitated the development of various methods for achieving image-based quantification of stomata-related traits. However, the installation cost of such a system and the difficulties of operating it on-site have been hurdles for experimental biologists. Here, we present a platform that allows real-time stomata detection during microscopic observation. The proposed system consists of a deep neural network model-based stomata detector and an upright microscope connected to a USB camera and a graphics processing unit (GPU)-supported single-board computer. All the hardware components are commercially available at common electronic commerce stores at a reasonable price. Moreover, the machine-learning model is prepared based on freely available cloud services. This approach allows users to set up a phenotyping platform at low cost. As a proof of concept, we trained our model to detect dumbbell-shaped stomata from wheat leaf imprints. Using this platform, we collected a comprehensive range of stomatal phenotypes from wheat leaves. We confirmed notable differences in stomatal density (SD) between adaxial and abaxial surfaces and in stomatal size (SS) between wheat-related species of different ploidy. Utilizing such a platform is expected to accelerate research that involves all aspects of stomata phenotyping.

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Section: Introductionmentioning

confidence: 99%

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

Toda

Tameshige

Tomiyama³

et al. 2021

Front. Plant Sci.

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“…Stomatal pore area measurements require the identification of stomata in a microscope image ( Dow et al, 2014 ); with a small number of stomata, this can be achieved using manual image analysis tools. More recently, higher order image processing and machine learning has been used to automate this process ( Laga et al, 2014 ; Liu et al, 2016 ; Jayakody et al, 2017 ; Toda et al, 2018 ; Fetter et al, 2019 ; Sakoda et al, 2019 ). In this work, a CNN ( Lecun et al, 1998 ) based on the MATLAB® implementation of the AlexNet ( Krizhevsky et al, 2012 ) network was used to identify stomata ( MathWorks, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…Whether it be through detecting the unique fluorescence emission of stomatal guard cells under UV excitation ( Karabourniotis et al, 2001 ), through rhodamine 6G staining ( Eisele et al, 2016 ), or through template matching ( Laga et al, 2014 ), it has ultimately been the automatic measurement, not detection, of stomatal pores in large samples that has proven most difficult. More recent research ( Jayakody et al, 2017 ; Toda et al, 2018 ; Fetter et al, 2019 ; Sakoda et al, 2019 ) utilizes machine learning and image processing to detect stomata (and sometimes classify the state of the stomata) in microscope images. The accuracy levels achieved in these studies shows promise and enables plant scientists to conduct high-throughput analysis for stomata detection.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Automated Stomata Analysis Through Simplified Sample Collection and Imaging Techniques

et al. 2020

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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.

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“…A crystal-clear tape was used as the desired area of the leaf surface and drawn off carefully. A 1 mm 2 grid was superimposed on the images for the calculation of stomata density [31]. Some example of captured imprint is shown in Figure 1.…”

Section: Datasetmentioning

confidence: 99%

Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning

et al. 2021

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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.

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Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection

Cited by 39 publications

References 24 publications

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

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

Accelerating Automated Stomata Analysis Through Simplified Sample Collection and Imaging Techniques

Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning

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