LabelStoma: A tool for stomata detection based on the YOLO algorithm

Casado-García, Ángela; del-Canto, Arantza; Sanz‐Sáez, Álvaro; Pérez‐López, Usue; Bilbao-Kareaga, Amaia; Fritschi, Felix B.; Miranda-Apodaca, Jon; Muñoz‐Rueda, Alberto; Sillero-Martínez, Anna; Yoldi-Achalandabaso, Ander; Lacuesta, Maite; Heras, Jónathan

doi:10.1016/j.compag.2020.105751

Cited by 42 publications

(19 citation statements)

References 28 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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“…The stomata analysis serves as a basic example of instance segmentation. Despite several previous works on the automated examination of stomata (Toda et al, 2018;Fetter et al, 2019;Li et al, 2019;Carrasco et al, 2020;Casado-García et al, 2020;Meeus et al, 2020;Song et al, 2020), this contribution, to our knowledge, is the first trying to automatically segment whole stomata (represented by their guard cells) With the presented exemplary analyses, we hope to provide guidance for the application of GinJinn2 for automatic data collection and feature extraction. Despite GinJinn2's progress compared to its predecessor, there is still room for further improvements.…”

Section: Discussionmentioning

confidence: 99%

GinJinn2: Object detection and segmentation for ecology and evolution

Ott

Lautenschlager

2021

Preprint

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Proper collection and preparation of empirical data still represent one of the most important, but also expensive steps in ecological and evolutionary/systematic research. Modern machine learning approaches, however, have the potential to automate a variety of tasks, which until recently could only be performed manually. Unfortunately, the application of such methods by researchers outside the field is hampered by technical difficulties, some of which, we believe, can be avoided.Here, we present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data. Besides providing a convenient command-line interface to existing software libraries, it comprises several additional tools for data handling, pre- and postprocessing, and building advanced analysis pipelines.We demonstrate the application of GinJinn2 for biological purposes using four exemplary analyses, namely the evaluation of seed mixtures, detection of insects on glue traps, segmentation of stomata, and extraction of leaf silhouettes from herbarium specimens.GinJinn2 will enable users with a primary background in biology to apply deep learning-based methods for object detection and segmentation in order to automate feature extraction from image data.

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“… Meeus et al (2020) use VGG19 in which the number (19) corresponds to the number of layers. Casado-García et al (2020) use an object detection network known as YOLO ( Redmon and Farhadi, 2018 ), to detect the bounding boxes of stomata with accuracy of 91%. Whilst good results are reported for detecting stomata using the VGG and YOLO networks, a considerable amount of post-processing is required if morphological measurements are to be extracted, which is susceptible to error.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

et al. 2021

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Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.

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LabelStoma: A tool for stomata detection based on the YOLO algorithm

Cited by 42 publications

References 28 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

GinJinn2: Object detection and segmentation for ecology and evolution

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

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