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Yang X, Wang J, Li F, Zhou C, Wu M, Zheng C, Yang L, Li Z, Li Y, Guo S, Song C. RotatedStomataNet: a deep rotated object detection network for directional stomata phenotype analysis. PLANT CELL REPORTS 2024; 43:126. [PMID: 38652181 DOI: 10.1007/s00299-024-03149-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/02/2024] [Indexed: 04/25/2024]
Abstract
KEY MESSAGE Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).
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Affiliation(s)
- Xiaohui Yang
- Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, School of Mathematics and Statistics, Henan University, Kaifeng, 475004, Henan, China.
| | - Jiahui Wang
- Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, School of Mathematics and Statistics, Henan University, Kaifeng, 475004, Henan, China
| | - Fan Li
- School of Automation, Central South University, Changsha, 410000, Hunan, China
| | - Chenglong Zhou
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Wuxi, 214400, Jiangsu, China
| | - Minghui Wu
- Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, School of Mathematics and Statistics, Henan University, Kaifeng, 475004, Henan, China
| | - Chen Zheng
- Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, School of Mathematics and Statistics, Henan University, Kaifeng, 475004, Henan, China
| | - Lijun Yang
- Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, School of Mathematics and Statistics, Henan University, Kaifeng, 475004, Henan, China
| | - Zhi Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan University, Kaifeng, 475004, Henan, China
| | - Yong Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan University, Kaifeng, 475004, Henan, China
| | - Siyi Guo
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan University, Kaifeng, 475004, Henan, China
- The Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, Henan, China
| | - Chunpeng Song
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan University, Kaifeng, 475004, Henan, China
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2
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Pathoumthong P, Zhang Z, Roy SJ, El Habti A. Rapid non-destructive method to phenotype stomatal traits. PLANT METHODS 2023; 19:36. [PMID: 37004073 PMCID: PMC10064510 DOI: 10.1186/s13007-023-01016-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 03/23/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Stomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato. RESULTS The method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. CONCLUSIONS We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.
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Affiliation(s)
- Phetdalaphone Pathoumthong
- School of Agriculture, Food and Wine, The University of Adelaide, Urrbrae, 5064, Australia
- The Waite Research Institute, Urrbrae, 5064, Australia
| | - Zhen Zhang
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, 5000, Australia
| | - Stuart J Roy
- School of Agriculture, Food and Wine, The University of Adelaide, Urrbrae, 5064, Australia
- The Waite Research Institute, Urrbrae, 5064, Australia
- Australian Research Council Industrial Transformation Training Centre for Future Crops Development, The University of Adelaide, Urrbrae, 5064, Australia
| | - Abdeljalil El Habti
- School of Agriculture, Food and Wine, The University of Adelaide, Urrbrae, 5064, Australia.
- The Waite Research Institute, Urrbrae, 5064, Australia.
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3
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Yang M, He J, Sun Z, Li Q, Cai J, Zhou Q, Wollenweber B, Jiang D, Wang X. Drought priming mechanisms in wheat elucidated by in-situ determination of dynamic stomatal behavior. FRONTIERS IN PLANT SCIENCE 2023; 14:1138494. [PMID: 36875605 PMCID: PMC9983753 DOI: 10.3389/fpls.2023.1138494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Stomata play a critical role in balancing photosynthesis and transpiration, which are essential processes for plant growth, especially in response to abiotic stress. Drought priming has been shown to improve drought tolerance. Lots of studies have been done with the response of stomatal behavior to drought stress. However, how the stomatal dynamic movement in intact wheat plants response to drought priming process is not known. Here, a portable microscope was used to take microphotographs in order to in-stiu determination of stomatal behavior. Non-invasive micro-test technology was used for measurements of guard cell K+, H+ and Ca2+ fluxes. Surprisingly, the results found that primed plants close stomatal much faster under drought stress, and reopening the stomatal much quicker under recovery, in relation to non-primed plants. Compared with non-primed plants, primed plants showed higher accumulation of ABA and Ca2+ influx rate in guard cells under drought stress. Furthermore, genes encoding anion channels were higher expressed and K+ outward channels activated, leading to enhanced K+ efflux, resulting in faster stomatal closure in primed plants than non-primed plants. During recovery, both guard cell ABA and Ca2+ influx of primed plants were found to be significantly reducing K+ efflux and accelerating stomatal reopening. Collectively, a portable non-invasive stomatal observation of wheat found that priming promoted faster stomatal closure under drought stress and faster reopening during post-drought recovery in relation to non-primed plants, thereby enhancing overall drought tolerance.
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Affiliation(s)
- Mengxiang Yang
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
| | - Jiawei He
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
| | - Zhuangzhuang Sun
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
| | - Qing Li
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
| | - Jian Cai
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
| | - Qin Zhou
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
| | | | - Dong Jiang
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
| | - Xiao Wang
- Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, National Technique Innovation Center for Regional Wheat Production, Nanjing Agricultural University, Nanjing, China
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Antonello P, Morone D, Pirani E, Uguccioni M, Thelen M, Krause R, Pizzagalli DU. Tracking unlabeled cancer cells imaged with low resolution in wide migration chambers via U-NET class-1 probability (pseudofluorescence). J Biol Eng 2023; 17:5. [PMID: 36694208 PMCID: PMC9872392 DOI: 10.1186/s13036-022-00321-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/27/2022] [Indexed: 01/26/2023] Open
Abstract
Cell migration is a pivotal biological process, whose dysregulation is found in many diseases including inflammation and cancer. Advances in microscopy technologies allow now to study cell migration in vitro, within engineered microenvironments that resemble in vivo conditions. However, to capture an entire 3D migration chamber for extended periods of time and with high temporal resolution, images are generally acquired with low resolution, which poses a challenge for data analysis. Indeed, cell detection and tracking are hampered due to the large pixel size (i.e., cell diameter down to 2 pixels), the possible low signal-to-noise ratio, and distortions in the cell shape due to changes in the z-axis position. Although fluorescent staining can be used to facilitate cell detection, it may alter cell behavior and it may suffer from fluorescence loss over time (photobleaching).Here we describe a protocol that employs an established deep learning method (U-NET), to specifically convert transmitted light (TL) signal from unlabeled cells imaged with low resolution to a fluorescent-like signal (class 1 probability). We demonstrate its application to study cancer cell migration, obtaining a significant improvement in tracking accuracy, while not suffering from photobleaching. This is reflected in the possibility of tracking cells for three-fold longer periods of time. To facilitate the application of the protocol we provide WID-U, an open-source plugin for FIJI and Imaris imaging software, the training dataset used in this paper, and the code to train the network for custom experimental settings.
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Affiliation(s)
- Paola Antonello
- grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Faculty of Biomedical Sciences, Institute for Research in Biomedicine, CH-6500 Bellinzona, Switzerland ,grid.5734.50000 0001 0726 5157Graduate School of Cellular and Molecular Sciences, University of Bern, CH-3012 Bern, Switzerland
| | - Diego Morone
- grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Faculty of Biomedical Sciences, Institute for Research in Biomedicine, CH-6500 Bellinzona, Switzerland ,grid.5734.50000 0001 0726 5157Graduate School of Cellular and Molecular Sciences, University of Bern, CH-3012 Bern, Switzerland
| | - Edisa Pirani
- grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Faculty of Biomedical Sciences, Institute for Research in Biomedicine, CH-6500 Bellinzona, Switzerland
| | - Mariagrazia Uguccioni
- grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Faculty of Biomedical Sciences, Institute for Research in Biomedicine, CH-6500 Bellinzona, Switzerland
| | - Marcus Thelen
- grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Faculty of Biomedical Sciences, Institute for Research in Biomedicine, CH-6500 Bellinzona, Switzerland
| | - Rolf Krause
- grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Euler institute, CH-6962 Lugano-Viganello, Switzerland ,FernUni, Faculty of Mathematics and Informatics, Brig, Switzerland
| | - Diego Ulisse Pizzagalli
- grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Faculty of Biomedical Sciences, Institute for Research in Biomedicine, CH-6500 Bellinzona, Switzerland ,grid.29078.340000 0001 2203 2861Università della Svizzera italiana, Euler institute, CH-6962 Lugano-Viganello, Switzerland
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5
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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6
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Liang X, Xu X, Wang Z, He L, Zhang K, Liang B, Ye J, Shi J, Wu X, Dai M, Yang W. StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:577-591. [PMID: 34717024 PMCID: PMC8882810 DOI: 10.1111/pbi.13741] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/26/2021] [Accepted: 10/16/2021] [Indexed: 05/05/2023]
Abstract
To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two portable microscopes with different resolutions (TipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomata in maize leaves. FPN model was used to detect stomata in the TipScope images and measure the stomata number and stomatal density. Faster RCNN model was used to detect opening and closing stomata in the ProScope HR2 images, and the number of opening and closing stomata was measured. An improved CV model was used to segment pores of opening stomata, and a total of 6 pore traits were measured. Compared to manual measurements, the square of the correlation coefficient (R2 ) of the 6 pore traits was higher than 0.85, and the mean absolute percentage error (MAPE) of these traits was 0.02%-6.34%. The dynamic stomata changes between wild-type B73 and mutant Zmfab1a were explored under drought and re-watering condition. The results showed that Zmfab1a had a higher resilience than B73 on leaf stomata. In addition, the proposed method was tested to measure the leaf stomatal traits of other nine species. In conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed. An open-access and user-friendly web portal was also developed which has the potential to be used in the stomata phenotyping of large populations in the future.
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Affiliation(s)
- Xiuying Liang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Xichen Xu
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Zhiwei Wang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Lei He
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Kaiqi Zhang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Bo Liang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Junli Ye
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Xi Wu
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Mingqiu Dai
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)College of EngineeringHuazhong Agricultural UniversityWuhanChina
- Shenzhen BranchGuangdong Laboratory for Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of AgricultureAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
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7
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Toda Y, Tameshige T, Tomiyama M, Kinoshita T, Shimizu KK. An Affordable Image-Analysis Platform to Accelerate Stomatal Phenotyping During Microscopic Observation. FRONTIERS IN PLANT SCIENCE 2021; 12:715309. [PMID: 34394171 PMCID: PMC8358771 DOI: 10.3389/fpls.2021.715309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
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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Affiliation(s)
- Yosuke Toda
- Japan Science and Technology Agency, Saitama, Japan
- Phytometrics co., ltd., Shizuoka, Japan
- Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Nagoya, Japan
| | - Toshiaki Tameshige
- Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan
- Department of Biology, Faculty of Science, Niigata University, Niigata, Japan
| | | | - Toshinori Kinoshita
- Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Nagoya, Japan
| | - Kentaro K. Shimizu
- Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
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