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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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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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Mohd Saad NS, Neik TX, Thomas WJW, Amas JC, Cantila AY, Craig RJ, Edwards D, Batley J. Advancing designer crops for climate resilience through an integrated genomics approach. CURRENT OPINION IN PLANT BIOLOGY 2022; 67:102220. [PMID: 35489163 DOI: 10.1016/j.pbi.2022.102220] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.
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Affiliation(s)
- Nur Shuhadah Mohd Saad
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ting Xiang Neik
- Sunway College Kuala Lumpur, Bandar Sunway, 47500, Selangor, Malaysia
| | - William J W Thomas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Junrey C Amas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Aldrin Y Cantila
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ryan J Craig
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Jacqueline Batley
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia.
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Gibbs JA, Mcausland L, Robles-Zazueta CA, Murchie EH, Burgess AJ. A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation. FRONTIERS IN PLANT SCIENCE 2021; 12:780180. [PMID: 34925424 PMCID: PMC8675901 DOI: 10.3389/fpls.2021.780180] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 11/01/2021] [Indexed: 06/14/2023]
Abstract
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, g smax, 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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Affiliation(s)
- Jonathon A. Gibbs
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Lorna Mcausland
- School of Biosciences, University of Nottingham, Loughborough, United Kingdom
| | | | - Erik H. Murchie
- School of Biosciences, University of Nottingham, Loughborough, United Kingdom
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