1
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Matsumoto S, Utsumi Y, Kozuka T, Iwamura M, Nakai T, Yamauchi D, Karahara I, Mineyuki Y, Hoshino M, Uesugi K, Kise K. CT image-based 3D inflorescence estimation of Chrysanthemum seticuspe. FRONTIERS IN PLANT SCIENCE 2024; 15:1374937. [PMID: 39135648 PMCID: PMC11317900 DOI: 10.3389/fpls.2024.1374937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 06/27/2024] [Indexed: 08/15/2024]
Abstract
To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.
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Affiliation(s)
- Soushi Matsumoto
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Yuzuko Utsumi
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Toshiaki Kozuka
- Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
| | - Masakazu Iwamura
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Tomonori Nakai
- Graduate School of Science, University of Hyogo, Himeji, Japan
| | | | | | | | - Masato Hoshino
- Scattering and Imaging Division, Japan Synchrotron Radiation Research Institute, Sayo, Japan
| | - Kentaro Uesugi
- Scattering and Imaging Division, Japan Synchrotron Radiation Research Institute, Sayo, Japan
| | - Koichi Kise
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
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2
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Guha PK, Magar ND, Kommana M, Barbadikar KM, Suneel B, Gokulan C, Lakshmi DV, Patel HK, Sonti RV, Sundaram RM, Madhav MS. Strong culm: a crucial trait for developing next-generation climate-resilient rice lines. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2024; 30:665-686. [PMID: 38737321 PMCID: PMC11087419 DOI: 10.1007/s12298-024-01445-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 03/24/2024] [Accepted: 03/27/2024] [Indexed: 05/14/2024]
Abstract
Lodging, a phenomenon characterized by the bending or breaking of rice plants, poses substantial constraints on productivity, particularly during the harvesting phase in regions susceptible to strong winds. The rice strong culm trait is influenced by the intricate interplay of genetic, physiological, epigenetic, and environmental factors. Stem architecture, encompassing morphological and anatomical attributes, alongside the composition of both structural and non-structural carbohydrates, emerges as a critical determinant of lodging resistance. The adaptive response of the rice culm to various biotic and abiotic environmental factors further modulates the propensity for lodging. Advancements in next-generation sequencing technologies have expedited the genetic dissection of lodging resistance, enabling the identification of pertinent genes, quantitative trait loci, and novel alleles. Concurrently, contemporary breeding strategies, ranging from biparental approaches to more sophisticated methods such as multi-parent-based breeding, gene pyramiding, genomic selection, genome-wide association studies, and haplotype-based breeding, offer perspectives on the genetic underpinnings of culm strength. This review comprehensively delves into physiological attributes, culm histology, epigenetic determinants, and gene expression profiles associated with lodging resistance, with a specialized focus on leveraging next-generation sequencing for candidate gene discovery.
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Affiliation(s)
- Pritam Kanti Guha
- Department of Biotechnology, ICAR-Indian Institute of Rice Research, Hyderabad, India
- Department of Microbiology, Yogi Vemana University., Y.S.R Kadapa, India
| | - Nakul D. Magar
- Department of Biotechnology, ICAR-Indian Institute of Rice Research, Hyderabad, India
| | - Madhavilatha Kommana
- Department of Biotechnology, ICAR-Indian Institute of Rice Research, Hyderabad, India
| | - Kalyani M. Barbadikar
- Department of Biotechnology, ICAR-Indian Institute of Rice Research, Hyderabad, India
| | - B. Suneel
- Department of Biotechnology, ICAR-Indian Institute of Rice Research, Hyderabad, India
| | - C. Gokulan
- Department of Biotechnology, CSIR-Center for Cellular and Molecular Biology, Hyderabad, India
| | - D. Vijay Lakshmi
- Department of Microbiology, Yogi Vemana University., Y.S.R Kadapa, India
| | - Hitendra Kumar Patel
- Department of Biotechnology, CSIR-Center for Cellular and Molecular Biology, Hyderabad, India
| | - Ramesh V. Sonti
- Department of Biotechnology, CSIR-Center for Cellular and Molecular Biology, Hyderabad, India
- International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - R. M. Sundaram
- Department of Biotechnology, ICAR-Indian Institute of Rice Research, Hyderabad, India
| | - Maganti Sheshu Madhav
- Department of Biotechnology, ICAR-Indian Institute of Rice Research, Hyderabad, India
- ICAR-Central Tobacco Research Institute, Rajahmundry, India
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3
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Zhang Y, Gu S, Du J, Huang G, Shi J, Lu X, Wang J, Yang W, Guo X, Zhao C. Plant microphenotype: from innovative imaging to computational analysis. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:802-818. [PMID: 38217351 PMCID: PMC10955502 DOI: 10.1111/pbi.14244] [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: 03/10/2023] [Revised: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 01/15/2024]
Abstract
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shenghao Gu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianjun Du
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guanmin Huang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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4
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Xue M, Huang S, Xu W, Xie T. Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging. FRONTIERS IN PLANT SCIENCE 2024; 15:1358360. [PMID: 38486848 PMCID: PMC10937343 DOI: 10.3389/fpls.2024.1358360] [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: 12/19/2023] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Introduction In contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors. Methods For automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species. Results These models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties. Discussion This research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species.
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Affiliation(s)
- Mengjia Xue
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Siyi Huang
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Wenting Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tianwu Xie
- Institute of Radiation Medicine, Fudan University, Shanghai, China
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5
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Waldner S, Wendelspiess E, Detampel P, Schlepütz CM, Huwyler J, Puchkov M. Advanced analysis of disintegrating pharmaceutical compacts using deep learning-based segmentation of time-resolved micro-tomography images. Heliyon 2024; 10:e26025. [PMID: 38384517 PMCID: PMC10878950 DOI: 10.1016/j.heliyon.2024.e26025] [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: 07/23/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/23/2024] Open
Abstract
The mechanism governing pharmaceutical tablet disintegration is far from fully understood. Despite the importance of controlling a formulation's disintegration process to maximize the active pharmaceutical ingredient's bioavailability and ensure predictable and consistent release profiles, the current understanding of the process is based on indirect or superficial measurements. Formulation science could, therefore, additionally deepen the understanding of the fundamental physical principles governing disintegration based on direct observations of the process. We aim to help bridge the gap by generating a series of time-resolved X-ray micro-computed tomography (μCT) images capturing volumetric images of a broad range of mini-tablet formulations undergoing disintegration. Automated image segmentation was a prerequisite to overcoming the challenges of analyzing multiple time series of heterogeneous tomographic images at high magnification. We devised and trained a convolutional neural network (CNN) based on the U-Net architecture for autonomous, rapid, and consistent image segmentation. We created our own μCT data reconstruction pipeline and parameterized it to deliver image quality optimal for our CNN-based segmentation. Our approach enabled us to visualize the internal microstructures of the tablets during disintegration and to extract parameters of disintegration kinetics from the time-resolved data. We determine by factor analysis the influence of the different formulation components on the disintegration process in terms of both qualitative and quantitative experimental responses. We relate our findings to known formulation component properties and established experimental results. Our direct imaging approach, enabled by deep learning-based image processing, delivers new insights into the disintegration mechanism of pharmaceutical tablets.
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Affiliation(s)
- Samuel Waldner
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Erwin Wendelspiess
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Pascal Detampel
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | | | - Jörg Huwyler
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Maxim Puchkov
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
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6
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Yang W, Feng H, Hu X, Song J, Guo J, Lu B. An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management. Methods Mol Biol 2024; 2787:3-38. [PMID: 38656479 DOI: 10.1007/978-1-0716-3778-4_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, the construction and outlook of crop phenotype databases are introduced and the need for global cooperation and data sharing is emphasized. High-throughput crop phenotyping significantly improves accuracy and efficiency compared to traditional measurements, making significant contributions to overcoming bottlenecks in the phenotyping field and advancing crop genetics.
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Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xiao Hu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jing Guo
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Bingjie Lu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
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7
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Li Y, Huang G, Lu X, Gu S, Zhang Y, Li D, Guo M, Zhang Y, Guo X. Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology. FRONTIERS IN PLANT SCIENCE 2023; 14:1219476. [PMID: 37900733 PMCID: PMC10613036 DOI: 10.3389/fpls.2023.1219476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/28/2023] [Indexed: 10/31/2023]
Abstract
Cotton (Gossypium hirsutum L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods. In recent years, the application of micro-CT technology has made it possible to analyze the three-dimensional morphological structure of seeds, and has shown technical advantages in accurate identification of seed phenotypes. In this study, we reconstructed the seed morphological structure based on micro-CT technology, deep neural network Unet-3D model, and threshold segmentation methods, extracted 11 basics phenotypes traits, and constructed three new phenotype traits of seed coat specific surface area, seed coat thickness ratio and seed density ratio, using 102 cotton germplasm resources with clear year characteristics. Our results show that there is a significant positive correlation (P< 0.001) between the cotton seed size and that of the seed kernel and seed coat volume, with correlation coefficients ranging from 0.51 to 0.92, while the cavity volume has a lower correlation with other phenotype indicators (r<0.37, P< 0.001). Comparison of changes in Chinese self-bred varieties showed that seed volume, seed surface area, seed coat volume, cavity volume and seed coat thickness increased by 11.39%, 10.10%, 18.67%, 115.76% and 7.95%, respectively, while seed kernel volume, seed kernel surface area and seed fullness decreased by 7.01%, 0.72% and 16.25%. Combining with the results of cluster analysis, during the hundred-year cultivation history of cotton in China, it showed that the specific surface area of seed structure decreased by 1.27%, the relative thickness of seed coat increased by 8.70%, and the compactness of seed structure increased by 50.17%. Furthermore, the new indicators developed based on micro-CT technology can fully consider the three-dimensional morphological structure and cross-sectional characteristics among the indicators and reflect technical advantages. In this study, we constructed a microscopic phenotype research system for cotton seeds, revealing the morphological changes of cotton seeds with the year in China and providing a theoretical basis for the quantitative analysis and evaluation of seed morphology.
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Affiliation(s)
- Yuankun Li
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Hebei Agricultural University, Baoding, China
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guanmin Huang
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xianju Lu
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shenghao Gu
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ying Zhang
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Dazhuang Li
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Minkun Guo
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Yongjiang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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8
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Tang Z, Chen Z, Gao Y, Xue R, Geng Z, Bu Q, Wang Y, Chen X, Jiang Y, Chen F, Yang W, Hu W. A Strategy for the Acquisition and Analysis of Image-Based Phenome in Rice during the Whole Growth Period. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0058. [PMID: 37304154 PMCID: PMC10249964 DOI: 10.34133/plantphenomics.0058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 05/23/2023] [Indexed: 06/13/2023]
Abstract
As one of the most widely grown crops in the world, rice is not only a staple food but also a source of calorie intake for more than half of the world's population, occupying an important position in China's agricultural production. Thus, determining the inner potential connections between the genetic mechanisms and phenotypes of rice using dynamic analyses with high-throughput, nondestructive, and accurate methods based on high-throughput crop phenotyping facilities associated with rice genetics and breeding research is of vital importance. In this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice. Up to 84.8% of the phenotypic variance of the rice yield could be explained by these i-traits. A total of 285 putative quantitative trait loci (QTLs) were detected for the i-traits, and principal components analysis was applied on the basis of the i-traits in the temporal and organ dimensions, in combination with a genome-wide association study that also isolated QTLs. Moreover, the differences among the different population structures and breeding regions of rice with regard to its phenotypic traits demonstrated good environmental adaptability, and the crop growth and development model also showed high inosculation in terms of the breeding-region latitude. In summary, the strategy developed here for the acquisition and analysis of image-based rice phenomes can provide a new approach and a different thinking direction for the extraction and analysis of crop phenotypes across the whole growth period and can thus be useful for future genetic improvements in rice.
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Affiliation(s)
- Zhixin Tang
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory,
Huazhong Agricultural University, Wuhan 430070, China
| | - Zhuo Chen
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
| | - Yuan Gao
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
| | - Ruxian Xue
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
| | - Zedong Geng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory,
Huazhong Agricultural University, Wuhan 430070, China
| | - Qingyun Bu
- Northeast Institute of Geography and Agroecology, Key Laboratory of Soybean Molecular Design Breeding,
Chinese Academy of Sciences, Harbin 150081, China
| | - Yanyan Wang
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaoqian Chen
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
| | - Yuqiang Jiang
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
| | - Fan Chen
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory,
Huazhong Agricultural University, Wuhan 430070, China
| | - Weijuan Hu
- Institute of Genetics and Developmental Biology,
Chinese Academy of Sciences, Beijing 100101, China
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9
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Yasmeen E, Wang J, Riaz M, Zhang L, Zuo K. Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants. PLANT COMMUNICATIONS 2023:100558. [PMID: 36760129 PMCID: PMC10363483 DOI: 10.1016/j.xplc.2023.100558] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
With the development of high-throughput biology techniques and artificial intelligence, it has become increasingly feasible to design and construct artificial biological parts, modules, circuits, and even whole systems. To overcome the limitations of native promoters in controlling gene expression, artificial promoter design aims to synthesize short, inducible, and conditionally controlled promoters to coordinate the expression of multiple genes in diverse plant metabolic and signaling pathways. Synthetic promoters are versatile and can drive gene expression accurately with smart responses; they show potential for enhancing desirable traits in crops, thereby improving crop yield, nutritional quality, and food security. This review first illustrates the importance of synthetic promoters, then introduces promoter architecture and thoroughly summarizes advances in synthetic promoter construction. Restrictions to the development of synthetic promoters and future applications of such promoters in synthetic plant biology and crop improvement are also discussed.
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Affiliation(s)
- Erum Yasmeen
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Riaz
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lida Zhang
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kaijing Zuo
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
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10
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Kinose R, Utsumi Y, Iwamura M, Kise K. Tiller estimation method using deep neural networks. FRONTIERS IN PLANT SCIENCE 2023; 13:1016507. [PMID: 36714728 PMCID: PMC9880423 DOI: 10.3389/fpls.2022.1016507] [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: 08/11/2022] [Accepted: 12/19/2022] [Indexed: 06/18/2023]
Abstract
This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so an automated approach is necessary for high-throughput phenotyping. Conventional methods use heuristic features to estimate the tiller number. Based on the successful application of DNNs in the field of computer vision, the use of DNN-based features instead of heuristic features is expected to improve the estimation accuracy. However, as DNNs generally require large volumes of data for training, it is difficult to apply them to estimation problems for which large training datasets are unavailable. In this paper, we use two strategies to overcome the problem of insufficient training data: the use of a pretrained DNN model and the use of pretext tasks for learning the feature representation. We extract features using the resulting DNNs and estimate the tiller numbers through a regression technique. We conducted experiments using side-view whole plant images taken with plan backgroud. The experimental results show that the proposed methods using a pretrained model and specific pretext tasks achieve better performance than the conventional method.
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Affiliation(s)
- Rikuya Kinose
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
| | - Yuzuko Utsumi
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Masakazu Iwamura
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Koichi Kise
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
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11
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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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12
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Yang L, Zhang P, Wang Y, Hu G, Guo W, Gu X, Pu L. Plant synthetic epigenomic engineering for crop improvement. SCIENCE CHINA. LIFE SCIENCES 2022; 65:2191-2204. [PMID: 35851940 DOI: 10.1007/s11427-021-2131-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Efforts have been directed to redesign crops with increased yield, stress adaptability, and nutritional value through synthetic biology-the application of engineering principles to biology. A recent expansion in our understanding of how epigenetic mechanisms regulate plant development and stress responses has unveiled a new set of resources that can be harnessed to develop improved crops, thus heralding the promise of "synthetic epigenetics." In this review, we summarize the latest advances in epigenetic regulation and highlight how innovative sequencing techniques, epigenetic editing, and deep learning-driven predictive tools can rapidly extend these insights. We also proposed the future directions of synthetic epigenetics for the development of engineered smart crops that can actively monitor and respond to internal and external cues throughout their life cycles.
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Affiliation(s)
- Liwen Yang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Pingxian Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yifan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Guihua Hu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Weijun Guo
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaofeng Gu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Li Pu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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13
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Wang C, Han B. Twenty years of rice genomics research: From sequencing and functional genomics to quantitative genomics. MOLECULAR PLANT 2022; 15:593-619. [PMID: 35331914 DOI: 10.1016/j.molp.2022.03.009] [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: 02/16/2022] [Revised: 03/04/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Since the completion of the rice genome sequencing project in 2005, we have entered the era of rice genomics, which is still in its ascendancy. Rice genomics studies can be classified into three stages: structural genomics, functional genomics, and quantitative genomics. Structural genomics refers primarily to genome sequencing for the construction of a complete map of rice genome sequence. This is fundamental for rice genetics and molecular biology research. Functional genomics aims to decode the functions of rice genes. Quantitative genomics is large-scale sequence- and statistics-based research to define the quantitative traits and genetic features of rice populations. Rice genomics has been a transformative influence on rice biological research and contributes significantly to rice breeding, making rice a good model plant for studying crop sciences.
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Affiliation(s)
- Changsheng Wang
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200233, China.
| | - Bin Han
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200233, China.
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14
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Wang W, Gao L, Cui X. A New Year's spotlight on two years of publication. PLANT COMMUNICATIONS 2022; 3:100274. [PMID: 35059635 PMCID: PMC8760135 DOI: 10.1016/j.xplc.2021.100274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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15
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Kong H, Chen P. Mask R-CNN-based feature extraction and three-dimensional recognition of rice panicle CT images. PLANT DIRECT 2021; 5:e00323. [PMID: 33981945 PMCID: PMC8110429 DOI: 10.1002/pld3.323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/26/2021] [Accepted: 04/01/2021] [Indexed: 05/03/2023]
Abstract
The rice panicle seed setting rate is extremely important for calculating rice yield and performing genetic analysis. Unlike machine vision, X-ray computed tomography (CT) imaging is a nondestructive technique that provides direct information on the internal and external structure of rice panicles. However, occlusion and adhesion of panicles and grains in a CT image sequence make these objects difficult to identify, which in turn hinders accurate determination of the seed setting rate of rice panicles. Therefore, this paper proposes a method based on a mask region convolutional neural network (Mask R-CNN) for feature extraction and three-dimensional (3-D) recognition of CT images of rice panicles. X-ray CT feature characterization was combined with the Mask R-CNN algorithm to perform feature extraction and classification of a panicle and grains in each layer of the CT sequence. The Euclidean distance between adjacent layers was minimized to extract the features of a 3-D panicle and grains. The results were used to calculate the rice panicle seed setting rate. The proposed method was experimentally verified using eight sets of different rice panicles. The results showed that the proposed method can efficiently identify and count plump grains and blighted grains to achieve an accuracy above 99% for the seed setting rate.
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Affiliation(s)
| | - Ping Chen
- North University of ChinaTaiyuanChina
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