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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 董致韡(Chih-Wei Tung) | |
| dc.contributor.author | Julien Hennequart | en |
| dc.contributor.author | 朱立安 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:45:24Z | - |
| dc.date.copyright | 2022-09-02 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-29 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86260 | - |
| dc.description.abstract | none | zh_TW |
| dc.description.abstract | Bread wheat (Triticum aestivum) is one of the most important crops in the world and is grown in a wide range of environments. Wheat yields are mainly limited by the lack of precipitations in certain parts of the world. Stomatal density (SD) and stomatal area (SA) are important traits for gas exchanges between the plant and the atmosphere, and thus, for water-use efficiency. Therefore, understanding the genetic architecture of SD and SA is essential for the breeding of drought tolerant wheat. However, the ability to discover genetic loci controlling stomatal traits has been hindered by the low throughput manual phenotyping methods employed for measuring SD and SA. We used a deep learning method to automatically measure SD and SA on 133 bread wheat accessions. The automatic measurements of SD were compared with SD measured manually. The deep learning model was able to accurately detect stomata with a precision, recall and F1-score of 0.990, 0.982 and 0.986 respectively. A genome-wide association study (GWAS) identified 58 quantitative trait loci associated with SA as well as automatically and manually measured SD. QTL were consistently detected between manual and automatic phenotyping. Thus, the two methods can be used in conjunction in order to validate the detected loci. Our results demonstrate that deep learning can be used to investigate the diversity and genetic control of SD on large populations accurately. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:45:24Z (GMT). No. of bitstreams: 1 U0001-2708202220323300.pdf: 3895355 bytes, checksum: adb4870e95c01b55441aca43ed34d4ca (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Abstract I Table of Contents II Index of Figures and Tables IV List of abbreviations V Introduction 1 Materials and methods 4 Plant material, growing conditions and imprinting 4 Stomatal density 4 Microscope measurements 4 Predicted measurements 4 Image processing 5 SD predictions 5 Performance of SD predictions 6 GWAS analyses 7 SNP genotypes 7 Principal component analysis and Linkage disequilibrium analysis 7 Genome-wide association studies 8 Results 9 Performance of the deep learning method for SD measurement 9 Classical and deep learning methods evaluate natural variations in SD 11 Mask R-CNN allows for the measurement of SA 11 Highlights of GWAS 12 Discussions 16 The Mask R-CNN algorithm allows for accurate measurement of SD 16 Classical and deep learning methods uncover the natural variations in SD and SA 17 Identification of candidate genetic loci associated with SD and SA 19 References 24 Figures and Tables 32 | |
| dc.language.iso | en | |
| dc.subject | 全基因組關聯研究 | zh_TW |
| dc.subject | 氣孔區 | zh_TW |
| dc.subject | 氣孔密度 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 小麥 | zh_TW |
| dc.subject | stomatal area | en |
| dc.subject | wheat | en |
| dc.subject | genome-wide association study | en |
| dc.subject | deep learning | en |
| dc.subject | stomatal density | en |
| dc.title | Investigating the Genetic Architecture of Stomatal Density in Wheat using a Convolutional Neural Network and GWAS | zh_TW |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | Valérie Schurdi-Levraud(Valérie Schurdi-Levraud),蔡育彰(Yu-Chang Tsai),Sylvain Prigent(Sylvain Prigent),Pierre-Francois Bert(Pierre-Francois Bert) | |
| dc.subject.keyword | 氣孔密度,氣孔區,全基因組關聯研究,深度學習,小麥, | zh_TW |
| dc.subject.keyword | stomatal density,stomatal area,genome-wide association study,deep learning,wheat, | en |
| dc.relation.page | 47 | |
| dc.identifier.doi | 10.6342/NTU202202886 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-08-30 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-08-29 | - |
| 顯示於系所單位： | 農藝學系 | |
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