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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李承叡 | zh_TW |
| dc.contributor.advisor | Cheng-Ruei Lee | en |
| dc.contributor.author | 高瑄蔚 | zh_TW |
| dc.contributor.author | Hsuan-Wei Kao | en |
| dc.date.accessioned | 2025-08-20T16:13:26Z | - |
| dc.date.available | 2025-08-21 | - |
| dc.date.copyright | 2025-08-20 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-13 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98903 | - |
| dc.description.abstract | 本研究旨在結合植物果實影像與基因型資料，建立一套能解釋形態與遺傳關係的分析流程。為此，我們開發一個基於自編碼器架構的深度學習模型，用以自動萃取茄子（Solanum melongena）果實影像的latent representation，並進一步評估其與基因型的對應關係。模型訓練採用來自亞洲蔬菜中心共1,609張果實影像資料，透過一種稱為 Variational Sparse Coding（VSC）的方法，使所學得的特徵具備稀疏性與可解釋性。們對這些潛在特徵進行視覺化與統計分析，並將其作為性狀輸入，結合 GEMMA 工具進行全基因體關聯分析（GWAS），以鑑定與影像形態特徵相關的單核苷酸多態性（SNP）。
此外，我們比較了不同的特徵聚合方式（平均值、最大值、最小值）及其主成分分析（PCA）後的效果，探討表徵方式對後續分析結果的影響。最後，我們以多層感知器（MLP）模型，根據顯著 SNP 資訊預測影像所對應的latent representation，展示從基因型重建表型特徵的可行性與潛力。整體而言，本研究建立一套由影像特徵學習、基因型關聯分析到性狀預測的整合流程，為深入理解植物的基因型與表型關係提供一項具體而有效的技術架構。 | zh_TW |
| dc.description.abstract | This study aims to integrate plant fruit images and genotype data to establish an analytical framework for understanding the relationship between morphological traits and genetic variation. To this end, we developed a deep learning model based on an autoencoder architecture to automatically extract latent representations from fruit images of eggplant (Solanum melongena) and assess their correspondence with genotypic information. The model was trained on 1,609 fruit images provided by the World Vegetable Center, using a method known as Variational Sparse Coding (VSC) to obtain sparse and interpretable features.
We performed visualization and statistical analysis of these latent features and treated them as phenotypic traits for genome-wide association studies (GWAS) using the GEMMA tool, identifying single nucleotide polymorphisms (SNPs) associated with image-based morphological characteristics. In addition, we compared different feature aggregation methods (mean, maximum, minimum) and their principal component analysis (PCA) results to evaluate how representation strategies affect downstream analyses. Finally, we employed a multilayer perceptron (MLP) model to predict latent representations from significant SNPs, demonstrating the feasibility and potential of reconstructing phenotypic features from genotype data. Overall, this study establishes an integrated workflow from image-based feature learning to genotype-to-trait association and phenotype prediction, providing a practical and effective framework for understanding the genotype–phenotype relationship in plants. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-20T16:13:26Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-20T16:13:26Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Contents
致謝 I 摘要 II Abstract III Contents V List of Figures VIII List of Tables XI 1. Introduction 1 2. Materials and Methods 6 2.1 Image Data Collection 6 2.1.1 Plant Materials 6 2.1.2 Image Acquisition and Preprocessing 7 2.2 Model Training and Latent Representation 9 2.2.1 Model Architecture, Implementation and Augmentation 9 2.2.2 Grid Search of Latent Parameters 12 2.2.3 Full Model Training and Latent Code Extraction 13 2.2.4 Latent Code Extraction and Visual Evaluation of Reconstruction Quality 14 2.2.5 Latent Feature Extraction and Visualization 15 2.3 Genomic Data Processing and Trait Preparation 16 2.3.1 Genomic data source and filtering 16 2.3.2 Genomic PCA and population structure 17 2.3.3 Latent code extraction and PCA-based traits 17 2.4 Genome-Wide Association Analysis and Trait Reconstruction 18 2.4.1 GWAS Analysis 18 2.4.2 Trait–Genotype Association Pre-screening and Polygenicity Evaluation 19 2.4.3 Reconstructing Phenotypes from Genotype-Trait Associations 21 2.5 Genomic Prediction of Latent Traits 22 2.5.1 Genotype Data Processing for Prediction 22 2.5.2 MLP Model Architecture and Training 23 2.6 User Interface for Trait Query and Image Reconstruction 24 3. Results 25 3.1 Dataset Overview 25 3.2 Image Quality and Standardization 25 3.3 Model Design and Reconstruction Ability 26 3.4 Model Optimization 26 3.5 Reconstruction Benchmark and Visual Evaluation 28 3.6 Latent Representations Interpretation 29 3.7 Trait–Genotype Association and Visualization 31 3.8 Trait Polygenicity and Genotype Association Patterns 33 3.9 Image-Based Reconstruction of SNP Effects 34 3.10 Genotype-Wise Trait Visualization via Box Plots 35 3.11 Genomic Prediction Performance Using MLP 37 4. Discussion 40 4.1 Image Data Quality Limitations 40 4.2 Interpretable and Disentangled Latent Representations 40 4.3 Trait Polygenicity and Genomic Associations 42 4.4 Visualizing the Effects of Genomic Variation 44 4.5 Trait Prediction from Genotype Using Neural Models 45 5. Conclusion 47 6. References 112 List of Figures Figure 1. Workflow of image preprocessing for eggplant morphological analysis. 50 Figure 2. Architecture of the variational sparse coding (VSC) model. 51 Figure 3. Reconstruction loss curves of the final VSC models trained using linear and log₂ resize strategies. 52 Figure 4. Visual evaluation of reconstruction and generation performance using VSC models.. 54 Figure 5. Morphological effects of individual latent dimensions on synthetic image reconstruction. 58 Figure 6. GWAS results based on linearly resized morphological features of eggplant fruit. 59 Figure 7. Prediction performance of ridge regression across traits using increasing numbers of SNPs. 60 Figure 8. Predicted eggplant morphologies based on SNP genotypes using two latent trait reconstruction strategies. 66 Figure 9. Boxplots of trait values and representative images for each genotype group. 75 Figure 10. Performance of MLP model in predicting latent traits from genotype data. 77 Figure 11. Correlation between SNP-based heritability (PVE) and prediction accuracy across latent representations. 78 Supplementary Figure 1. Geographic distribution of Solanum melongena accessions used in this study. 79 Supplementary Figure 2. Distribution of eggplant image sizes before and after resizing. 80 Supplementary Figure 3. Hyperparameter tuning results under the linear resize setting. 81 Supplementary Figure 4. Hyperparameter tuning results under the log₂ resize setting. 83 Supplementary Figure 5. Visualization of latent space structure in VSC models trained with linear and log₂ resizing. 86 Supplementary Figure 6. Pairwise scatter plots of latent codes and principal components. 91 Supplementary Figure 7. Genomic PCA and genotype distribution of representative GWAS SNPs. 92 Supplementary Figure 8. GWAS results based on log2 resized morphological features of eggplant fruit. 97 Supplementary Figure 9. GWAS results using side length traits derived from linearly resized eggplant images. 98 Supplementary Figure 10. GWAS results using side length traits derived from log2 resized eggplant images. 99 Supplementary Figure 11. Comparison of three polygenic scoring methods using different numbers of top SNPs. 103 Supplementary Figure 12. Prediction performance of the MLP model across latent representations.. 104 List of Tables Supplemental Table 1. Detailed model architecture with mapping to the schematic in Figure 2. 105 Supplemental Table 2. Number of Solanum melongena accessions collected per country. 106 Supplemental Table 3. GWAS summary for image-derived traits from linearly resized eggplant images 107 Supplemental Table 4. GWAS summary for image-derived traits from log2 resized eggplant images. 108 Supplemental Table 5. GWAS summary for side length traits from linearly resized eggplant images. 110 Supplemental Table 6. GWAS summary for side length traits from log2 resized eggplant images. 111 | - |
| dc.language.iso | en | - |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 茄子 | zh_TW |
| dc.subject | Variational Sparse Coding | zh_TW |
| dc.subject | 植物表型 | zh_TW |
| dc.subject | 全基因體關聯分析 | zh_TW |
| dc.subject | GWAS | en |
| dc.subject | Plant Phenotyping | en |
| dc.subject | Variational Sparse Coding | en |
| dc.subject | Deep Learning | en |
| dc.subject | Eggplant | en |
| dc.title | 以神經網路資訊解析茄子基因型與果實形態特徵間的關聯 | zh_TW |
| dc.title | Unveiling the Relationships between Genotype and Fruit Morphology in Eggplant via Latent Representations of Neural Networks | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林耀正;劉力瑜;林士勛 | zh_TW |
| dc.contributor.oralexamcommittee | Yao-Cheng Lin;Li-yu Daisy Liu;Shih-Syun Lin | en |
| dc.subject.keyword | 茄子,深度學習,全基因體關聯分析,植物表型,Variational Sparse Coding, | zh_TW |
| dc.subject.keyword | Eggplant,Deep Learning,GWAS,Plant Phenotyping,Variational Sparse Coding, | en |
| dc.relation.page | 119 | - |
| dc.identifier.doi | 10.6342/NTU202503987 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-15 | - |
| dc.contributor.author-college | 生命科學院 | - |
| dc.contributor.author-dept | 植物科學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 植物科學研究所 | |
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