請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3976
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 周楚洋(Chu-Yang Chou) | |
dc.contributor.author | Tzu-Yi Kuo | en |
dc.contributor.author | 郭子毅 | zh_TW |
dc.date.accessioned | 2021-05-13T08:39:35Z | - |
dc.date.available | 2021-03-08 | |
dc.date.available | 2021-05-13T08:39:35Z | - |
dc.date.copyright | 2016-03-08 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-02-14 | |
dc.identifier.citation | Arlot, S., Celisse, A., 2010. A survey of cross-validation procedures for model selection. Statistics surveys 4, 40-79.
Bay, H., Tuytelaars, T., Van Gool, L., 2006. Surf: Speeded up robust features, Computer vision–ECCV 2006. Springer, pp. 404-417. Becerra, V., Paredes, M., Gutierrez, E., Rojo, C., 2015. Genetic diversity, identification, and certification of Chilean rice varieties using molecular markers. Chilean journal of agricultural research 75, 267-274. Bradbury, P.J., Zhang, Z., Kroon, D.E., Casstevens, T.M., Ramdoss, Y., Buckler, E.S., 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633-2635. Bush, W.S., Moore, J.H., 2012. Chapter 11: Genome-wide association studies. PLoS Comput Biol 8, e1002822. Camelo-Mendez, G.A., Camacho-Diaz, B.H., del Villar-Martinez, A.A., Arenas-Ocampo, M.L., Bello-Perez, L.A., Jimenez-Aparicio, A.R., 2012. Digital image analysis of diverse Mexican rice Varieties . J Sci Food Agric 92, 2709-2714. Chen, H., Xie, W., He, H., Yu, H., Chen, W., Li, J., Yu, R., Yao, Y., Zhang, W., He, Y., 2014. A high-density SNP genotyping array for rice biology and molecular breeding. Molecular plant 7, 541-553. Chuang, H.-Y., Lur, H., Hwu, K., Chang, M., 2011. Authentication of domestic Taiwan rice varieties based on fingerprinting analysis of microsatellite DNA markers. Botanical Studies 52, 393-405. Cirillo, A., Del Gaudio, S., Di Bernardo, G., Galderisi, U., Cascino, A., Cipollaro, M., 2009. Molecular characterization of Italian rice Varieties . European Food Research and Technology 228, 875-881. Costa, C., Menesatti, P., Paglia, G., Pallottino, F., Aguzzi, J., Rimatori, V., Russo, G., Recupero, S., Recupero, G.R., 2009. Quantitative evaluation of Tarocco sweet orange fruit shape using optoelectronic elliptic Fourier based analysis. Postharvest biology and Technology 54, 38-47. Dillencourt, M.B., Samet, H., Tamminen, M., 1992. A general approach to connected- component labeling for arbitrary image representations. Journal of the ACM 39, 253-280. Galloway, M.M., 1975. Texture analysis using gray level run lengths. Computer graphics and image processing 4, 172-179. Garris, A.J., Tai, T.H., Coburn, J., Kresovich, S., McCOUCH, S., 2005. Genetic structure and diversity in Oryza sativa L. Genetics 169, 1631-1638. Gonzalez, R.C., Woods, R.E., 2007. Digital image processing 3rd edition. Prentice Hall. Haralick, R.M., Shanmugam, K., Dinstein, I.H., 1973. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions (6), 610-621. Harris, F.J., 1978. On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE 66, 51-83. Hartigan, J.A., Wong, M.A., 1979. Algorithm AS 136: A k-means clustering algorithm. Applied statistics 28 (1), 100-108. Huang, Kurata, N., Wei, X., Wang, Z.-X., Wang, A., Zhao, Q., Zhao, Y., Liu, K., Lu, H., Li, W., 2012. A map of rice genome variation reveals the origin of cultivated rice. Nature 41 490, 497-501. Huang, X., Wei, X., Sang, T., Zhao, Q., Feng, Q., Zhao, Y., Li, C., Zhu, C., Lu, T., Zhang, Z., 2010. Genome-wide association studies of 14 agronomic traits in rice landraces. Nature genetics 42, 961-967. Huang, X., Zhao, Y., Wei, X., Li, C., Wang, A., Zhao, Q., Li, W., Guo, Y., Deng, L., Zhu, C., 2012. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nature genetics 44, 32-39. Hunter, A., 1975. The loss of community: An empirical test through replication. American Sociological Review 40 (5), 537-552. Jin, H.-y., Yang, X.-h., Jiao, L.-c., Liu, F., 2005. Image enhancement via fusion based on laplacian pyramid directional filter banks, Image Analysis and Recognition. Springer, pp. 239-246. Kong, W., Zhang, C., Liu, F., Nie, P., He, Y., 2013. Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors 13, 8916- 8927. Kovach, M.J., Sweeney, M.T., McCouch, S.R., 2007. New insights into the history of rice domestication. TRENDS in Genetics 23, 578-587. Liu, M., Zhang, D., Shen, D., Initiative, The Alzheimer's Disease Neuroimaging Initiative, 2012. Ensemble sparse classification of Alzheimer's disease. NeuroImage 60, 1106-1116. Majumdar, S., Jayas, D., 2000. Classification of cereal grains using machine vision: IV. Combined morphology, color, and texture models. Transactions of the ASAE 43, 1689- 1694. Mebatsion, H., Paliwal, J., Jayas, D., 2013. Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Computers and Electronics in Agriculture 90, 99-105. Muja, M., Lowe, D.G., 2009. Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. VISAPP (1) 2. Pazoki, A., Farokhi, F., Pazoki, Z., 2014. Classification of rice grain varieties using two Artificial Neural Networks (MLP and Neuro-Fuzzy). Journal of Animal and Plant Sciences 24, 336-343. Rohlf, F.J., Archie, J.W., 1984. A comparison of Fourier methods for the description of wing shape in mosquitoes (Diptera: Culicidae). Systematic Biology 33, 302-317. Steele, K.A., Ogden, R., McEwing, R., Briggs, H., Gorham, J., 2008. InDel markers distinguish Basmatis from other fragrant rice varieties. Field Crops Research 105, 81-87. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y., 2010. Locality-constrained linear coding for image classification, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, San Francisco, CA pp. 3360-3367. Wang, W., Chang, F., 2011. A multi-focus image fusion method based on Laplacian pyramid. Journal of Computers 6, 2559-2566. Wei, C.-P., Chao, Y.-W., Yeh, Y.-R., Wang, Y.-C.F., 2013. Locality-sensitive dictionary learning for sparse representation based classification. Pattern Recognition 46, 1277-1287. Xie, C., Zhang, J., Li, R., Li, J., Hong, P., Xia, J., Chen, P., 2015. Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning. Computers and Electronics in Agriculture 119, 123-132. Yuan, X.-T., Liu, X., Yan, S., 2012. Visual classification with multitask joint sparse representation. Image Processing, IEEE Transactions on 21, 4349-4360. Zhao, K., Tung, C.-W., Eizenga, G.C., Wright, M.H., Ali, M.L., Price, A.H., Norton, G.J., Islam, M.R., Reynolds, A., Mezey, J., 2011. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature communications 2, 467. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3976 | - |
dc.description.abstract | 水稻是全世界許多人的主食,每年在國際市場上交易的數量十分龐大。不同的品種 的水稻在外觀上存在著差異,這些外觀差異可以藉由分析其與水稻基因型的關聯 來了解造成外觀差異的原因。本研究利用影像處理以及稀疏表達分類器等非破壞 性檢測分辨 30 種不同的水稻,同時也對於 255 種水稻的外觀以及基因型做出其關 聯性的分析。稀疏表達分類器則可以利用過度充分基底來捕捉具有代表性的外觀 特徵。在實驗中,種子取自於 Genetic Stocks Oryza,基因資訊取自於公開資料庫, 利用顯微鏡與高解析度數位相機提高影像畫質。量化的外觀特徵大致上被分為水 稻種子以及護穎的形態、顏色以及紋理等特徵。接下來利用線性模型對上述特徵分 析其與基因型的關係。稀疏表達分類器藉著輸入量化的外觀特徵來辨識其中 30 種 水稻品種,稀疏表達分類器對於 30 種品種的辨識準確率可達到 89.1%。 | zh_TW |
dc.description.abstract | Rice (Oryza sativa L.) is a major staple food and is traded globally in considerable amount. Rice shows remarkable variation in grains. The phenotypic information of the rice grains need to be quantified as the first step to investigate the association between the phenotypes and genotypes. This study proposed to distinguish the rice grains of 30 varieties nondestructively using image processing, sparse representation based classification (SRC) and a procedure to phenotype rice grains of 255 varieties in high precision. SRC is a method that uses over-complete bases to capture the representative traits of rice grains. In the experiments, rice seeds were acquired from Genetic Stocks Oryza germplasm collection. The genotypic information (i.e., SNPs) of these seeds are publicly available. The images of the grains were acquired in high resolution using microscopy (approximately 2413 dots per inch). Morphological, color, and textural traits of the grain body, sterile lemmas, and brush were quantified. The traits were subsequently fit into a unified mixed linear model for investigating the association between the phenotypic and genotypic variations of the varieties. An SRC classifier was developed to identify the varieties of the grains using the traits as the inputs. The proposed approach could discriminate the varieties of the rice grains with an accuracy of 89.1%. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T08:39:35Z (GMT). No. of bitstreams: 1 ntu-105-R02631039-1.pdf: 3502642 bytes, checksum: 20570e36495bd9c91566ed55e56b0cac (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | ACKNOWLEDGEMENTS ............................................................................................... i
摘要................................................................................................................................... ii ABSTRACT ..................................................................................................................... iii TABLE OF CONTENTS................................................................................................. iv LIST OF FIGURES ........................................................................................................vii CHAPTER 1. INTRODUCTION .............................................................................. 11 1.1 Rice Phylogeny ................................................................................................. 11 1.2 Genome Architecture ........................................................................................ 11 1.3 Objectives ......................................................................................................... 12 1.4 Organization...................................................................................................... 12 CHAPTER 2. LITERATURE REVIEW ................................................................... 13 2.1 Genetic Marker-based Methods to Identify Varieties of Rice Grains .............. 13 2.2 Nondestructive Method to Identify Varieties of Rice Grains ........................... 13 2.3 Sparse Representation Based Classifier to Identify Divergent Objects............ 14 2.4 Genome-wide Association Studies on Rice Grains .......................................... 14 CHAPTER 3. IDENTIFYING RICE GRAINS OF VARIOUS VARIETIES .......... 15 3.1 Material and Methods ....................................................................................... 15 3.1.1 Grain sample preparation .................................................................................. 15 3.1.2 Rice grain exterior ............................................................................................. 15 3.1.3 Imaging system and image acquisition..............................................................16 3.1.4 Multi-focus image fusion and background removal..........................................16 3.1.5 Trait quantification ............................................................................................ 17 3.1.6 Variations in grain shape...................................................................................20 3.1.7 Variety identification.........................................................................................20 3.2 Results............................................................................................................... 22 3.2.1 Image pre-processing.........................................................................................22 3.2.2 Illustration of grain shape variation...................................................................23 3.2.3 Grain color discrepancies .................................................................................. 24 3.2.4 Classification performance................................................................................26 3.3 Concluding Remarks......................................................................................... 27 CHAPTER 4. STUDYING THE GENOTYPE-PHENOTYPE ASSOCIATION OF RICE GRAINS ..................................................................................................... 28 4.1 Material and Methods .................................................................................... 28 4.1.1 Genotyping ..................................................................................................... 28 4.1.2 Genome-wide association study ..................................................................... 28 4.2 Results............................................................................................................ 29 4.2.1 Statistical models optimization .........................................................................29 4.3 Concluding Remarks......................................................................................... 38 CHAPTER 5. CONCLUSION................................................................................... 39 REFERENCES ............................................................................................................... 40 APPENDIX 1.................................................................................................................. 45 APPENDIX 2.........................................................................................47 | |
dc.language.iso | en | |
dc.title | 辨識不同種類之稻米以及研究其基因型與表現型之關聯 | zh_TW |
dc.title | Identifying Rice Grains of Various Varieties and Studying the Genotype-Phenotype Association of Rice Grains | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 郭彥甫(Yan-Fu Kuo) | |
dc.contributor.oralexamcommittee | 林達德(Ta-Te Lin),鍾嘉綾(Chia-Lin Chung) | |
dc.subject.keyword | 水稻辨識,稀疏編碼,影像處理,機器視覺,數量性狀基因座, | zh_TW |
dc.subject.keyword | Variety identification,sparse coding,locality constraint,machine vision,machine learning,image processing,phenotyping, | en |
dc.relation.page | 47 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2016-02-15 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
顯示於系所單位: | 生物機電工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-105-1.pdf | 3.42 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。