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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳世芳(Shih-Feng Chen) | |
| dc.contributor.author | Hsien-Hua Lai | en |
| dc.contributor.author | 賴賢華 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:33:41Z | - |
| dc.date.copyright | 2021-08-18 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-06-30 | |
| dc.identifier.citation | Amirzadi, N., Shokoohi, E., Abolafia, J. (2013). Description of nine species of the family Cephalobidae (Nematoda, Rhabditida) and morphometric analysis in the genus Acrobeles Von Linstow, 1877. Acta Zool. Bulg., 65(1), 3-26. Arjovsky, M., Chintala, S., Bottou, L. (2017, August 6-11). Wasserstein generative adversarial networks. In International conference on machine learning (ICML), SYD, Australia (pp. 214-223). Asif, M., Prasad, J. S., Khan, R., Somasekhar, N., Tahseen, Q. (2013). A revision of the genus Metarhabditis (Nematoda: Rhabditidae) with description of three known species, a key to the identification of congeners and discussion of their relationships. J. Nat. Hist., 47, 41-42 2599-2622. Birchfield, W. (1965). Host parasite relations and host range studies of a new Meloidogyne species in southern USA. Phytopathology, 55(12), 1359-1361 Bird, D. M., Kaloshian, I. (2003). Are roots special? Nematodes have their say. Physiol. Mol. Plant Path., 62(2), 115-123. https://doi.org/10.1016/S0885-5765(03)00045-6. Boström, S., Holovachov, O. (2013). Description of one new species of Heterocephalobellus Rashid, Geraert Sharma, 1985 (Rhabditida: Cephalobidae) from Kelso Dunes, Mojave National Preserve, California, USA and Usno, Argentina. J. Nematode Morphol. Syst., 16(2), 161-166. Bucki, P., Qing, X., Castillo, P., Gamliel, A., Dobrinin, S., Alon, T., Braun Miyara, S. (2020). The Genus Pratylenchus (Nematoda: Pratylenchidae) in Israel: From Taxonomy to Control Practices. Plants, 9(11), 1475. Bulletin OEPP/EPPO Bulletin (2016). Meloidogyne enterolobii. 46(2). 190–201. https://doi.org/10.1111/epp.12293. Caccia, M., Lax, P., Doucet, M. (2012). Effect of entomopathogenic nematodes on the plant-parasitic nematode Nacobbus aberrans. Bio. Fertil. Soils, 49. https://doi.org/10.1007/s00374-012-0724-z. Carnegie, A. J., Venn, T., Lawson, S., Nagel, M., Wardlaw, T., Cameron, N., Last, I. (2018). 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Deep Learning for Nematode Detecion and Classification. Retrieved from https://basfproject-2018spring.github.io/Website/. Jiménez-Chavarría, J. (2019). SegNema: Nematode segmentation strategy in digital microscopy images using deep learning and shape models. MS thesis. Provincia de Cartago, Cartago: Tecnol´ogico de Costa Rica, Department of Computer Science. Kaletta, T., Hengartner, M. O. (2006). Finding function in novel targets: C. elegans as a model organism. Nat. Rev. Drug discov., 5(5), 387-399. https://doi.org/10.1038/nrd2031. Kim, J., Kim, T., Park, J. Ki. (2016). First Report of Aphelenchoides bicaudatus (Nematoda: Aphelenchoididae) from South Korea. Anim. Syst., Evol. Diversity, 32, 253-257. https://doi.org/10.5635/ASED.2016.32.4.033. Kofoid, C. A., White, A.W. (1919). A NEW NEMATODE INFECTION OF MAN. JAMA, 72(8), 567–569. https://doi.org/10.1001/jama.1919.02610080033010. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012, December 3-8). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (NIPS), Lake Tahoe, Nevada, USA. (pp. 1097-1105). Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... Shi, W. (2017, July 21-26). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA (pp. 4681-4690). Lambert, K. and S. Bekal. (2002). Introduction to Plant-Parasitic Nematodes. Plant Health Instr. https://doi.org/10.1094/PHI-I-2002-1218-01. Li, D. Z. (1984). Description of some species of the genus Aphelenchoides parasitizing above-ground parts of plants in Sichuan Province. J. SCAU, 1, 68-74. Lin, Y. Y., Tsai, T. T. (1985). Research for Meloidogyne spp. on bananas in the middle region of Taiwan. J. Chinese Soc. Hortic. Sci., 31(1), 44-49. https://doi.org/10.6964/JCSHS.198503.0044. 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Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, Nevada, USA (pp. 2921-2929). http://www.doi.org/10.1109/CVPR.2016.319. Zhuo, K., Cui, R., Ye, W., Luo, M., Wang, H., Hu, X., Liao, J. (2010). Morphological and Molecular Characterization of Aphelenchoides Fujianensis N. Sp. (Nematoda: Aphelenchoididae) from Pinus Massoniana in China. Zootaxa, 2509, 39-52. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82207 | - |
| dc.description.abstract | "作物生產折損率之來源，除氣象型天災及運送時所造成的損失，餘者最大宗者為作物病蟲害，其中植物寄生性線蟲(Plant-parasitic nematodes, PPN)所導致之損失為主因之一，每年約造成全球農業一億美元以上的損失。在糧食蔬果方面，水稻、番石榴、番茄、洋蔥等作物常因線蟲寄生導致黃化、矮化，進而枯萎死亡及減產；於林木保育方面，如：多種松木因線蟲寄生罹患萎凋病造成針葉枯萎且快速死亡，且其可透過天牛快速傳播，受感染之林區多需砍除或燒毀大量松木以策安全；檢疫上，如：穿孔線蟲 (Radopholus similis)、黃金線蟲 (Globodera rostochiensis)等寄主廣泛且危害力強，若是不甚引進將造成國內農林業大量損失。線蟲的種類可依其顯微形態特徵進行判定，然全球線蟲專家人數有限，需應付巨量的鑑定案件需求實為困難。若能利用影像辨識技術開發一線蟲辨識系統，提供快速種類判別分析，則可望紓解實務需求。近年來深度學習於影像辨識上的興起，提供更強大的物件辨識方法，故本研究採用相關演算法建立植物性寄生線蟲影像辨識系統。試驗樣本選用四屬10種全球常見之植物寄生線蟲，包含葉芽線蟲屬(Aphelenchoides spp.)、松材線蟲屬(Bursaphelenchus spp.)、根瘤線蟲屬(Meloidogyne spp.)及根腐線蟲屬(Pratylenchus spp.)。此外，並加入三種除錯類別，包含：食細菌性之秀麗隱桿線蟲(Caenorhabditis elegans)、及兩類蟲生線蟲(Heterocephalobellus sp.及Metarhabditis amsactae)，作為非植物寄生性線蟲之對照組別。資料集樣本共蒐集含局部、全隻及多隻線蟲影像共9481張。深度學習模型選用更快速區域卷積神經網路(Faster Region-based Convolutional Neural Network, Faster R-CNN)，搭配VGG-16、ResNet-50、ResNet-101及ResNet-152等四種骨幹進行辨識效能比較。於四骨幹所建立之辨識模型所得平均精確度均值(mean Average Precision, mAP)分別為85.43%、73.59%、89.53%和88.00%，準確度(accuracy)為67.74%、52.27%、75.05%和71.98%，以ResNet-101模型表現最佳。為嘗試提升辨識效果，引入超解析度生成對抗網路搭配譜歸一化(Super Resolution Generative Adversarial Network with Spectral Normalization, SRGAN-SN)對於影像清晰度較低之影像進行前處理，以期強化其特徵達到增強效果，導入後頭部及尾部之mAP可微幅提升0.9%。最後，為提升模型準確度和統整辨識結果，使用加權投票機制(Weighted voting mechanism)將頭、尾及全隻三者的結果進行整合，最終輸出唯一區域及種類，得出權重組合(0.14, 0.34, 0.52)，使準確度從75.10%提升至90.32%。本研究結果成功結合深度學習中Faster R-CNN及SRGAN-SN方法，完成植物寄生性線蟲辨識系統之建立，並開發使用者介面，提供需求者如檢疫單位及農民一快速篩檢、便利使用之工具。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:33:41Z (GMT). No. of bitstreams: 1 U0001-2806202123243600.pdf: 6973079 bytes, checksum: 365ac61d57ae9c8c3112a91ba682a576 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "TABLE OF CONTENT 致謝 i 摘要 ii ABSTRACT iv TABLE OF CONTENT vi LIST OF FIGURES viii LIST OF TABLES x ABBREVIATIONS xi CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Research Objectives 2 CHAPTER 2 LITERATURE REVIEW 3 2.1 Overview of Nematodes 3 2.1.1 Plant-Parasitic Nematodes (PPN) 3 2.1.2 Entomopathogenic and Free-Living Nematodes 8 2.2 Image Processing on Detection of Nematodes 10 2.3 Deep Learning 10 2.3.1 Convolutional Neural Network (CNN) 10 2.3.2 Faster Region-based Convolutional Neural Network (Faster R-CNN) 12 2.3.3 Generative Adversarial Network (GAN) 14 CHAPTER 3 MATERIALS AND METHODS 16 3.1 Image Acquisition 16 3.2 Image Annotation and Augmentation 18 3.3 Faster Region-based Convolutional Neural Network (Faster R-CNN) 19 3.4 Loss Function 20 3.5 Performance of the Faster R-CNN Detector 21 3.6 SRGAN with Spectral Normalization (SRGAN-SN) 22 3.8 Weighted Voting Mechanism 24 CHAPTER 4 RESULTS AND DISCUSSION 27 4.1 Model Performance 27 4.2 Confusion Matrix 33 4.3 Feature Visualization 36 4.4 Challenging Cases 41 4.5 Image Enhancement Using SRGAN-SN 42 4.6 Weighted Voting mechanism 44 4.7 User Interface (UI) 46 CHAPTER 5 CONCLUSION 48 5.1 Summary 48 5.2 Future work 48 REFERENCES 49 APPENDIX A Performance of Different Augmentation of Faster R-CNN 54 APPENDIX B The Models for Individual Regions (Head, Tail, and Full-length) 55 APPENDIX C Application of NMS on Each Region 56 " | |
| dc.language.iso | en | |
| dc.subject | 加權投票 | zh_TW |
| dc.subject | 植物寄生性線蟲 | zh_TW |
| dc.subject | 更快速區域卷積神經網路 | zh_TW |
| dc.subject | 生成對抗網路 | zh_TW |
| dc.subject | weighted voting | en |
| dc.subject | Plant-parasitic nematode | en |
| dc.subject | Faster Region-based Convolutional Neural Network | en |
| dc.subject | Generative Adversarial Network | en |
| dc.title | 應用卷積神經網路及生成對抗網路於植物寄生性線蟲影像辨識系統之建立 | zh_TW |
| dc.title | Application of Convolutional Neural Network and Generative Adversarial Network on the Development of Plant-Parasitic Nematode Image Identification System | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林達德(Hsin-Tsai Liu),郭彥甫(Chih-Yang Tseng),楊爵因,謝廷芳 | |
| dc.subject.keyword | 植物寄生性線蟲,更快速區域卷積神經網路,生成對抗網路,加權投票, | zh_TW |
| dc.subject.keyword | Plant-parasitic nematode,Faster Region-based Convolutional Neural Network,Generative Adversarial Network,weighted voting, | en |
| dc.relation.page | 57 | |
| dc.identifier.doi | 10.6342/NTU202101181 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-06-30 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物機電工程學系 | zh_TW |
| dc.date.embargo-lift | 2025-01-01 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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