(3.235.236.13) 您好!臺灣時間:2021/05/15 04:28
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:鐘偉菖
研究生(外文):Wei-Chang Chung
論文名稱:應用高光譜影像技術於青江菜黑斑病與草莓葉部炭疽病之偵測與分析
論文名稱(外文):Detection and Analysis of Bok Choy Black Spot Disease and Strawberry Foliar Anthracnose Disease Using Hyperspectral Imaging
指導教授:林達德林達德引用關係
指導教授(外文):Ta-Te Lin
口試委員:郭彥甫鍾嘉綾
口試委員(外文):Yan-Fu KuoChia-Lin Chung
口試日期:2013-06-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生物產業機電工程學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:129
中文關鍵詞:高光譜影像草莓炭疽病青江菜黑斑病機器學習非破壞性植物病害評估
外文關鍵詞:Hyperspectral ImagingStrawberry Anthracnose DiseaseBok Choy Black Spot DiseaseMachine LearningNon-destructive Plant Disease Assessment
相關次數:
  • 被引用被引用:0
  • 點閱點閱:560
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
病害侵入植物後,會影響植物成長與外表,在農產品部分則會降低產量,進而造成經濟損失。評估應用高光譜影像技術辨識青江菜黑斑病與草莓葉部炭疽病的潛力是本研究的主旨。高光譜影像科技是一項結合在可見光與近紅外光的數位影像與光譜資訊之非破壞性量測技術,透過高光譜影像可以獲取植物葉部許多生理與化學特性等,諸如葉綠素與水分的變化。本研究以逐步判別分析法 (Stepwise Discriminant Analysis, SDA) 與彈性網路 (Elastic Net, EN) 兩種特徵選取演算法找出具有判別植物病害的重要波長,並且將這些重要的波長以線性判別分析法建立分類器,判別植物是否健康。後續應用假彩色於高光譜影像上,透過顏色的不同表示植物葉部感染病害的位置與感染的範圍。
本研究透過反射率、反射率之一階導數與二階導數,比較不同波長數量組合的模型之成效。SDA在光譜反射率中選取12波長組合的模型,辨識青江菜是否有黑斑病的正確率為98.3%。同樣由SDA在光譜反射率選取4波長組合的模型辨識青江菜的健康、發病前與發病三類別,其正確率達77.4%。辨識草莓葉部是否有炭疽病,將反射率進行一階導數計算後,由SDA挑選2波長組合的模型,辨識正確率達98.5%。而在辨識草莓葉部的健康、發病前與發病的部分,SDA挑選出反射率中16個波長組合的模型為最理想,其正確率可達89.3%。
實驗與分析結果說明應用機器學習建立少量波長組合的模型,可以進行植物病害的辨識。未來可以透過多光譜影像技術來建立一套自動非破壞性植物病害偵測系統,以利於監控植物病害。


The growth and appearance of plant are affected and destroyed when infected by pathogens. Plant diseases not only decrease the production rate, but also cause economic losses. The thesis aims to evaluate the potential of strawberry foliar Anthracnose disease and Bok Choy black spot disease identification by using hyperspectral images. Hyperspectral imaging is a non-destructive measurement technique that combines digital imaging and spectroscopy in visible and near infrared wavelength regions. The technique can provide physical and chemical information of leaves simultaneously, such as the change of chlorophyll and water in leaves. In this study, two feature selection algorithms, stepwise discriminant analysis (SDA) and elastic net (EN), were applied to define the significant wavelengths for discriminating plant diseases, and then employed the defined to establish a linear discriminant analysis classifier. Moreover, pseudo color image were also used to represent the infected locations and regions on the leaf.
In this approach, the reflectivity of the selected wavelengths, and the first and second derivative of the reflectivity were employed as the features. The recognition performance of the features was compared. The accuracy of classifying healthy Bok Choy leaves and infected leaves is 98.3%, and the classification model was the built with 12 wavelengths selected by SDA. To discriminate healthy, incubation and symptomatic of Bok Choy leaves, the accuracy is 77.4% with 4 selected wavelengths. For strawberry, the accuracy of the classifying healthy and infected leaves reaches to 98.5%, and the classification model was established with 2 wavelengths selected by SDA according to the first derivative. For classifying three different Anthracnose infection status (healthy, incubation and symptomatic), the accuracy is 89.3%, and 16 wavelengths defined by SDA were applied to build the model.
The experimental results imply that the use of machine learning algorithm is able to discriminate plant disease by few and significant wavelengths. Furthermore, the proposed method and procedure can be applied to establish an automatic, non-destructive plant disease detection system using multispectral imaging for monitoring plant disease.


誌謝 i
中文摘要 iii
Abstract iv
圖目錄 x
表目錄 xiv
第1章 緒論 1
1.1 前言 1
1.2 研究目的 3
1.3 論文架構 5
第2章 文獻探討 7
2.1 植物病害 7
2.1.1 黑斑病 7
2.1.2 炭疽病 7
2.2 植物病害檢測 8
2.3 高光譜影像系統 10
2.3.1 高光譜科技 10
2.3.2 高光譜影像技術 11
2.3.3 植物病害檢測之光譜技術 13
2.4 高光譜分析 15
2.4.1 光譜基本分析方法 15
2.4.2 植被指標 16
2.4.3 分類演算法 17
2.4.4 特徵選取 (Feature selection) 19
第3章 材料與方法 21
3.1 實驗材料 21
3.1.1 植物栽培 21
3.1.2 葉片前處理與固定 22
3.1.3 接種處理 23
3.2 實驗器材 26
3.2.1 拍攝彩色影像器材 26
3.2.2 高光譜影像儀器 27
3.2.3 儀器操作之軟體 30
3.2.4 儀器使用流程 30
3.3 數據處理 31
3.3.1 高光譜分析軟體 31
3.3.2 正規化 33
3.3.3 降低雜訊 34
3.3.4 灰階影像轉彩色影像 35
3.3.5 以兩波長去除影像背景 37
3.4 特徵萃取 39
3.4.1 病害程度評估 39
3.4.2 彩色影像之比較 42
3.4.3 影像座標匹配 42
3.5 特徵擷取 44
3.5.1 一階導數 44
3.5.2 二階導數 45
3.6 辨識模型 46
3.6.1 線性判別分析 46
3.7 特徵選取 48
3.7.1 逐步判別分析法 49
3.7.2 彈性網路 (Elastic net) 51
3.8 實驗樣本與分析樣本 54
3.9 數據分析流程 57
第4章 分析與結果 59
4.1 青江菜黑斑病之高光譜影像與分析 61
4.1.1 高光譜影像拍攝結果與光譜曲線 61
4.1.2 黑斑病辨識 66
4.1.3 影像顯示辨識結果 72
4.2 青江菜黑斑病發病前之分析 77
4.2.1 黑斑病發病前之辨識 77
4.2.2 影像顯示病害辨識結果 83
4.2.3 未包含與包含潛伏期分析之比較 88
4.3 草莓炭疽病之高光譜影像與分析 89
4.3.1 高光譜影像拍攝結果與光譜曲線 89
4.3.2 炭疽病辨識 93
4.3.3 影像顯示辨識結果 99
4.4 草莓炭疽病發病前之分析 102
4.4.1 炭疽病發病前之辨識 102
4.4.2 影像顯示病害辨識結果 108
4.4.3 未包含與包含潛伏期分析之比較 113
第5章 結論與建議 115
5.1 結論 115
5.2 建議 117
參考文獻 119
附錄 127
Matlab 之重要函式說明 127



1.王紀華、趙春江、黃文江。2008。農業定量遙感基礎與應用。初版。北京:科學出版社。
2.艾群。1996。利用音波檢測西瓜內部品質--成熟度、空洞之存在與方位。農業機械學刊 5(4):57-71。
3.艾群。2000。鳳梨內部品質音波檢測及重量分級自動化系統研製。關鍵技術及成果發表會6.24 - 6.28。
4.安寶貞、謝廷芳、蔡志濃、黃晉興、楊宏仁。2008。非農藥防治新技術的開發與應用。節能減碳與作物病害管理研討會專刊 23:137-153。
5.行政院農業委員會。2009。國產優良品牌草莓生產管理技術作業標準。苗栗:行政院農業委員會農糧署。網址:http://www.afa.gov.tw/。上網日期:2013-04-02。
6.李聯興、葉俊巖、賴守正。1992。草莓主要病害綜合防治。農藥世界 104:17-18。
7.吳文希。2011。臺灣植物病害之微生物防治。生物生技產業季刊 28。
8.柯建全、黃膺任、艾群。1999。應用影像處理檢測荔枝果皮之顏色變化。農業機械學刊 8(1):59-68。
9.郭立穎、陳世銘、張文宏。1998。洋香瓜糖度檢測之研究 (一) 影像紋理分析法。農業機械學刊 7(1):75-86。
10.陳世銘、方煒、羅筱鳳、曹幸之、張耀乾、廖國基、顏炳郎、蔡兆胤。2011。台灣植物工廠現況與發展策略之分析。農業機械學刊 20(4):95-106。
11.費雯綺、王喻其編。2007。植物保護手冊-果樹篇。初版,204-297。臺中:行政院農業委員會農業藥物毒物試驗所。
12.鄧汀欽、安寶貞、鄭櫻慧、林子凱、蔣國司。2011。氣候變遷與植物病害消長。臺中:農業試驗所。
13.鄭宇哲、余仁方、林達德。2005。桃子與李子磁振影像中損傷區域之影像分割方法。農業機械學刊 14(2):11-26。
14.劉興庫、李兆華。1993。多光譜診斷植物病害的初步研究。東北林業大學學報 21(2):11-20。
15.蔡竹固、黃啟鐘。1994。十字花科蔬菜病蟲草害及其綜合防治。嘉義農專推廣簡訊 49:11-19。
16.謝廷芳、黃鴻章。2009。病害防治與植物健康管理。2009 花卉健康管理研討會專刊 91-104。
17.臺南區農業改良場。2010。不結球白菜類型之青梗白菜”臺南一號”。臺南區農業專訊 72:10。
18.Bagdanov A. D., A. del Bimbo, and F. Pernici. 2005. Acquisition of high-resolution images through on-line saccade sequence planning. Proceedings of the Third ACM International Workshop on Video Surveillance 121-130.
19.Bannari, A., D. Morin, F. Bonn, and A. R. Huete. 1995. A review of vegetation indices. Remote Sensing Reviews 13(1-2): 95-120.
20.Birth, S.G. and R. G. McVey. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal 60: 640-643.
21.Chaerle, L. and D. van der Straeten. 2001. Seeing is believing: imagingtechniques to monitor plant health. Biochimica et Biophysica Acta (BBA) - Gene Structure and Expression 1519: 153-166.
22.Chaerle, L., D. Hagenbeek, X. Vanrobaeys, and D. Van Der Straeten. 2007. Early detection of nutrient and biotic stress in Phaseolus vulgaris. International Journal of Remote Sensing 28(16): 3479-3492.
23.Chaerle, L., S. Lenk, I. Leinonen, H. G. Jones, D. Van Der Straeten, and C. Buschmann. 2009. Multi-sensor plant imaging: towards the development of a stress-catalogue. Biotechnology Journal 4: 1152-1167.
24.Chang, C. C., J. C. Lin. 2001. Libsvm: A Library for Support Vector Machines. Accessed 10/2009.
25.Chappelle, E. W., F. M. Wood, J. E. McMurtrey III , and W. W. Newcomb. 1984. Laser-induced fluorescence of green plants. 1: A technique for the remote detection of plant stress and species differentiation. Applied Optics 23(1): 134-138.
26.Clemmensen, L., T. Hastie, D. Witten, and B. Ersboll. 2011. Sparse discriminant analysis. Technometrics 53(4): 406-413.
27.Cortes, C. and V. Vapnik. 1995. Support-vector networks. Machine Learning 20(3): 273-297.
28.Csefalvay, L., G. D. Gaspero, K. Matous, D. Bellin, B. Ruperti, and J. Olejnickova. 2009. Pre-symptomatic detection of Plasmopara viticola infection in grapevine leaves using chlorophyll fluorescence imaging. European Journal of Plant Pathology 125: 291-302.
29.Delalieux, S., J. van Aardt, W. Keulemans, and P. Coppin. 2007. Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral analysis. In Proceedings of the 4th EARSLeL Workshop on Imaging Spectroscopy, Warsaw 677-689.
30.Delwiche, S. R. and R. A. Graybosch. 2002. Identification of waxy wheat by near-infrared reflectance spectroscopy. Journal of Cereal Science 35 (1): 29-38.
31.ElMasry, G., N. Wang, C. Vigneault, J. Qiao, and A. ElSayed. 2008. Early detection of apple bruises on different background colors using hyperspectral imaging. LWT-Food Science and Technology 41(2): 337-345.
32.Fisher, R. A. 1936. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7(2): 179-188.
33.Green, R. O., M. L. Eastwood, C. M. Sarture, T. G. Chrien, M. Aronsson, B. J. Chippendale, J. A. Faust, B. E. Pavri, C. J. Chovit, M. Solis, M. R. Olah, and O. Williams. 1998. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ 65: 227-248.
34.Grellet-Bournonville, C. F., M. G. Martinez-Zamora, A. P. Castagnaro, and J. C. Diaz-Ricci. 2012. Temporal accumulation of salicylic acid activates the defense response against in strawberry. Plant Physiology and Biochemistry 54: 10-16.
35.Hastie, T., A. Buja, and R. Tibshirani. 1995. Penalized Discriminant Analysis. The Annals of Statistics 23(1): 73-102.
36.Hoerl, A. and R. Kennard. 1988. Ridge regression. In Encyclopedia of Statistical Sciences 8: 129-136.
37.Huang, W., D. W. Lamb, Z. Niu, Y. Zhang, L. Liu, and J. Wang. 2007. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture 8: 187-197.
38.Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14(2): 1137-1145.
39.Kruse, F. A., A. B. Lefkoff., J. W. Boardman., K. B. Heidebrecht., A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz. 1993. The spectral image-processing system (Sips) - interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment 44: 145-163.
40.Laluk, K. and T. Mengiste. 2010. Necrotroph attacks on plants: Wanton destruction or covert extortion. The Arabidopsis book/American Society of Plant Biologists 8.
41.Larrsolle, A. and H. H. Muhammed. 2007. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precision Agriculture 8: 37-47.
42.Lee, K. J., S. Kang, M. S. Kim, and S.H. Noh. 2005. Hyperspectral imaging for detecting defect on apples. ASABE 053075.
43.Lopez, M. M., E. Bertolini, A. Olmos, P. Caruso, M. T. Gorris, P. Llop, R. Penyalver, and M. Cambra. 2003. Innovative tools for detection of plant pathogenic viruses and bacteria. International Microbiology 6: 233-243.
44.Lucas, J. 2009. Plant pathology and plant pathogens. Wiley 133.
45.Mahlein, A. K., E. C. Oerke, U. Steiner, and H. W. Dehne. 2012. Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology 133(1): 197-209.
46.Mahlein, A. K., T. Rumpf, P. Welke, H. W. Dehne, L. Plumer, U. Steiner, and E. C. Oerke. 2013. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment 128: 21-30.
47.Mantero, P., G. Moser, and S. B. Serpico. 2005. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing 43 (3): 559-570.
48.Martinez-Sotoca, J., and F. Pla. 2006. Hyperspectral data selection from mutual information between image bands. Lecture Notes in Computer Science 4109: 853-861.
49.Moshou, D., C. Bravo, R. Oberti, J. West, L. Bodria, A. McCartney, and H. Ramon. 2005. Plant disease detection basedondata fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging 11(2): 75-83.
50.Mountrakis, G., J. Im, and C. Ogole. 2011. Support vector machines in remote sensing: a review. Isprs Journal of Photogrammetry and Remote Sensing 66: 247-259.
51.Munch, S., U. Lingner, D. S. Floss, N. Ludwig, N. Sauer, and H. B. Deising. 2008. The hemibiotrophic lifestyle of species. Journal of Plant Physiology 165(1): 41-51.
52.Nutter, F. W. J., R. H. Littrell, and T. B. Brennemann. 1990. Utilization of a multispectral radiometer to evaluate fungicide efficacy to control late leaf spot in peanut. Phytopathology 80: 102-108.
53.Oerke, E. C., P. Frohling, and U. Steiner. 2011. Thermographic assessment of scab disease on apple leaves. Precision Agriculture 12(5): 699-715.
54.Okamoto, H., T. Murata, T. Kataoka, and S. I. Hata. 2007. Plant classification for weed detection using hyperspectral imaging and wavelet analysis. Weed Biol. Weed Biology and Management 7: 31-37.
55.Pieterse, C. M., A. Leon-Reyes, S. Van der Ent, and S. C. Van Wees. 2009. Networking by small-molecule hormones in plant immunity. Nature Chemical Biology 5(5): 308-316.
56.Pimentel, D., R. Zuniga, and D. Morrison. 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics 52(3): 273-288.
57.Purcell, D. E., M. G. O’Shea, R. A. Johnson, and S. Kokot. 2009. Near-infrared spectroscopy for the prediction of disease rating for Fiji leaf gall in sugarcane clones. Applied Spectroscopy 63(4): 450-457.
58.Qin, J., T. F. Burks, M. S. Kim, K. Chao, and M. A. Ritenour. 2008. Citrus canker detection using hyperspectral reflectance imaging and PCA based image classification method. Sens. Instrum. Food Qual. Safety 2: 168-177.
59.Rabinowitch, H. D., and J. L. Brewster. 1990. Onions and allied crops: biochemistry, Food Science, and Minor Crops. CRC Press, Boca Raton, FL, USA.
60.Rouse, J. W., R. H. Haas, J. A. Schell, J. A. and D. W. Deering. 1973. Monitoring vegetation systems in the Great Plains with ERTS. In Proc. 3rd Earth Resourses Technology Satellite (ERTS) 301- 317.
61.Rumpf, T., A. K. Mahlein, D. Dorschlag, and L. Plumer. 2009. Identification of combined vegetation indices for the early detection of plant diseases. In: Proceedings of the SPIE Conference on Sensing for Agriculture, Ecosystems and Hydrology 747217-747227.
62.Rumpf, T., A. K. Mahlein, U. Steiner, E. C. Oerke, H. W. Dehne, and L. Plumer. 2010. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74(1): 91-99.
63.Sankaran, S., A. Mishra, R. Ehsani, and C. Davis. 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 72: 1-13.
64.Scholes, J. D., and S. A. Rolfe. 2009. Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective. Functional Plant Biology 36: 880-892.
65.Steddom, K., W. M. Bredehoeft, M. Khan, and M. C. Rush. 2005. Comparison of visual and multispectral radiometric disease evaluations of Cercospora leaf spot of sugar beet. Plant Disease 89: 153-158.
66.Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58: 267-288.
67.Tsai, F. and W. Philpot. 1998. Derivative analysis of hyperspectral data. Remote Sensing of Environment 66(1): 41-51.
68.Upchurch, B. L., H. A. Affeldt, K. A. Norris, and J. A. Throop. 1990. Spectrophotometric study of bruises on whole red delicious apples. Trans. ASAE 33: 585-589.
69.Vrindts, E., J. D. Baerdemaeker, and H. Ramon. 2002. Weed detection using canopy reflection. Precision Agriculture 3(1): 63-80.
70.Von Witzke, H., S. Noleppa, and G. Schwarz. 2008. Global agricultural market trends and their impacts on European Union agriculture 6276.
71.Williams, P., and K. H. Norris. 2001. Near-infrared technology in the agricultural and food industries. 2nd ed. USA: American Association of Cereal Chemists.
72.Yang, C., J. H. Everitt, and J. M. Bradford. 2004. Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the ASAE 47(3): 915-924.
73.Yao, P., and M. Tian. 2003. A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction. IEEE Transactions on Geoscience and Remote Sensing 41(6): 1469-1478.
74.Ye, X., K. Sakai, M. Manago, S. I. Asada, and A. Sasao. 2007. Prediction of citrus yield from airborne hyperspectral imagery. Precision Agriculture 8: 111-125.
75.Zion, B., P. Chen and M.J. McCarthy. 1995. Detect of bruises in magnetic resonance images of apples. Computers and Electronics in Agriculture 13(4): 289-299.
76.Zou, H., T. Hastie, and R. Tibshirani. 2006. Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics 15: 265-286.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文