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研究生:陳慧儀
研究生(外文):Hui-Yi Chen
論文名稱:應用高光譜影像法偵測草莓炭疽病之發生
論文名稱(外文):Hyperspectral imaging analysis for the detection of strawberry anthracnose
指導教授:王尚禮葉國楨
指導教授(外文):Shan-Li WangKuo-Chen Yeh
口試委員:葉信宏洪挺軒申雍
口試委員(外文):Hsin-Hung YehTing-Hsuan HungYuan Shen
口試日期:2019-07-09
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:植物醫學碩士學位學程
學門:農業科學學門
學類:植物保護學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:110
中文關鍵詞:草莓炭疽病高光譜影像分析定量偵測非破壞性檢測
DOI:10.6342/NTU201902616
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草莓在台灣是一種重要的經濟作物,年產值可高達 16 億新台幣,屬於一種宿根性作物,可存活 2~3 年,然其在台灣病蟲害眾多,尤以炭疽病為害甚劇,造成台灣草莓種植需年年更新母株。本試驗針對草莓育苗期間面臨主要病害-炭疽病,利用高光譜影像偵測系統,找出可辨識炭疽病的特徵波段,針對草莓葉部炭疽病進行快速且非破壞性的早期偵測,以協助炭疽病的診斷。最終期望達到自動化偵測草莓炭疽病,且在肉眼未見病徵前可利用光譜影像診斷出炭疽病潛伏感染,以協助草莓育苗業者由源頭控管母株及種苗的健康程度,降低炭疽病在苗期危害的嚴重度,將有利於草莓健康種苗供應鏈的建立。本文採用利用逐步迴歸分析進行簡易降維,再利用簡單迴歸分析評估草莓炭疽病的病害程度,將模型分為發病害與發病前潛伏感染兩階段,發病期與潛伏感染期間分別以發病比例與接種病原菌後天數作為發病程度的依據,在評估模型的 R2、RMSEC 值後,其模型平均的 R2 和 RMSEC 分別為 R2 = 0.79、0.91; RMSEC = 0.11、0.53,另外計算偵測極限作為辨識健康與生病樣本的門檻值進行驗證,驗證結果中發病模型的健康樣本辨識率為 87%,而發病樣本的辨識率為 72%,而評估潛伏感染期間的模型在驗證結果中,健康樣本的辨識率為 72%,潛伏感染樣本的辨識率為 71%,兩模型中被辨識為發病或潛伏感染的樣本可再經由檢量模型進一步評估其發病的程度,此研究結果證實高光譜影像分析配合逐步迴歸分析對於草莓炭疽病的定量與田間防治是具有潛力的工具之一。
Strawberry is an economically important herbaceous plant and the annual output value of strawberry industry is up to 1.6 billion NT dollars in Taiwan. Strawberry can subsist two to three years in field; however, there are many diseases during its cultivation, especially, anthracnose, which is the most devastating fungal disease that threatens in strawberry and causes farmers to replant new plants every year. Hyperspectral imaging has the potential to extract integrated spatial and spectral information related to the plant''s functional dynamics regarding both structure and physiology. The objective of this research is to detect strawberry anthracnose with the hyperspectral imaging system, which is expected to find out the characteristic bands of anthracnose identification and detect disease as early as possible. Finally, we hope it can provide novel and non-destructive tools to diagnosis plant disease severity more practically and efficiently. Therefore, strawberry farmers could profit greatly from this technology. In this study, we use a variety of methods to carry out simple dimensionality reduction. We hope to find the characteristic bands that are significant for distinguishing anthracnose. After evaluating the R2 and RMSEC values of various models, we will infer that a better model can be established by stepwise regression analysis of the whole band from 470-950 nm. The model is divided into two stages: disease and latent infection. The average R2 and RMSEC of the two models are R2 = 0.79, 0.91, respectively and RMSEC = 0.11, 0.53, respectively. And then using limited of detection value as a threshold for distinguishing between healthy and sick samples. The diseased model provided the detection of healthy samples with accuracy of 87% and symptomatic samples with accuracy of 72%, while the model for evaluating latent infection provided the detection of healthy samples with accuracy of 72% and symptomatic samples with accuracy of 71%. The samples classified as infected or latent infections in the two models were further evaluated the disease severity by the model. This study confirms that stepwise regression analysis can be one of the classifier tools in hyperspectral imaging analysis for quantitative analysis and field control of strawberry anthracnose.
口試委員審定書 I
致謝 II
摘要 III
Abstract IV
目錄 VI
圖目錄 IX
表目錄 X
第一章 緒言 1
1-1 前言 1
1-2 研究目的 3
第二章 文獻回顧與探討 4
2-1 植物表型體分析 4
2-1-1 起源 4
2-1-2 演進 5
2-1-3 種類 5
2-2 高光譜影像 7
2-2-1 起源 7
2-2-2 高光譜影像應用 8
2-2-3 光譜數據分析 13
2-3 草莓 15
2-3-1 基本特性與重要性 15
2-3-2 台灣栽培史 15
2-3-3 台灣栽培模式 16
2-3-4 台灣關鍵病害 17
2-3-5 草莓炭疽病 17
2-3-6 現行炭疽病檢測技術 19
2-3-7 高光譜影像技術應用於草莓 20
第三章 研究設備與方法 22
3-1 實驗材料 22
3-1-1 供試植物與菌株 22
3-1-2 製備草莓炭疽病菌孢子懸浮液 22
3-1-3 炭疽病菌接種試驗 22
3-2 實驗器材 24
3-2-1 高光譜相機規格與架設 24
3-3 光譜軟體操作與高光譜影像前處理 26
3-3-1 黑白校正公式 26
3-3-2 灰階強度去背 26
3-3-3 降低雜訊 26
3-4 光譜數據分析 28
3-4-1 反射光譜曲線繪製 28
3-4-2 尋找判別草莓炭疽病之特徵波段 28
3-5 檢量線製備 30
3-6 方法偵測極限測定 (Limit of detection, LOD) 31
3-7 模式驗證 33
3-7 訓練數據與測試數據表格 33
3-8 數據分析流程 33
3-9 統計分析軟體 34
第四章 結果 35
4-1草莓炭疽病病徵 35
4-1-1 三維光譜反射圖 37
4-1-2 二維光譜反射率圖 38
4-1-3 光譜植被指標 (Spectral vegetation indices, SVI) 40
4-1-4 一維導數光譜圖 47
4-2 定量分析 48
4-2-1 簡易降維 49
4-2-2 檢量線備製 53
4-2-3 ROI 大小對於檢量線建模能力之影響 54
4-2-4 光譜特徵與病害嚴重程度的相關性 57
4-2-5 探討方法偵測極限 65
4-3 驗證模型準確度 69
4-3-1 新樣品驗證 69
4-3-2 光譜標準化 73
4-3-3 分開發病期與潛伏期 74
4-3-4 兩次分析 83
第五章 討論 85
5-1草莓炭疽病病害 85
5-1-1 草莓炭疽病危害過程 85
5-1-2病害程度 86
5-2 光譜隨發病程度的變化 88
5-2-1 光譜曲線變化 88
5-2-2 光譜植被指標 (Spectral vegetation indices, SVI) 89
5-2-3 一維導數光譜圖 90
5-2-4 降維策略 92
5-3 光譜標準化對於潛伏感染期與發病期模型辨識率的影響 92
第六章 結論與建議 94
6-1 結論 94
6-2 建議 95
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