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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳虹諺(Hungyen Chen) | |
| dc.contributor.author | Yu-Chuan Chen | en |
| dc.contributor.author | 陳昱權 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:05:05Z | - |
| dc.date.available | 2023-06-30 | |
| dc.date.available | 2022-11-24T03:05:05Z | - |
| dc.date.copyright | 2021-07-09 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-06-28 | |
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(1988b). 葉稻熱病引起水稻產量損失估計. 中華農業研究. 蔡武雄, 李新傳, 楊涌祚, 游俊明, 呂文通, 簡錦忠. (1981). 防治稻熱病藥劑之藥效. 中華農業研究. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80358 | - |
| dc.description.abstract | 稻米為亞洲地區國家主要糧食作物之一，但在稻米生產期常面臨病害問題造成產量減產，其中影響最大的一個為水稻稻熱病。根據嘉義農業試驗分所的研究在不同罹病時期以及罹病等級所造成的產量損失會有明顯差異，在早期進行防治與在較晚期進行防治相比，水稻產量損失可以達到3倍以上。然而罹病狀態的判斷需要專業的知識，在專業人員的人力缺乏下，難以達成全田區的偵測。 本研究中透過無人機所拍攝之多光譜影像並以全新的影像處理方式建立新的光譜特徵。將每個光譜資料切分成不同大小之類別，重新組合成新光譜光段組合後計算該特徵於田區中發生頻率。並將罹病率與罹病株率利用階層式分群分為高罹病狀態與低罹病狀態，配合熱圖(heatmap)以及階層式分群探討在不同種發生光譜光段組合頻率對應高罹病、低罹病罹病狀態之間的關係，並利用Cramer’s V衡量罹病狀態以及該光譜特徵之相關性。 並利用K近鄰分類法、支持向量機、決策樹以及隨機森林四種常用的分類模型將各光譜光段頻率作為特徵偵測水稻罹病狀態。其罹病狀態的判斷正確率最高可達78.18%。於實際田間應用可輔助農民在大範圍水稻田區偵測其罹病狀態以及罹病的區域，並可依此偵測結果對水稻田稻熱病的危害進行風險管理以及病害防治處理。於改良場育種人員來說，可以在試驗的初期於多水稻品種中輔助快速篩選掉高罹病狀態的水稻品種，真正落實並降低專業人力的匱乏。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:05:05Z (GMT). No. of bitstreams: 1 U0001-1706202119263400.pdf: 14786631 bytes, checksum: bf538b0928fb2b01ac30bd2508528775 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 摘要 iii Abstract iv I 前言 1 1 從糧食安全層面來看水稻稻熱病的影響 1 2 水稻稻熱病之影像辨識 2 (1)近距離影像偵測稻熱病 2 (2)無人機影像偵測稻熱病 3 3 探討各光譜間的關係所衍生特徵於稻熱病偵測 3 II 材料與方法 5 1 資料介紹與前處理 5 (1)罹病率與罹病株率與罹病狀態分群 5 多光譜影像資料前處理 6 2 光譜光段組合與頻率處理以及計算 9 3 統計分析方法與機器學習模型 11 (1)階層式分群分析 11 (2)卡方檢定與Cramer’s V 12 (3)機器學習模型 13 (4)十折交叉驗證(10-Fold Cross Validation) 14 III 結果與討論 15 1 專家標註資料進行罹病狀態分群 15 各光譜與光段分割方式組合探索式分群分析 18 2 利用分類器進行罹病狀態分類辨識 22 IV 總結 25 1 研究流程彙整 25 2 光譜光段與水稻稻熱病罹病狀態關聯性 26 3 機器學習模型偵測水稻稻熱病罹病狀態 27 4 實際應用之輔助決策建議 27 V 參考文獻 28 VI 圖附錄 光譜光段與罹病狀態之關係(節錄 可見光譜包含/不包含土壤資訊分割光段數16至128與可見光加進紅光包含/不包含土壤資訊分割光段數16至64) 32 | |
| dc.language.iso | zh-TW | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 水稻稻熱病 | zh_TW |
| dc.subject | 病害偵測 | zh_TW |
| dc.subject | 無人機影像 | zh_TW |
| dc.subject | Machine learning | en |
| dc.subject | Rice blast | en |
| dc.subject | Disease detection | en |
| dc.subject | UAV image | en |
| dc.title | 應用影像資料分群分析及機器學習偵測水稻稻熱病 | zh_TW |
| dc.title | Detect Rice Blast Disease by Applying Image Data Clustering Analysis and Machine Learning | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 劉力瑜(Li-Yu Liu) | |
| dc.contributor.oralexamcommittee | 蔡育彰(Hsin-Tsai Liu),(Chih-Yang Tseng) | |
| dc.subject.keyword | 水稻稻熱病,病害偵測,無人機影像,機器學習, | zh_TW |
| dc.subject.keyword | Rice blast,Disease detection,UAV image,Machine learning, | en |
| dc.relation.page | 45 | |
| dc.identifier.doi | 10.6342/NTU202101036 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-06-28 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-06-30 | - |
| 顯示於系所單位： | 農藝學系 | |
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