臺灣博碩士論文加值系統

世界各地近年來因氣候快速變遷，形成極端與災害性天氣發生，尤其是溫度變化及降雨型態的改變，造成農業生產所需之水資源缺乏，當前作物面臨非生物逆境如乾旱、淹水等狀態，所以強化作物栽培管理的韌性 (resilience)為現階段作物管理的重要目標。然而過去評估作物水分逆境的傳統方法十分耗時，且因環境條件時常變化，而影響觀測值，非接觸式感測 (non-contact sensing)技術擁有快速獲得資訊與非破壞性檢測等優點，如紅外線熱影像儀 (infrared thermal imager)的使用，因此已成為現代面臨乾旱問題中，控制灌溉及檢測作物生長狀態下著重開發的重要工具。本試驗種植中晚熟稉稻台南11號，於國立中興大學農業試驗場霧峰北溝第三試驗區，進行不同乾旱程度試驗，並搭配生理參數及植體溫度之調查，乾旱處理組於分蘗中期進行，乾旱程度分為植體含水率 (plant water content, PWC) 80%、60%及40%三種，當達到植體含水率目標即回復灌溉，且使用紅外線熱影像儀，檢測乾旱處理下水稻之植體溫度資料，初步判斷作物生長狀態後，並搭配作物各項生理指標建立模型，並結合氣象資料與植體含水率進行複回歸分析，結果顯示在於110年一、二期作及111年一期作的校正後決定係數 (adjusted coefficient determination, Ra2)分別為0.80、0.82、0.86以及0.94，因此推斷未來可能可以利用拍攝紅外線熱影像進行植體含水量的推估，並利用作物水分逆境指數 (crop water stress index, CWSI)判斷作物水分逆境狀態，以利於在期作的栽培管理灌溉計畫中，提前規劃田間事務，並賦予在農事中較彈性的安排與決策，而能減少水資源浪費，降低人力、物力及時間成本上的支出，並讓環境及栽培資訊進行連動與共享，確保資源利用效率最大化，最終達成具有韌性的智慧農業體系。

In recent years, extreme and catastrophic weather events have occurred worldwide due to rapid climate change, particularly changes in temperature and rainfall patterns, leading to a lack of water resources necessary for agricultural production. As a result, crops are exposed to non-biological stress such as drought and flooding. In prolonged drought conditions, crops are susceptible to water shortage, affecting their growth and reducing their productivity. Therefore, strengthening the resilience of crop management is an important goal for current crop management.However, traditional methods of measuring crop water status to assess water stress are very time-consuming, and frequent changes in environmental conditions can affect observations. Therefore, non-contact sensing technology has advantages such as rapid information acquisition and non-destructive testing. Infrared thermal imagers have become important tools for controlling irrigation and detecting crop growth status in modern agriculture facing drought problems.In this experiment, the mid-late maturity indica rice cultivar Tainan 11 was used in the third experimental area of the Wufeng Beigou Agricultural Experimental Station of National Chung Hsing University to conduct experiments under different drought conditions. Physiological parameters and plant temperature were investigated. The control group was subjected to conventional cultivation, and the treatment group was subjected to drought in the tillering stage. The drought level was divided into three levels of plant water content (PWC) of 80%, 60%, and 40%. When the target PWC was reached, irrigation was resumed, and the infrared thermal imager was used to detect the plant temperature data of rice under drought treatment, preliminary judging the growth status of crops. A model was established with various physiological indicators of crops and combined with meteorological data and PWC for multiple regression analysis. The results showed that the adjusted coefficient of determination (Ra2) was high at 0.80, 0.81, 0.86 and 0.94 in the 2021 and 2022-year first and second cropping seasons, respectively. Therefore, it is inferred that infrared thermal imaging may be used to estimate PWC in the future, and the crop water stress index (CWSI) may be used to determine the water stress status of crops. This will facilitate the flexible planning of field affairs in cultivation management and irrigation plans in the cropping season, reduce water resource waste, and reduce human, material, and time costs. Moreover, environmental and cultivation information can be linked and shared, providing a reference for decision-makers and implementers, ensuring that resource utilization efficiency is maximized, and ultimately achieving a resilient smart agriculture system.

中文摘要 i
Abstract ii
目次 iii
表目次 vi
圖目次 vii
附圖目次 viii
壹、前言 1
一、極端氣候下全球與臺灣之水稻生產概況 1
二、極端氣候下臺灣韌性農業之概況 2
三、智慧化栽培中利用紅外線熱影像技術之栽培管理 3
(一) 紅外線熱影像在作物栽培管理之應用 3
(二) 紅外線熱影像與其他表型分析方法之差異 4
四、利用紅外線熱影像儀在水稻在生產過程中之水分管理 4
(一) 水稻在生產過程中水分管理之重要性 4
(二) 紅外線熱影像在作物栽培中水分管理之應用 5
貳、材料與方法 7
一、試驗地點與材料 7
二、田間試驗栽培管理 7
三、盆栽試驗栽培管理 8
四、水稻植體含水率之檢測 8
五、水稻葉片葉綠素破壞性取樣之檢測 8
六、水稻離子滲漏率之檢測 9
七、紅外線熱影像儀 10
(一) 紅外線熱影像儀拍攝流程及架設 10
(二) 熱影像溫度選取判讀與分析 10
八、氣象資料 10
(一) 逐時資料 11
(二) 逐日資料 11
(三) 逐月資料 11
(四) 生育度數 (growing degree days, GDDs) 11
九、作物水分逆境指數分析 (crop water stress index, CWSI) 12
十、水稻產量及產量構成要素之測量 12
十一、統計分析 12
(一) 變方分析 (Analysis of variance, ANOVA)及多重比較 12
(二) Student's t-test 12
(三) 簡單線性回歸分析 13
(四) 複回歸分析 13
參、實驗結果 14
一、中晚熟稉稻台南11號於不同乾旱處理下之田間紅外線熱影像拍攝 14
二、中晚熟稉稻台南11號於不同乾旱處理下之生理性狀變化 14
三、中晚熟稉稻台南11號於不同乾旱處理下水稻產量構成要素及產量影響 16
四、中晚熟稉稻台南11號於不同乾旱處理下植體含水率與逆境指數之回歸分析 17
五、中晚熟稉稻台南11號於不同乾旱處理下植體含水率與氣象因子之複回歸分析 17
六、以紅外線熱影像建構回歸模型之水分灌溉決策流程 18
肆、討論 20
一、利用紅外線熱影像建立水稻水分判釋及管理栽培資料庫 20
二、利用紅外線熱影像進行水稻灌溉評估之情況與影響衍生 21
三、如何在臺灣建立具有韌性的智慧農業生態系之探討 21
伍、參考文獻 23
陸、圖表 29
柒、附錄 45

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