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標題: | 應用光學非破壞性檢測技術於生物材料分析 Assessments of Biomaterials Using Non-Destructive Optical Inspection |
作者: | I-Chang Yang 楊宜璋 |
指導教授: | 陳世銘(Suming Chen) |
關鍵字: | 生物材料,光學檢測,麥粒,鐮胞菌枯萎症,種苗,溫室,無線射頻辨識系統,光譜影像, Biomaterials,Optical Inspection,Wheat Kernel,Fusarium Head Blight,Seedlings,Greenhouse,RFID (Radio Frequency Identification),Spectral Imaging, |
出版年 : | 2009 |
學位: | 博士 |
摘要: | 使用非破壞、非侵入性的檢測方法已漸成為生物材料測定方法的新趨勢。而光學方法正符合這樣的性質,且能依據不同的檢測目的來進行檢測系統的設計,包含外部與內部品質。本論文包含三部份,從光學分析的角度,探討穀物的安全及品質檢測,並延伸探討在溫室中種苗生長的檢測與管理。在第一部份提到鐮胞菌枯萎症 (Fusarium Head Blight, FHB) 是一種在小粒穀物上由真菌引起之全球性疾病,因此影響了產量、食品和飼料產品的品質與食用安全。本研究發展有效的光學檢測方法,從正常的麥粒中,檢測枯萎損壞的小麥。經由高功率脈衝的LED (綠光和紅光)檢測系統的發展,發現鐮胞菌損壞和正常小麥粒有不同的反射能量表現,以其反射響應強度迴歸而得之雙參數(斜率與判定係數r2)建立線性判別分析模型。在良好、受控制的條件下,麥粒以動態自由落體方式檢測其精度,可大於90%。第二部份研究中,應用高光譜影像來鑑別健康小麥與由真菌引起的黑頭損傷。經由波長影像之分析,於531 nm 波長取得之螢光影像用於影像處理及分類之分析。分析結果指出,當麥粒皆位於背向朝著上方攝影機時,其分類的準確率可高達95%。論文的第三部份,是將RFID(無線射頻辨識系統)導入溫室內精準栽培環境,以光譜影像遙測技術為基礎,針對溫室開發多功能監測系統。透過RFID技術建立一套種苗生產履歷資料庫,內部架構涵蓋栽培管理履歷、設施環境履歷。對溫室內種苗植株進行遙測光譜影像及環境因子擷取,搭配RFID技術整合生產相關履歷資料,自動巡走及擷取、記錄相關種苗生產資訊,以提供溫室種苗生產管理之依據。 Non-destructive and non-invasive inspection of biomaterials is a relatively new technology. The detection apparatus design depends on the purpose of the inspection, such as whether external or internal quality is of interest. There are three parts in this dissertation. From the view point of the optical analysis, the safety and quality issues of grain were discussed in the first two parts, and the monitoring and management of seedling production in the greenhouse was also elaborated in this dissertation. These three parts are summarized as follows: First, a study was implemented to develop more efficient methods for optically detecting wheat kernels damaged by Fusarium head blight, a fungal disease that is usually accompanied by the mycotoxin, deoxynivalenol. Through development of a high-power pulsed LED (green and red light) inspection system, it was found that Fusarium-damaged and normal wheat kernels have different reflected energy responses. Two parameters (slope and coefficient of determination r2) from a regression analysis of the green and red LED responses were used as input parameters in linear discriminant analysis models. The examined factors affecting accuracy were the orientation of the optical probe, the color contrast between normal and Fusarium-damaged kernels, and the manner in which one LED’s response was time-matched to the other LED. The current research on free-falling kernels has demonstrated accuracies (>90% for wheat samples with high visual contrast) that approach those of controlled, in-laboratory conditions. This approach may lead to improvements in high-speed optical sorters. Second, a feasibility study was conducted on the use of hyperspectral imaging to differentiate sound wheat kernels from those with a damage condition called black point. Through analysis of wavelength images, one fluorescence wavelength (531 nm) was selected for image processing and classification analysis. Results indicated that with this wavelength alone, classification accuracy could be as high as 95% when kernels were oriented with their dorsal side toward the camera. Third, a system was designed and implemented for precision cultivation and micro-environment monitoring for seedling production in the greenhouse. Based on RFID-integrated multi-functional remote sensors with a plant-oriented sensing algorithm for both monitoring and controlling the greenhouse environment, the system linked spectral and environmental inputs for the control of seedling irrigation. Further, the study aimed to construct a traceability system for seedling production in greenhouses using RFID technology. The contents of the developed database were divided into two parts, a management traceability system and an environment traceability system. The traceability systems provided the operators with records of seedling growth and management history and served as the decision bases for spray and related operations. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44189 |
全文授權: | 有償授權 |
顯示於系所單位: | 生物機電工程學系 |
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