細胞培養為生物實驗研究之根本，時至今日，多數細胞培養實驗仍需仰賴大量人力且耗費大量時間，反覆操作類似步驟以進行培養流程。市面現存自動化細胞培養機台所費不貲，且因其體積過大、操作步驟繁複，難以與現行多數生物研究實驗室互容。本研究力求以低成本、設備輕巧及簡易操作模式開發一般實驗室用自動化細胞培養系統，並引入物聯網應用，使用者可遠端了解實驗室並掌握細胞狀況，期許在細胞繼代培養領域大幅降低人力使用及時間成本。
本實驗研究旨為以開發設計之自動化細胞培養系統，同時維持四種貼附式細胞株活性達一個月之久，即時提供生醫相關實驗室具活性的細胞，以利後續相關應用與研究。此系統功能包含自動控制細胞繼代程序、細胞培養環境維持、細胞汙染判斷、影像演算法辨識細胞生長面積、即時的雲端資料傳輸及遠端即時監控操作等相關物聯網應用。本系統以樹梅派(Raspberry Pi)微型電腦為系統核心，並輔以研究相關之軟硬體設備，不僅能遠端控制自動化繼代培養程序、即時細胞影像判別，還能利用相關影像處理技術進行滿盤汙染偵測及系統環境監控，將所得的繼代資料與影像即時上傳Firebase雲端資料庫及使用者手機應用程式(Application, APP)。
本系統環境使用二氧化碳、溫濕度感測器及水位監測器控制環境參數，使用者可透過人機介面或手機APP訂定欲繼代的貼附式細胞、培養皿、培養基(medium)、胰蛋白酶(trypsin)、磷酸鹽緩衝生理鹽水(Phosphate buffered saline, PBS)、培養環境等數值及資訊，其後，相關細胞影像資訊及所處環境數據將以遠端傳輸方式傳至手機APP，提供使用者觀察並於Firebase雲端、外接數據儲存硬碟同部進行備份。當影像判斷細胞滿盤時系統將會進入繼代換盤程序，完成後再繼續觀察培養。若使用者欲觀察細胞生長情況、環境狀況或想更改系統參數及執行模式，皆可透過手機APP進行監視及操控，當細胞生長環境異常時或發生汙染時，系統也會透過手機APP及電子郵件即時提供示警通知。

Cell culture is the fundamental of biological experiment and research, up to now, In order to do the process of cellculturing﹐most of the cellculture experiments still rely on a lot of manpower and spend plenty of time to repeat similar steps. Existing automatic cell culture machine available in the market is not expensive, it’s difficult to fit with most current biological research laboratories because of its giant size and complicated steps. This research will develop an automated cell culture system be suitable for general laboratory with low cost, light weight and simple operation mode, furthermore, it introduces the application of Internet of Things, in this way, users do not need to keep a close eye on the cell status in the laboratory all the time which reducing manpower and time costs.
The aim of this experiment is to develop and design an automated cell culture system while maintaining the activity of four attached cell lines for up to a month, providing cells in biomedical laboratories to facilitate subsequent application and research. The system has many functions including automatic control of cell culture programs, maintenance of cell culture environment, judgement of cell contamination, recognition of cell growth area by image algorithms, real-time cloud data transmission and remote real-time monitoring operations and other related IoT applications. We choose a microcomputer called Raspberry Pi as the core of the system and install related hardware and software equipment, not only to remote control of automated cultures, but also to use the techniques of image processing to execute real-time cell imaging, full-panel detection of pollution and system environment monitoring, etc. The resulting data and images can be instantly uploaded to Firebase cloud database and user's mobile APP.
This system environment uses carbon dioxide, temperature and humidity sensors and water level monitors to control environmental parameters. The user can set the attached cells , cellculture dish, medium, trypsin, PBS, cellculture solution, cellculture environment and other data values and information through the human-machine interface or the mobile phone APP., then, the system will conduct automated process to cell culture cells. The relevant cell image information and the environment data will be passed to the mobile APP by remote transmission, providing user observation and backup in the Firebase cloud and the external data storage hard disk. When the system determines that the cell growth area is full, the system will enter the change-disk program. After that, the system will continue observing and cellculturing. If the users wants to monitor cell growth situation, environmental conditions or want to change the system parameters and execution mode, they can use mobile phone APP to monitor and control. When the cell growth environment is abnormal or pollution occurs, the system will also provide instant notification via mobile APP and email. The data collected to the cloud will also be used for data analysis and prediction of cell growth trend in the future to achieve the concept of the Internet of Things, models.

摘要--------------------------I
Abstract---------------------III
致謝--------------------------VI
目錄--------------------------IX
圖目錄------------------------XIII
表目錄------------------------XVII
第一章 緒論----------------------1
1.1 前言---------------------1
1.2 研究動機-----------------2
第二章 文獻回顧------------------3
2.1細胞繼代培養--------------3
2.2物聯概念------------------6
2.3影像處理------------------9
2.3.1 數位影像---------------9
2.3.2 影像濾波---------------10
2.3.3 Otsu二值演算法---------11
2.3.4 數學形態學-------------12
2.3.4.1 膨脹(Dilation)-------13
2.3.4.2 侵蝕(Erosion)--------14
2.3.5 邊緣檢測---------------16
2.4商用自動化細胞培養系統-----20
第三章 研究方法-------------------26
3.1系統硬體區域--------------26
3.1.1機台架構----------------26
3.1.2作業系統----------------34
3.1.3感測器------------------36
3.1.3.1 溫濕度感測器----------37
3.1.3.2 二氧化碳感測器--------38
3.1.3.3 水位感測器------------39
3.1.4顯微鏡-------------------40
3.1.5步進馬達-----------------40
3.2系統軟體區域---------------43
3.2.1系統核心-----------------43
3.2.2 APP ( Android / iOS )---43
3.3細胞影像分析----------------45
3.3.1 滿盤預測-----------------45
3.3.2 汙染偵測-----------------46
第四章 結果與討論-------------------50
4.1 系統架設-------------------50
4.1.1 細胞培養區---------------50
4.1.2 氣壓夾爪區---------------53
4.1.3 觀測暫存區---------------54
4.1.4 培養皿空盤區-------------57
4.2 APP實作-------------------58
4.3 系統整合-------------------63
4.3.1 環境系統-----------------63
4.3.1.1 溫濕度-----------------63
4.3.1.2 二氧化碳---------------64
4.3.1.3 水位-------------------65
4.3.1.4 環境物聯網--------------66
4.3.2 流程監控-----------------67
4.3.3 細胞影像處理系統-----------68
4.3.3.1 細胞滿盤檢測------------68
4.3.3.2 細胞分離檢測------------73
4.3.3.3 黴菌污染檢測------------74
4.3.4 資料傳輸與備份------------80
4.3.4.1 雲端Firebase-----------81
4.3.4.2 外接數據儲存硬碟--------82
4.4 系統比較-------------------87
第五章 結論與展望--------------------87
5.1 研究貢獻-------------------87
5.2 未來展望-------------------88
參考文獻-----------------------90

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