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作者(中文):吳宥璇
作者(外文):Wu, You-Shiuan
論文名稱(中文):基於LoRaP2P澆灌系統之排程機制研製
論文名稱(外文):On the Design and Implementation of Scheduling Mechanism for LoRaP2P based Irrigation System
指導教授(中文):黃能富
指導教授(外文):HUANG, NEN-FU
口試委員(中文):陳俊良
張耀中
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學號:108062706
出版年(民國):111
畢業學年度:110
語文別:英文
論文頁數:94
中文關鍵詞:澆水排程React Native應用程式農業物聯網系統自動化控制系統
外文關鍵詞:Watering schedulingReact NativeApplicationAgricultural IoT SystemAutomatic Control System
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農業是國家發展、國人生活以及生態保育的根基。而澆灌是農業一項非常重要也必要的關鍵,因此如何幫助農夫達到省時省力的澆灌技術成了本研究努力的方向。在這篇論文當中,我們提出了一個架構於物聯網的澆水系統,其中的澆水排程演算法,可以幫助使用者只需要簡單的設定,就可以澆好幾公頃的土地,我們也使用了現在非常流行的開發平台-React Native來撰寫app,提供農夫六大功能來做使用。同時,我們也將系統放在實際農場測試,蒐集了3個半月的數據,結果顯示,APP的澆水排程都有成功的送出指令,提供了澆水狀態的即時顯示,也提供了應對臨時狀況的中斷功能,中途關閉其中一棚的澆水或是中途關閉整個排程。我們得出的結論是,我們的目標系統解決了以往農業種植相當重要的一環 – 澆灌所需要花費的大量時間以及人力成本問題,確實可以幫助農民更有效地澆水。
Farming has become a foundation to country development, citizens' living, and environment preservation. Amid all the factors, irrigation is a very important and very essential one. Therefore, to achieve an efficient irrigation technology that is less time-consuming and less labor-intensive for farmers, is the major direction of this study. In this thesis, we will purpose an irrigation system based on Internet of Things (IoT). The watering scheduling algorithm inside will irrigate several hectares of farmland in just a few steps. Now trending cross-platform programming platform - "React Native" is adopted in our application. The application provides six major functions for users to use. Meanwhile, we also have conducted field tests and experiments. With the nearly 100 days of data collected in the experiments, the results show our application has sent all watering commands successfully, is able to display real-time status of irrigation, and is capable of handling emergencies by shutting down a single shed or the entire schedule. Finally, we will reach the conclusion that our proposed system has solved the time-consuming and labor-intensive problem which would increase the costs of farming, and also that it indeed helps farmers to water much more efficiently.
Abstract I
中文摘要 II
Contents III
List of Figures V
List of Tables VIII
Chapter 1. Introduction 1
Chapter 2. Background and Related Work 7
2.1 Related Work 7
2.1.1 Irrigation Technology 7
2.1.1.1 Surface Irrigation 8
2.1.1.2 Micro-irrigation 10
2.1.1.3 Drip Irrigation 12
2.1.1.4 Sprinkler Irrigation 13
2.1.1.5 Section Conclusion 15
2.1.2 Agriculture IoT System 15
2.1.2.1 Aeon Matrix 15
2.1.2.2 AgriTalk 20
2.1.2.3 An IoT based smart irrigation management system using Machine learning and open source technologies 25
2.1.2.4 Section Conclusion 29
2.2 background 32
2.2.1 React Native 32
Chapter 3. System Design and Implementation 33
3.1 System Architecture 33
3.1.1 Mobile App and Website 34
3.1.2 API Server 34
3.1.3 Database 34
3.1.4 MQTT Broker 35
3.1.5 Rule Engine 36
3.1.6 LoRaP2P 36
3.1.7 Gateway and End Node (or Master and Slaves) 38
3.2 System Design - Watering Scheduling 38
3.3 System Implementation 45
3.3.1 Login/Logout System 45
3.3.2 Data Inquiry System 46
3.3.3 Data Comparison System 48
3.3.4 Control System 50
3.3.5 Watering Scheduling System 51
3.3.6 Agricultural Diary System 53
3.4 Acknowledgement Mechanism of Manual Control 56
3.5 Watering Scheduling 60
3.5.1 The Data Structure of Watering Scheduling 61
3.5.2 The Algorithm of Watering Scheduling 63
3.5.3 The Limitation of the Implementation of Watering Scheduling. 64
3.5.4 Backend Watering Scheduling 65
3.5.4.1 Backend Watering Scheduling – System Design 65
3.5.4.2 API for Backend Watering Scheduling 73
3.5.5 Comparison between Frontend and Backend Watering Scheduling 74
Chapter 4. Experiment and Results 76
4.1 Data Collection 76
4.1.1 Dapingwo Organic VegeTable Farm 76
4.1.2 Antin Organic VegeTable Farm 79
4.2 Experiment Results 80
4.2.1 The Reliability Test (Interruption Test) - Answer calls during the usage of the app and swipe to close the app 81
4.2.1.1 The Motivation and the Purpose of the test 81
4.2.1.2 Method of Testing 82
4.2.1.3 Experiment Results 83
4.2.2 Field Test 84
4.2.3 Users Feedback 87
Chapter 5. Conclusion and Future Work 89
5.1 Conclusion 89
5.2 Future Work 90
References 91

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