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    Title: 物聯網應用於農業種植決策系統之研究 -以台灣甜玉米品質躍升計畫為例
    Research on the application of the Internet of Things to the agricultural planting decision-making system -Taking Taiwan’s sweet corn quality jump plan as an example
    Authors: 黃世貴
    Huang, Shih-Kuei
    Contributors: 詹文男
    尚孝純

    Tsan, Wen-Nan
    Shang, Shari S. C

    黃世貴
    Huang, Shih-Kuei
    Keywords: 物聯網
    大數據
    決策系統
    智慧農業
    田間感測
    Internet of Things
    big data
    decision-making systems
    smart agriculture
    field sensing
    Date: 2021
    Issue Date: 2021-09-02 16:29:29 (UTC+8)
    Abstract: 本次研究的目的主要為探討目前物聯網建置之感測單元用於農業的技術可行性,並設計田間感測模組連接物聯網資料收集之應用架構,來進行數據分析應用系統建置,同時使用專案計畫所獲得之資訊,透過數據收集來判斷生產之決策系統可行性,並提出建議方式及供給未來其他使用者之建置經驗。並探討以物聯網技術透過物聯網感測單元所獲得之數據,建構一套目前適合應用於農業的物種種植之決策系統技術,佈設於分散式玉米田間以此決策技術系統來精進農業生產技術。
    本研究之研究目的主要為:
    一、探討目前物聯網建置之感測單元用於農業的技術可行性,並設計田間感測模組連接物聯網資料收集之應用架構,來進行數據分析應用系統建置,同時使用專案計畫所獲得之資訊,透過數據收集來判斷生產之決策系統可行性。二、提出物聯網農業決策系統及數據來源方式之建議供給未來其他使用者之建置參考。
    透過物聯網設施佈建,能否為農業生產決策系統發揮作物更大潛能,創造安全又便利的從農環境,吸引更多年輕人力投入,使臺灣農業邁向年輕化、有活力、高競爭力的精緻農業,提供穩定、生鮮、安全糧食。
    以筆者多年在物聯網建置設備實務經驗,提供本研究在建置應用技術上一份實際的使用經驗,並透過實際的設備建置過程及數據收集的歷程,完成建置及收集農業田間運作資訊,並將此資訊提供至農業種植決策系統;透過此建置資訊資料進行農業種植決策系統規劃及設計建置,並以物聯網技術收集田間農業運作資訊,完成精進農業種植決策系統,提供規劃及建議方式,最為未來業界之參考。
    The purpose of this research is to explore the technical feasibility of the current sensor unit built by the Internet of Things for agriculture, and to design an application framework for field sensor modules to connect to the Internet of Things data collection to build a data analysis application system. At the same time, use the information obtained from the project plan to determine the feasibility of the production decision-making system through data collection, and propose ways to suggest and provide other users with the construction experience in the future. It also explores the use of the Internet of Things technology to obtain data through the Internet of Things sensing unit to construct a set of decision-making system technology suitable for species planting in agriculture, and deploy it in a distributed corn field to improve agricultural production technology with this decision-making technology system.
    The main research objectives of this study are:
    1. Discuss the technical feasibility of the current sensor unit built by the Internet of Things to be used in agriculture, and design the application framework of the field sensor module connected to the Internet of Things data collection to implement the data analysis application system construction, and use the project plan at the same time The information obtained is used to determine the feasibility of the production decision-making system through data collection. 2. Propose suggestions for the Internet of Things agricultural decision-making system and data source methods to provide future reference for other users` construction.
    Through the deployment of Internet of Things facilities, can the agricultural production decision-making system realize the greater potential of crops, create a safe and convenient farming environment, attract more young people to invest, and make Taiwan`s agriculture more youthful, vigorous, and highly competitive The refined agriculture provides stable, fresh and safe food.
    Based on the author`s many years of practical experience in the construction of equipment in the Internet of Things, I will provide a practical experience in the application of this research in the construction and application of technology, and complete the construction and collection of agricultural field operations through the actual equipment construction process and data collection process Information, and provide this information to the agricultural planting decision-making system; through the establishment of information data for agricultural planting decision-making system planning, design and construction, and the Internet of Things technology to collect field agricultural operation information, complete the refined agricultural planting decision-making system, and provide planning And the suggested method is the most reference for the industry in the future.
    Reference: 一、中文文獻
    1.行政院農業委員會農業試驗所智慧農業專案小組,智慧農業說明((https://www.intelligentagri.com.tw/xmdoc/cont?xsmsid=0J164373919378174143)
    2.余祈暐。2017。國際智慧農業發展策略。台灣經濟研究月刊 第 40 卷第 3 期: 21-30。
    3.低功耗廣域網路說明,引用維基百科 https://zh.wikipedia.org/wiki/LPWAN
    4.物聯網名詞解釋說明,引用維基百科(https://zh.wikipedia.org/wiki/%E7%89%A9%E8%81%94%E7%BD%91)
    5.陳駿季、楊智凱。2017。推動智慧農業-翻轉臺灣農業。國土及公共治理季刊 第 5 卷第 4 期: 104-111。
    6.植生指標之單/雙影像模組發展 科儀新知 220 期 108.9 廖泰杉 著
    7.無人飛行載具說明,引用維基百科,https://zh.wikipedia.org/wiki/%E7%84%A1%E4%BA%BA%E8%88%AA%E7%A9%BA%E8%BC%89%E5%85%B7
    8.楊智凱、施瑩艷、楊舒涵。2016。以智慧科技邁向臺灣農業 4.0 時代。農政與農 情 289:6-11。
    9.楊織郡。2017。科技革新打造智慧農業-專訪農業委員會科技處處長張致盛。國際 農業科技新知 No.75: 4-8。

    二、英文文獻
    1.Advantech Technical Writers (2013). IoT and Big Data Combine Forces. Advantech Technical Whitepaper.
    2.Andrew Meola (2016). Why IoT, big data & smart farming are the future of agriculture. Business Insider: http://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10
    3.Ankur Pariyani, PhD; Ulku G. Oktem, PhD; Deborah L. Grubbe, PE.(2013).Process Risk Assessment Uses Big Data., Control Engineering:http://www.controleng.com/single-article/process-risk-assessment-uses-big-data/632b3ce8d25102b9ab558b3833cc5885.html
    4.Carlson, Toby N., and David A. Ripley. "On the relation between NDVI, fractional vegetation cover, and leaf area index." Remote sensing of Environment 62.3 (1997): 241-252.
    5.Faiçal, Bruno S., et al. "Exploiting Evolution on UAV Control Rules for Spraying Pesticides on Crop Fields." International Conference on Engineering Applications of Neural Networks.Springer International Publishing, 2014.
    6.Faiçal, Bruno S., et al. "Fine-Tuning of UAV Control Rules for Spraying Pesticides on Crop Fields: An Approach for Dynamic Environments." International Journal on Artificial Intelligence Tools 25.01 (2016): 1660003.
    7.Gay, Alan P., et al. "Developing unmanned aerial vehicles for local and flexible environmental and agricultural monitoring." Proceedings of RSPSoc 2009 Annual Conference. RSPSoc. 2009.
    8.Goncalves, Leandro Bertini Lara, et al. "Influence of mobility models in precision spray aided by wireless sensor networks." Journal of Physics: Conference Series. Vol. 574. No. 1. IOP Publishing, 2015.
    9.Greg McMillan and Stan Weiner (2010). Drowning in Data, Starving for Information-2. Control: http://www.controlglobal.com/articles/2010/AutomationData1003.html
    10.Huang, Yanbo, and Krishna Reddy. "Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management." Meeting Proceedings. Vol. 1. 2015.
    11.Joe Feeley (2013). `Internet of Things` Becomes Internet of Everything. Control Design: http://www.controldesign.com/articles/2013/feeley-gigabytes-how-quaint.html
    Description: 碩士
    國立政治大學
    經營管理碩士學程(EMBA)
    108932086
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108932086
    Data Type: thesis
    DOI: 10.6814/NCCU202101052
    Appears in Collections:[Executive Master of Business Administration] Theses

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