進階搜尋


 
系統識別號 U0026-2906201517503800
論文名稱(中文) 應用機器學習方法於作物種植條件之研究
論文名稱(英文) A Study on Cultivation Conditions of Crops by Applying Machine Learning Technigues
校院名稱 成功大學
系所名稱(中) 會計學系
系所名稱(英) Department of Accountancy
學年度 103
學期 2
出版年 104
研究生(中文) 陳孟萱
研究生(英文) Meng-Hsuan Chen
學號 R16011268
學位類別 碩士
語文別 中文
論文頁數 51頁
口試委員 指導教授-徐立群
口試委員-蕭鉢
口試委員-余政龍
中文關鍵字 機器學習  決策樹  農業大數據 
英文關鍵字 Machine learning  Decision tree  Big data of agriculture 
學科別分類
中文摘要 在全球氣候急速變遷與糧食逐漸短缺的衝擊下,如何在有限的耕種面積下有效地去提升作物的質與量是當今在農業領域上一個急需解決的問題。本研究基於此相關議題來探討關於作物之最佳化種植條件,藉由某作物產銷班所提供的種植履歷資料,利用決策樹分類模型來進行作物產量與質量的分析,進而歸納出最佳化的種植法則。經由此科學化方式所歸納出的法則提供給耕種者或農作物諮詢者參考,取代過去傳統農業僅以經驗法則來傳承種植方法的模式,期望能有效地提升作物的質量與統一管理之效率性。
英文摘要 Due to the rapid changes in the global climate and food shortages increasingly, how to effectively improve the yield and quality of crops is an urgent problem in the field of agriculture today. Based on these situations, this thesis discusses a related issue about the optimal cultivation conditions of crops by cultivation records provided by an agriculture manufacturer and use the decision tree to analyze the yield and quality of crops, and then summarize the best of the planting rules. Through this scientific manner to generalize the planting rules can provide to the cultivators or agricultural planners references, replace the previous mode that only passed on the cultivation methods by the rules of thumb, and it is anticipated that it can effectively improve the yield and quantity of crops and enhance the efficiency of unified management.
論文目次 第一章、緒論…………………………………………………………………………1
第一節、研究背景與動機……………………………………………………1
第二節、研究目的…………………………………………………………………2
第三節、研究限制…………………………………………………………………3
第四節、研究貢獻…………………………………………………………………3
第五節、論文架構…………………………………………………………………4
第二章、文獻探討……………………………………………………………………6
第一節、精準農業…………………………………………………………………6
第二節、農業大數據………………………………………………………………8
第三節、機器學習方法………………………………………………………… 12
第四節、監督式機器學習方法應用於農作物領域之研究探討……………… 13
第三章、研究方法………………………………………………………………… 15
第一節、研究架構……………………………………………………………… 15
第二節、資料描述……………………………………………………………… 16
第三節、資料前處理…………………………………………………………… 16
第四節、研究技術……………………………………………………………… 18
第四章、實證結果與分析………………………………………………………… 25
第一節、建立模型前之初步分析……………………………………………… 25
第二節、依據三大影響因素細部歸納出分類規則…………………………… 28
第三節、決策樹與其它三種機器學習方法之模型比較……………………… 33
第五章、結論與未來研究方向…………………………………………………… 36
參考文獻…………………………………………………………………………… 38
參考文獻 江世傑(2001),模糊類神經網路在消費性貸款之應用,國立成功大學工業管理學系碩士論文
行政院農業委員會甘藷主題館(2011),一步一腳印(沿革) http://kmweb.coa.gov.tw/subject/ct.asp?xItem=107180&ctNode=3050&mp=190&kpi=0&hashid
周歆凱,張怡秋,黃興進,蔡明足,翁林仲,蘇喜. (2007),運用〝購物籃分析技術〞探討滯留急診超過 24 小時病患特性. 醫務管理期刊, 8(3), 216-231.
林慧貞(2015),大旱來襲,農試所研究員籲建立農業氣象的big data 適時適地適種 http://www.newsmarket.com.tw/blog/63520/
陳文德(2000),我國精準農業的發展方向與策略 http://210.69.150.18:8080/handle/345210000/6648
陳來成(2002),應用資料探勘技術建立商業預測模式─以信用卡為例,元智大學資訊管理學系學位論文, 1-50.
培養開放心態,迎接大數據時代(2014),Inside科技網誌http://www.inside.com.tw/2014/06/12/big-data-coauthor-victor-mayer-schonberger-first-visits-taiwan
軟雲(2014),大數據,為農業插上〝數字〞翅膀http://www.ruanyun.net/news/ryyc/n166.aspx
楊純明(1999),發展精準農業之淺見 http://210.69.150.18:8080/handle/345210000/2075
楊純明(2008),精準農業-引領農業永續之正途http://210.69.150.18:8080/handle/345210000/5902
楊昌儒(2014),大數據時代的綠色商機http://www.phycos.com.tw/index.php/tw/phycos-focus/agriculture/195-agricultural-big-data
Big Data defintion, Gartner, Inc. http://www.gartner.com/it-glossary/big-data/
Brause, R., Langsdorf, T., & Hepp, M. (1999). Neural data mining for credit card fraud detection. In Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on (pp. 103-106). IEEE.
Bin, L., Peiji, S., & Juan, L. (2007, June). Customer churn prediction based on the decision tree in personal handyphone system service. In Service Systems and Service Management, 2007 International Conference on (pp. 1-5). IEEE.
Chan, P. K., Fan, W., Prodromidis, A. L., & Stolfo, S. J. (1999). Distributed data mining in credit card fraud detection. Intelligent Systems and their Applications, IEEE, 14(6), 67-74.
Camps-Valls, G., Gómez-Chova, L., Calpe-Maravilla, J., Soria-Olivas, E., Martín-Guerrero, J. D., & Moreno, J. (2003). Support vector machines for crop classification using hyperspectral data. In Pattern recognition and image analysis (pp. 134-141). Springer Berlin Heidelberg.
Chandar, M., Laha, A., & Krishna, P. (2006). Modeling churn behavior of bank customers using predictive data mining techniques. In National conference on soft computing techniques for engineering applications (SCT-2006) (pp. 24-26).
Chen, J. W. (2006). A SVM Face Recognition Method Based on Gabor Feature Extraction.
Demchenko, Y., Ngo, C., & Membrey, P. (2013). Architecture framework and components for the big data ecosystem. Journal of System and Network Engineering, 1-31.
Edmond, J. B., & Ammerman, G. R. (1971). Sweet potatoes: Production, processing, marketing. AVI Publ. Co., Westport, CT.
Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with applications, 29(4), 927-940.
Gonzalez-Sanchez, A., Frausto-Solis, J., & Ojeda-Bustamante, W. (2014). Predictive ability of machine learning methods for massive crop yield prediction. Spanish Journal of Agricultural Research, 12(2), 313-328.
Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecom churn management. Expert Systems with Applications, 31(3), 515-524.
Huang, B., Kechadi, M. T., & Buckley, B. (2012). Customer churn prediction in telecommunications. Expert Systems with Applications, 39(1), 1414-1425.
IDC (2012), Big data : Trends,Strategies,and SAP Technology https://www.sap.com/bin/sapcom/en_ae/downloadasset.2012-09-sep-26-13.idc-report--big-data-trends-strategies-and-sap-technology-pdf.html
Kusiak, A., Zheng, H., & Song, Z. (2009). Short-term prediction of wind farm power: a data mining approach. Energy Conversion, IEEE Transactions on,24(1), 125-136.
Lim, T. S., Loh, W. Y., & Shih, Y. S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine learning, 40(3), 203-228.
Mingers, J. (1987). Expert systems-rule induction with statistical data. Journal of the operational research society, 39-47.
McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7-23.
Phillips, P. J. (1998). Support vector machines applied to face recognition (Vol. 285). US Department of Commerce, Technology Administration, National Institute of Standards and Technology.
Provost, F., & Kolluri, V. (1999). A survey of methods for scaling up inductive algorithms. Data mining and knowledge discovery, 3(2), 131-169.
Park, S. J., Hwang, C. S., & Vlek, P. L. G. (2005). Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agricultural Systems, 85(1), 59-81.
Reed, R. (1993). Pruning algorithms-a survey. Neural Networks, IEEE Transactions on, 4(5), 740-747.
Robert, P. C. (1993). Characterisation of soil conditions at the field level for soil specific management. Geoderma 60, 57–72.
Robert, P. C. (1997). Remote sensing:A potential powerful technique for precision agriculture,land satellite information in the next decade II.December 4,1997.Washinglon D.C.
Raorane, A. A., Kulkarni, R. V., & Jitkar, B. D. (2012). Association rule–Extracting knowledge using market basket analysis. Research journal of recent sciences. ISSN,2277, 2502.
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis:An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43-48.
Svetlana Sicular (2013). Gartner's Big Data Definition Consists of Three Parts, Not to Be Confused with Three "V"s, Gartner, Inc. 27 March 2013. http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists-of-three-parts-not-to-be-confused-with-three-vs/
Tsang, K. F., Lau, H. C. W., & Kwok, S. K. (2007). Development of a data mining system for continual process quality improvement. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(2), 179-193.
Villordon, A. Q., La Bonte, D. R., Firon, N., Kfir, Y., Pressman, E., & Schwartz, A. (2009). Characterization of adventitious root development in sweetpotato. HortScience, 44(3), 651-655.
Villordon, A., Clark, C., Smith, T., Ferrin, D., & LaBonte, D. (2010). Combining linear regression and machine learning approaches to identify consensus variables related to optimum sweetpotato transplanting date. HortScience,45(4), 684-686.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2020-07-02起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2020-07-02起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw