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博碩士論文 etd-0724122-133301 詳細資訊
Title page for etd-0724122-133301
Bactrocera Dorsalis Hendel Control and Analysis based on Mixed-effects Representation Learning
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Bactrocera Dorsalis, Linear Mixed-effect, Decision Tree, Random Forest, XGBoost, Artificial Neural Network
本論文已被瀏覽 416 次,被下載 85
The thesis/dissertation has been browsed 416 times, has been downloaded 85 times.
The Bactrocera dorsalis hendel has always been the most troublesome pest for fruit farmers. It causes fresh fruit to rot and seriously affects the economic value of fresh fruit. In the past, to prevent such a situation, many pesticides are sprayed by fruit farmers to control pests, causing pesticide residues, endangering the health of consumers, and producing drug resistance, which results in a three-lose situation for farmers, consumers and land.
In the past, the meteorological factors for Bactrocera dorsalis hendel that may have an impact, such as temperature, humidity, and rainfall, were mostly analyzed by simple regression, without considering different spaces (farmland) and time (long-term repeatability). Given that machine learning has become a common method for data analysis, its main concept is to let the machine learn by itself and find the correlation between variables from input data, and then generate data that match reality. Therefore, in this study, the linear mixed-effects model tree, decision tree, random forest, XGBoost and fully connected neural network are used to build models for prediction and interpretation.
Finally, through the models proposed in this study, it is expected that farmers can make corresponding field management strategies in their farmland through the most important meteorological factors such as temperature and rainfall.
目次 Table of Contents
論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 viii
第一章 緒 論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
第二章 文獻探討 3
2.1 相依資料與時間序列 3
2.2多層次嵌套資料 4
2.3 機器學習演算法 6
2.4 交叉驗證(Cross validation) 8
2.5 Prequential 9
第三章 研究方法與步驟 10
3.1 研究流程 11
3.2 研究方法 11
3.3 評估模型標準 13
第四章 研究結果與分析 14
4.1資料蒐集 14
4.2資料清理 14
4.3實驗設計 14
4.4 建立模型 16
4.5 評估模型 18
4.6模型解釋 19
4.7 Ensemble Model Selection 23
第五章 討論與建議 24
5.1 研究結論 24
5.2 未來建議 25
5.3 研究限制 27
參考文獻 27
參考文獻 References
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