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博碩士論文 etd-0724122-133301 詳細資訊
Title page for etd-0724122-133301
論文名稱
Title
基於混合效果特徵學習演算法的東方果實蠅的防治分析
Bactrocera Dorsalis Hendel Control and Analysis based on Mixed-effects Representation Learning
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
39
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2022-07-29
繳交日期
Date of Submission
2022-08-24
關鍵字
Keywords
東方果實蠅、線性混合效果模型(樹)、決策樹、隨機森林、極限梯度提升、神經網路
Bactrocera Dorsalis, Linear Mixed-effect, Decision Tree, Random Forest, XGBoost, Artificial Neural Network
統計
Statistics
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.
中文摘要
東方果實蠅一直以來是令果農感到頭痛的害蟲,其危害之後造成的鮮果腐爛,嚴重影響鮮果的經濟價值,過去為預防這樣的情況發生,果農普遍採用噴灑殺蟲劑來防治蟲害,但是這種撲殺的方式,不但會造成藥劑殘留,危害消費者健康,若長期過量使用,也會產生抗藥性,危害農民健康,對於我們生存的環境也會造成嚴重汙染,形成農民、消費者和土地三輸的局面。
過去對於東方果實蠅可能造成影響的氣象因子,如溫度、濕度和雨量,大多採用簡單線性迴歸分析,對於不同空間(農田)及時間(長期重複監測)因素所造成的影響並未加以考慮。有鑑於機器學習逐漸成為資料分析常用的方法,其主要基本概念是讓機器自行學習,從輸入的資料當中找出變數之間的關聯性,並生成貼近現實的資料,所以本研究分別使用線性混合效果模型(樹)、決策樹、隨機森林、極限梯度提升以及深度學習的全連接神經網路等演算法,試圖找出預測最準(誤差最小)及解釋性佳的預測模型。
最後,期望透過本研究建立的模型,對於重要氣象影響因子如溫度和雨量進行分析,提供農民參考並做出相對應的田間管理措施預作防範。
Abstract
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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