蝴蝶蘭為近年來最受歡迎的花卉之一，台灣為全球蝴蝶蘭苗最大的產銷國，為了使得蝴蝶蘭生產作業邁向自動化，提升台灣蝴蝶蘭產業的競爭力，本研究應用機器視覺建立蝴蝶蘭大苗選別系統，該系統包括幾何特徵之估算模組、病害檢測模組、選別機構等。為了建立完整系統，相關的研究範圍包括以下四部分 :
1、建立蝴蝶蘭大苗幾何特徵估算法則
（1）利用影像處理技術，建立估算蝴蝶蘭大苗幾何特徵之演算法則。
（2）撰寫幾何特徵之估算程式。
2、建立蝴蝶蘭病害檢測法則
（1）利用影像處理技術，建立蝴蝶蘭病害檢測法則。
（2）撰寫病害檢測程式。
3、建立蝴蝶蘭大苗選別系統
（1）設計製造蝴蝶蘭大苗選別機構。
（2）建立選別控制系統，整合影像處理及控制大苗選別系統程式。
4、建立蝴蝶蘭大苗選別系統軟體
（1）建立影像處理函式庫。
（2）撰寫使用者指南。
（3）撰寫指令參考資料。
本論文應用影像處理技術，建立蝴蝶蘭苗幾何特徵估算法則，包括苗莖中心與葉片端點搜尋法、葉片數估算法、苗盆與葉片分離法及葉片輪廓萃取法。針對44株蝴蝶蘭大苗進行估算試驗，同時以人工方式進行量測，人工量測所得之數據為正確值，針對估算的結果與實際量測結果進行比較，其中以葉片數之估算最為準確，其平均相對誤差為1.48%，其它特徵值的平均相對誤差分別為葉長為1.80%、葉幅為2.44%、葉片夾角為3.90%、葉寬為4.02%及葉片長寬比為7.04%。
本論文提出蝴蝶蘭病害檢測法則針對蝴蝶蘭之軟腐病、褐斑病及疫病病害進行檢測與分類。首先利用Rayleigh轉換與影像處理技術萃取蝴蝶蘭病害區域之特徵，這些特徵包括形心座標、面積、周長、平均直徑及平均灰階值 、 、 ，再進一步利用檢測線法則估算病害區域之最大灰階平均值 與灰階值陡峭係數 ，最後以 、 、 、 及 為特徵向量，利用貝氏分類法求得決策邊界 、 、 、 及 ，以這些決策邊界為判斷依據，針對軟腐病、褐斑病、疫病害區域進行檢測與分類。針對144個病害測試樣本進行檢測與分類，平均分類正確率為88.2%，每一株大苗的平均處理速度為1.78 sec。若僅考慮是否檢測出病害，而不論其屬於何種病害，則本系統之病害檢測能力可達到96.5%。
本論文完成蝴蝶蘭大苗選別系統之研製，該系統主要由四部份組成：(1)取像定位機構、(2)影像辨識系統、(3)分級顯示裝置、(4)選別控制系統。此選別系統作業方式係將蝴蝶蘭大苗藉由置料平台與升降機構送至取像平台，利用影像辨識系統估算蝴蝶蘭大苗的幾何特徵及檢測病害，並根據台糖外銷蝴蝶蘭大苗的選別標準進行品質選別。針對430株蝴蝶蘭大苗進行選別試驗，由試驗結果顯示，機器選別的正確率為90.0 %，每一株蝴蝶蘭大苗的平均選別作業時間為21.15 sec；人工選別的正確率則為97.2 %，每一株蝴蝶蘭大苗的平均選別作業時間為27.42 sec；機器選別的速度較人工選別約快22.3 %。
本論文完成蝴蝶蘭大苗選別系統軟體SSPS 1.0之開發，並建立影像程式庫，針對每一個副程式及函式的功能進行說明，可提供使用者參考。

Phalaenopsis is getting popular recently. In order to ensure competition in world market for Taiwanese floristic industry, an automatic production line is a key factor. In this study we used the image processing techniques to develop a sorting system for Phalaenopsis seedlings. The sorting system consists of the module of estimation of the geometric characteristics, the module of diseases detection, and the sorting machine. The scope of this research to develop the whole system includes:
1. Developing an algorithm for estimating characteristics of Phalaenopsis seedlings: The algorithm is developed to estimate the geometric characteristics of Phalaenopsis seedlings using the image processing techniques.
2. Developing an algorithm for detecting diseases of Phalaenopsis seedlings: The disease detection algorithm is established to detect bacterial soft rot (BSR), bacterial brown spot (BBS), and Phytophthora black rot (PBR) using image processing techniques.
3. Developing a sorting system: A sorting system for Phalaenopsis seedlings was designed and manufactured. The sorting system is composed of the sorting mechanism and control system.
4. Developing a software for the sorting system: The software includes: (1) image processing functions, (2) user guide, and (3) command reference.
A methodology using machine vision to estimate the geometric characteristics of Phalaenopsis seedlings was established in this paper. The image processing techniques including the stem-center search method, the leaf-endpoint search method, the leaf number search method, the pot removing method, and the leaf-shape extraction procedure were applied to develop the algorithms that were used to estimate the geometric characteristics. Forty-four samples were investigated. Measurements taken manually and from estimation using our method were obtained and compared. The average relative errors between estimated values and measured results were 1.48% for the total number of leaves, 1.80% for the length, 2.44% and 3.90% for the span and the angle between two upper leaves, 4.02% for the width, and 7.04% for the length/width ratio.
A novel system for detecting and classifying Phalaenopsis seedling diseases, including BSR, BBS, and PBR, was developed. The features of the lesion area of a Phalaenopsis seedling were extracted by Rayleigh transform and image processing techniques, such as hole-filling, erosion, dilation, opening, and closing operators. The detection line algorithm (DLA) was used to evaluate the lesion area. Five color features - Rmean, Gmean, Bmean, Gmax, and M were used in the classification procedure. A Bayes classifier was applied to classify BSR, BBS, and PBR of Phalaenopsis seedlings. One hundred and forty-four samples were used to evaluate the system. The methodology rapidly detected and classified these three Phalaenopsis seedlings diseases, at 1.78 sec/pot, to an accuracy of 88.2%. The disease detection capability of the system, without classifying the disease type, was as high as 96.5%.
A sorting system for Phalaenopsis seedlings was designed and manufactured. This sorting system consists of four major parts: (1) image grabbing and positioning mechanism, (2) pattern recognition system, (3) display panel, and (4) control system. Four hundred and thirty pots of Phalaenopsis seedlings were used to test the sorting system. According to the results, we were able to achieve a rapid sorting of 21.15 sec/pot compared to 27.42 sec/pot by manual sorting, to an accuracy of 90.0% compared to 97.2% when sorting manually. Our machine can save up to 22.3% of the time used for manual sorting.
The sorting system for Phalaenopsis seedling (SSPS) software 1.0 was developed. The SSPS 1.0 library had been established. The sub-programs and functions were described in the SSPS 1.0 reference manual.

中文摘要……………………………………………………………………………....I
英文摘要……………………………………………………………...………...……III
目錄……………………………………….…………………………………………..V
表目錄……………………………………………………………………………….IX
圖目錄……………………………………….……………………………..…..……XI
符號表……………………………………………………………………………....XV
第一章 前言
1.1 研究動機與目的………………………………………………………1
1.2 論文內容與結構………………………………………………………3
第二章 研究背景
2.1 蝴蝶蘭概述……………………………………………………………5
2.2 蝴蝶蘭產業現況………………………………………………………5
2.3 蝴蝶蘭大苗幾何特徵之選別標準……………………………………7
2.4 蝴蝶蘭病害……………………………………………………………9
2.5 影像處理在農業上之應用…………………………………………..11
2.6 直方圖等化法與貝氏分類法之應用………………………………..14
第三章 基本理論分析
3.1 數位影像與其描述方法……………………………………………..17
3.2 影像處理之基本運算………………………………………………..20
3.3 貝氏分類法…………………………………………………………..25
第四章 蝴蝶蘭大苗幾何特徵之估算法則
4.1 試驗材料與設備……………………………………………………..27
4.1.1 試驗材料…………………………………………………..27
4.1.2 試驗設備…………………………………………………..27
4.2 前視影像處理………………………………………………………..29
4.2.1 苗莖中心搜尋法…………………………………………..29
4.2.2 葉片端點搜尋法…………………………………………..34
4.2.3 葉片數估算法……………………………………………..37
4.3 上視影像處理………………………………………………………..39
4.3.1 分離苗盆與葉片…………………………………………..39
4.3.2 萃取葉片輪廓……………………………………………..47
第五章 蝴蝶蘭病害檢測與分類系統
5.1 試驗材料與設備……………………………………………………..48
5.1.1 試驗材料…………………………………………………..48
5.1.2 試驗設備…………………………………………………..48
5.2 病害之培養與接種…………………………………………………..52
5.3 萃取病害區域…………………………………………………….….56
5.4 檢測線法則……………………………………………………….….61
5.5 以貝氏分類法進行病害分類…………………………………….….69
5.6 蝴蝶蘭病害檢測與分類法則………………………………………..70
第六章 蝴蝶蘭大苗選別系統
6.1 試驗材料………………………..……………………………….…...72
6.2 選別機構之研製………………..……………………………….…...72
6.2.1 取像定位機構…………………..……………….…….…..75
6.2.2 影像辨識裝置與分級顯示裝置..………………….….…..78
6.3 選別控制系統之建立……………………..……………………..…..80
6.3.1 試驗設備………………………..……………………..…..80
6.3.2 系統控制電路…………………..…………………..……..81
6.3.3 系統控制程式…………………..………………….….…..83
第七章 蝴蝶蘭大苗選別系統軟體
7.1 選別軟體之設計……………………….…………….………..……..88
7.2 使用者介面………………………………………………..…….…...93
7.2.1 監控介面與執行按鈕之功能………….……………….93
7.2.2 幾何特徵估算模組………………………...…………...96
7.2.3 病害檢測與分類模組…………………………………..99
7.2.4 系統控制模組……….………………………………...101
7.2.5 選別系統之作業流程…………………………………103
7.3 影像處理程式與函式庫………………………………………104
7.4 SSPS 1.0安裝程式之製作……......…………….………….....106
第八章 結果與討論
8.1 打光與影像校正………………………….………………………...107
8.2 蝴蝶蘭大苗幾何特徵之估算…………………………………...….111
8.3 蝴蝶蘭病害之檢測………………………………………………....127
8.3.1 蝴蝶蘭病害培養與接種樣本……………………………127
8.3.2 病害檢測與分類系統……………………………………129
8.3.3 病害檢測與分類結果……………………………………137
8.3.4 以機器視覺與人工方式檢測病害之比較…………..…..139
8.4 蝴蝶蘭大苗選別系統………..………………………………….….140
8.5 影像經校正與未校正處理之比較…………………………………148
8.6 蝴蝶蘭大苗選別系統軟體…………………………………….…...159
第九章 結論與未來展望
9.1 結論…………………………………………………………………160
9.2 未來展望…………………………………………………………....162
參考文獻……………………………………………………………………………163
附錄A 區間對分法………………………………………………………………168
附錄B 控制系統之硬體規格……………………………………………………170
附錄C 蝴蝶蘭大苗選別系統程式………………………………………………179
附錄D 蝴蝶蘭大苗選別系統軟體SSPS 1.0之函式參考手冊………………...225
D.1 影像處理基本設定模組…………………..……………………....225
D.2 病害檢測與分類模組……...………..………………………….…231
D.3 幾何特徵估算模組……..…..………………………………….….237
D.4 系統控制模組……..………………………………………….…...247
附錄E MIL 6.0函式功能及說明..………………………………………...…….253
E.1 MIL影像系統之配置與釋放……………….…….……………….253
E.2 RGB彩色影像處理…………………………………….………….255
E.3 MIL 基本功能介紹…………...………….………………….…….256
E.4 Blob分析模組…………………………….………………..………258
附錄F 蝴蝶蘭大苗選別系統軟體SSPS 1.0之安裝程序………………………259
附錄G 蝴蝶蘭大苗選別結果…………………………………………….………262

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