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研究生:周奕
研究生(外文):Joseph E. Chou
論文名稱:影像辨識蜜蜂幼蟲及蜂王漿採收自動化之研究
論文名稱(外文):The Study on Image Recognition of Honeybee Larvae and Automation of Royal Jelly Harvester
指導教授:艾群黃膺任楊朝旺楊朝旺引用關係
指導教授(外文):Chyung AyYin-Jen HuangChao-Wang Young
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:生物機電工程學系研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
畢業學年度:103
語文別:中文
論文頁數:83
中文關鍵詞:蜜蜂幼蟲影像辨識自動化取漿自動化移蟲
外文關鍵詞:Honeybee LarvaeImage RecognitionAutomatic HarvestingAutomatic Removing
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蜂王漿是具有高營養價值的天然食品,但是近年來產量逐漸降低,因蜂王漿的採收均依賴人工進行,且採收流程相當耗費時間與農村勞力老化。若能將蜂王漿的採收流程自動化,將有助於提升其產量以及降低人力的負擔。機器視覺系統已經被廣泛的運用在各種領域中。本研究利用機器視覺來探討蜜蜂幼蟲識別過程的可行性,其方法為辨識王台杯內的蜜蜂幼蟲,使用50個蜜蜂幼蟲的影像樣本,撰寫演算法來計算其RGB的平均值,用於建立一個球體於整張影像的RGB色彩空間中,抓取目標物。從每個標記區域高於1000像素,合成該影像,並使用紅色十字來標註質心位置及中值濾波器來降低二值化的影像中的雜訊。實驗結果發現,此成功率為92.13%。本研究中,並開發出一個自動化移蟲取漿原型機,於王台杯內利用自製夾具移除蜜蜂幼蟲,並以真空的方式自動收集蜂王漿。其中取蟲裝置的成功率為 89.56%,取漿裝置的成功率為100%。
Royal jelly is considered as a high-valued nutritious of natural food product. The royal jelly production was still relied on much labor harvesting now. Automation is one way to reduce the processing time and human labor. Machine vision system has been used in a variety of applications on industry. This study investigates the possibility of the honeybee larvae identification process using machine vision and automatic harvest. An algorithm is developed to locate honeybee larvae in queen cells. A template was created which contained 50 images of honeybee larvae’s partial region. An algorithm takes the average value of red, green, and blue plane creates a ball-shape to capture the desired target. In order to reduce the noise, a median filter was applied to the binary image. The synthesized image is obtained from each labeled region which above 1000 pixels and marked the centroids from the coordinated regions in each synthesized image. Tests showed that the successful rate for the algorithm was 92.13%. In addition, an automatic harvesting machine is developed in this study. First of all, the honeybee larvae were removed from the queen cells. Then, collected the royal jelly with vacuum sucking. The study has shown that successful rates of gripper and royal jelly harvesting were 89.56% and 100% individually.
摘要 ..................................................................................................................... I
Abstract ............................................................................................................... II
致謝 .................................................................................................................. III
目錄 .................................................................................................................. IV
圖目錄 ............................................................................................................. VII
表目錄 ............................................................................................................... X
符號目錄 .......................................................................................................... XI
第一章 前言 ...................................................................................................... 1
1.1 研究背景 .............................................................................................. 1
1.2 研究動機 .............................................................................................. 1
1.3 研究目的 .............................................................................................. 2
第二章 文獻探討 .............................................................................................. 3
2.1 蜂王漿 .................................................................................................. 3
2.2 人工採收蜂王漿的流程 ...................................................................... 4
2.3 離心式蜂王漿採收機之操作步驟 ...................................................... 7
2.4 不同採收方法之蜂王漿品質比較 .................................................... 12
2.5 影像處理應用 .................................................................................... 14
第三章 理論基礎 ............................................................................................ 16
3.1 影像處理 ............................................................................................ 16
3.1.1 彩色影像分割 .......................................................................... 16
3.1.2 中值濾波器 .............................................................................. 17
3.1.3 影像標記法 .............................................................................. 18
3.2 機器手臂 ............................................................................................ 19
第四章 材料與方法 ........................................................................................ 23
4.1 影像辨識 ............................................................................................ 23
4.1.1 實驗設備 .................................................................................. 23
4.1.2 實驗材料 .................................................................................. 24
4.1.3 辨識率的計算 .......................................................................... 25
4.1.4 影像處理 .................................................................................. 26
4.2 蜂王漿自動化採收機 ........................................................................ 31
4.2.1 實驗設備 .................................................................................. 34
4.2.2 機器手臂系統 .......................................................................... 36
4.2.3 取蟲裝置及取漿裝置系統 ...................................................... 37
4.2.4 蜂王漿自動化採收機的整體動作流程 .................................. 39
第五章 結果與討論 ........................................................................................ 44
5.1 機器視覺對蜜蜂幼蟲的辨識 ............................................................ 44
5.2 機器手臂的移動精度 ........................................................................ 48
5.3 取蟲夾取成功率 ................................................................................ 52
5.4 取漿成功率 ........................................................................................ 53
第六章 結論與建議 ........................................................................................ 55
6.1 結論 .................................................................................................... 55
6.2 建議 .................................................................................................... 56
參考文獻 .......................................................................................................... 57
附錄一 伺服控制器之使用說明 .................................................................... 61
附錄二 王台條範本 ........................................................................................ 63
附錄三 王台條範本的座標 ............................................................................ 65
附錄四 感興趣區域之個別RGB平均值 ...................................................... 69
方文富、侯光珊、李傳東、彭文君。1996。 FWF 型電動蜂王漿分離機之研製。蜜蜂雜誌月刊 12:3-4。
李育成。2003。真空吸力應用於蜂王漿採收之研究。碩士論文。嘉義:國立嘉義大學生物機電工程學系研究所。
張世揚。1986。基礎養蜂學。淑馨出版社。
張雯婷。2013。蜂王乳自動移蟲取漿機構定位控制之研究。碩士論文。嘉義:國立嘉義大學生物機電工程學系研究所。
張瀛福。1990。台灣地區養蜂產業之現況與展望。蠶蜂業推廣簡訊 15:2-3。
連振昌、洪滉祐、李春壽、李育成、艾群。2009。真空吸力應用於蜂王漿採收之研究。台灣農學會報 10 (3):225-240。
陳冠霖。2010。機器視覺應用於蜜蜂幼蟲的辨識研究。碩士論文。嘉義:國立嘉義大學生物機電工程學系研究所。
陳昭鈞。1990。蜜蜂下咽喉腺體與蜂王漿分泌之關係。有用昆蟲研討會。中華昆蟲特刊 5:163。
曾宏銓。2012。蜜蜂幼蟲移蟲平台之整合設計研究。碩士論文。嘉義:國立嘉義大學生物機電工程學系研究所。
楊多福。1990。生產蜂王漿機械化。蜜蜂雜誌月刊 7:22-25。
賴祥民。2013。機器人整機技術。智慧自動化產業期刊 5:2-9。
Arivazhagan, S., R. Newlin Shebiah, S. Selva Nidhyanandhan and L. Ganesan 2010. Fruit Recognition Using Color and Texture Features. Journal of Emerging Trends in Computing and Information Sciences. 1 (2): 90-94.
Bishop, Robert H. 2007. LabVIEW 8: Student Edition. Pearson Education, Inc.
Blasco, J., N. Aleixos, E. Molto. 2003. Machine Vision System for Automatic Quality Grading of Fruit. Biosystems Engineering. 85 (4): 415-423.
Brosnan, Tadhg and Da-Wen Sun. 2002. Inspection and Grading of Agricultural and Food Products by Computer Vision System-A Review. Computers and Electronics in Agriculture. 36: 193-213.
Chen, Yud-Ren, Kuanglin Chao and Moon S. Kim. 2002. Machine Vision Technology for Agricultural Applications. Computers and Electronics in Agriculture. 36: 173-191.
Cheng, Fang, Yibin Ying and Yanbin Li. 2006. Detection of Defects in Rice Seeds Using Machine Vision. Transactions of the ASABE. 49 (6): 1929-1934.
Chong, Vui Kiong, Naoshi Kondo, Kazunori Ninomiya, Takao Nishi, Mitsuji Monta, Kazuhiko Namba and Qin Zhang. 2008. Features Extraction for Eggplant Fruit Grading System Using Machine Vision. Applied Engineering in Agriculture. 24 (5): 675-684.
Fujiwara, Suguru, Jiro Imai, Mineko Fujiwara, Tomoko Yaeshima, Takuji Kawashima and Kumpei Kobayashi. 1990. A Potent Antibacterial Protein in Royal Jelly: Purification and Determination of the Primary Structure of Royalisin. The Journal of Biological Chemistry. 265 (19): 11333-11337.
Gonzalez, Rafael C., Richard E. Woods. 2009. Digital Image Processing, Third Edition. Pearson Education Taiwan Ltd.
Groover, Mikell P. 2007. Fundamentals of Modern Manufacturing: Materials, Processes, and Systems, Third Edition. John Wiley &; Sons, Inc.
JAI. 2013. CB-080GE: User’s Manual. JAI Ltd.
Johnson, Richard A. and Gouri K. Bhattacharyya. 2011. Statistics: Principles and Methods, Sixth Edition. John Wiley &; Sons, Inc.
Kassler, Michael. 2001. Agricultural Automation in the New Millennium. Computers and Electronics in Agriculture. 30: 237-240.
Li, Yongyu, Sagar Dhakal and Yankun Peng. 2012. A Machine Vision System for Identification of Micro-crack in Egg Shell. Journal of Food Engineering. 109: 127-134.
MathWorks. 2013. Image Processing Toolbox: User’s Guide. The MathWorks Inc.
Mertens, K., B. De Ketelaere, B. Kamers, F. R. Bamelis, B. J. Kemps, E. M. Verhoelst, J. G. De Baerdemaeker and E. M. Decuypere. 2005. Dirt Detection on Brown Eggs by Means of Color Computer Vision. Poultry Science. 84: 1653-1659.
Pedreschi, Franco, Jorge Leon, Domingo Mery and Pedro Moyano. 2006. Development of a Computer Vision System to Measure the Color of Potato Chips. Food Research International. 39: 1092-1098.
Sabatini, Anna Gloria, Gian Luigi Marcazzan, Maria Fiorenza Caboni, Stefan Bogdanov and Ligia Bicudo De Almeida-Muradian. 2009. Quality and Standardisation of Royal Jelly. Journal of ApiProduct and ApiMedical Science. 1 (1): 1-6.
Shatadal, Pankaj and Jinglu Tan. 2003. Identifying Damaged Soybeans by Color Image Analysis. Applied Engineering in Agriculture. 19 (1): 65-69.
Sonka, Milan, Vaclav Hlavac and Roger Boyle. 1999. Image Processing, Analysis, and Machine Vision, Second Edition. Brooks/Cole Publishing Company.
Wang, Chenglong, Xiaoyu Li, Wei Wang, Yaoze Feng, Zhu Zhou, Hui Zhan. 2011. Recognition of Worm-eaten Chestnuts Based on Machine Vision. Mathematical and Computer Modelling. 54: 888-894.
Yam, Kit L. and Spyridon E. Papadakis. 2004. A Simple Digital Imaging Method for Measuring and Analyzing Color of Food Surfaces. Journal of Food Engineering. 61: 137-142.
YAMAHA. 2009. YAMAHA Single-axis Robot FLIP-X Series: T5/T5H User’s Manual. YAMAHA Motor Co., LTD.
YAMAHA. 2010. YAMAHA Robot Controller: ERCD User’s Manual. YAMAHA Motor Co., LTD.
YAMAHA. 2012. YAMAHA Single-axis Robot FLIP-X Series: YMS User’s Manual. YAMAHA Motor Co., LTD.
Young, Chao-Wang, Hong-Cyuan Zeng, Wen-Ting Chang and Chyung Ay. 2012. The Study on a Honeybee Larvae-moving Device. Proceedings of the 6th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering (ISMAB). 328-333. Jeonju, Korea, 18-20 June.
Zhou, Bin, Manhong Ye and Ke Zhang. 2008. Studies on the Artificial Feeding Conditions of Queen Bee Larvae. Journal of Biological Sciences. 8 (5): 950-953.
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