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標題: 線上雞隻活體重量量測與雞冠影像分析系統之研究
The Study on Online Life Weight Measurement and Images Analysis System of Comb of Chicken
作者: 劉子郁
Liu, Tzu-Yu
關鍵字: Machine Vision
Life Weight Measurement
Conveyor Belt
出版社: 生物產業機電工程學系所
引用: 1.李天立。2009。線上雞隻活體重量量測系統之研究。碩士論文。國立中興大學生物產業機電工程學系。 2.李淵百。1992。臺灣的土雞。國立中興大學畜牧學系。臺中。 3.李伯年。1952。台灣之家禽。台灣之畜產資源17-49頁。台灣銀行。 4.江佳益。2005。應用射頻識別系統於空軍飛機維修流程分析與改善。碩士論文。台南:立德大學科技管理研究所。 5.周岸騏。2007。以加溫乾燥來增強網板印刷RFID標籤效能之研究。碩士論文。台北:世新大學圖文傳播暨數位出版學系。 6.周志遠、蔡富忠、方煒、雷華德。2005。建構雞隻重量量測系統以利於飼料換肉率評估。九十四年度農業機械與生物機電論文發表會論文集。屏東。 7.林淳楠。2009。RFID技術應用於種土雞生產管理之研究。碩士論文。國立中興大學生物產業機電工程學系。 8.林德育。2007。近10年土雞選育研究報告。新化:行政院農委會畜產試驗所。 9.徐子建。2007。檬果外部品質檢測之應用研究。碩士論文。國立中興大學生物產業機電工程學系。 10.張哲維。2007。重量選別機量測系統動態分析。碩士論文。臺灣大學生物產業機電工程學研究所。 11.張晏碩。2005。秤重機負荷元之設計分析。碩士論文。國立中興大學生物產業機電工程學系。 12.揚智超。2006。一個以RFID為基礎的定位機制。碩士論文。新竹:國立交通大學。 13.蔡祥益。2005。基於機器視覺測重技術的果實分類。碩士論文。國立台灣科技大學。 14.蔡玉芬。1996。應用機械視覺與類神經網路分級玫瑰切花之研究。碩士論文。國立中興大學農業機械工程學研究所。 15.蕭子健,王智昱,儲昭偉。2007。虛擬儀控程式設計LabVIEW 8X。出版。台北:高立。 16.Bulanon, D. M., T. Kataoka, H. Okamoto and S. Hata. 2005. Feedback control of manipulator using machine vision for robotic apple harvesting. ASAE Annual Meeting Paper No. 053114. 17.E. J. Van Henten, B. A. J. Van Tuijl, G. -J. Hoogakker, M. J. Van Der Weerd, J. Hemming, J. G. Kornet and J. Bontsema. 2006. An autonomous robot for de-leafing cucumber plants grown in a high-wire cultivation system. Biosystems Engineering 94(3): 317–323. 18.Huang, K. Y., T. C. Lin and J. N. Tsai. 2002. Disease Detection and Classification in Phalaenopsis Seedlings using Machine Vision. Agricultural Engineering Journal 11(1):11-30. 19.Jay L. Devore. 2008. Probability and statistics for engineering and the sciences. 7th ed. Stamford, Connecticut, U.S. : Cengage Learning. 20.Lee, Y. P. 2006. Taiwan country chicken : A slow growth breed for eating quality. Scientific Cooperation in Agriculture between Council of Agriculture and Institut National de la Recherche Agronomique, France. 21.Schinckel, A. P. 2005. The Economic Impact of Genetic Improvement. National Swine Improvement Federation(NSIF), Fact Sheet Number1. 22.Sakai, N., S. Yonekawa and A. Matsuzaki. 1996. Two-dimensional image analysis of the shape of rice and its application to separating varieties. Journal of Food Engineering 27:397-407. 23.Tsai, R. Y. 1987. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses In IEEE Journal of Robotics and Automation, RA-3(4):323-344.
摘要: This study attempts to develop an online system for measuring the weight of live chickens and analyzing the image of combs in order to improve the management of breeding chickens. The system includes a major machine that adopts a belt transport mechanism as the guidance device, uses a belt scale consisting of belts and load cells to measure the weight of a chicken, and does not need the traditional preprocess of folding chicken wings in the measuring procedure. The LabView is used to integrate all kinds of instruments. RFID is first employed to read the serial number of a chicken wing, and then the weight data within the measuring range defined by a photoelectric switch are recorded. In a later stage of the system, a CMOS video camera is used to capture the images of chickens, and the stored images could be captured in an appropriate size to meet subsequent requirements. It is unnecessary for the recorded data to be transcribed manually, as the system features an automated output and is able to be linked to the information management system at a parent-stock native chicken farm. There is an error range of ±5% between the weight measured by this system and that measured manually. The whole process only needs a worker to run to accomplish the weight measurement work, and it takes approximately 25 seconds at a time to complete the operational process of weight measurement and image capture. After including the time spent grabbing a chicken and reading the wing number, approximately 90 chickens can be processed per hour. When the image processed by the system is converted into the area of the comb, the figure is 10%-50% less than the area measured manually.
其他識別: U0005-2208201117044600
Appears in Collections:生物產業機電工程學系



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