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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 朱元南(Yuan-Nan Chu) | |
dc.contributor.author | KA-CHUN POW | en |
dc.contributor.author | 鮑家俊 | zh_TW |
dc.date.accessioned | 2021-07-11T15:34:14Z | - |
dc.date.available | 2022-12-08 | |
dc.date.copyright | 2020-08-28 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-18 | |
dc.identifier.citation | 1. 王志文,2011。自動化殘餌偵測與精準投量控制投餌機之研發。國立臺灣大學生物產業機電工程學研究所碩士論文。 2. Aas, T. S., M. Oehme, M. Sørensen, G. He, I. Lygren, T. Åsgård, 2011. Analysis of pellet degradation of extruded high energy fish feeds with different physical qualities in a pneumatic feeding system. Aquacultural Engineering, 44(1), 25-34. 3. AFS, AIFRB and ASIH, 2020. Guidelines for the Use of Fishes in Research. American Fisheries Society. 4. Al-Jubouri, Q., W. Al-Nuaimy, M. Al-Taee, I. Young, 2017. An automated vision system for measurement of zebrafish length using low-cost orthogonal web cameras. Aquacultural Engineering, 78, 155-162. 5. Almansa, C., L. Reig, J. Oca, 2015. The laser scanner is a reliable method to estimate the biomass of a Senegalese sole (Solea senegalensis) population in a tank. Aquacultural Engineering, 69, 78-83. 6. Banan, A., A. Nasiri, A. Taheri-Garavand, 2020. Deep learning-based appearance features extraction for automated carp species identification. Aquacultural Engineering, 89. 7. Bhargava, Y. 2018. Open-design Recirculating Systems for Zebrafish Culture. Aquacultural Engineering, 81, 71-79. 8. Burg, L., K. Zhang, T. Bonawitz, V. Grajevskaja, G. Bellipanni, R. Waring, D. Balciunas, 2016. Internal epitope tagging informed by relative lack of sequence conservation. Sci Rep, 6, 36986. 9. Castranova, D., A. Lawton, C. Lawrence, D. P. Baumann, J. Best, J. Coscolla, . . . B. M. Weinstein, 2011. The effect of stocking densities on reproductive performance in laboratory zebrafish (Danio rerio). Zebrafish, 8(3), 141-146. 10. Chang, C. M., W. Fang, R. C. Jao, C. Z. Shyu, I. C. Liao, 2005. Development of an intelligent feeding controller for indoor intensive culturing of eel. Aquacultural Engineering, 32(2), 343-353. 11. Hammer, H. S. 2020. Recirculating Aquaculture Systems (RAS) for Zebrafish Culture. In:“ The Zebrafish in Biomedical Research ”(pp. 337-356). 12. Hong, H., X. Yang, Z. You, F. Cheng, 2014. Visual quality detection of aquatic products using machine vision. Aquacultural Engineering, 63, 62-71. 13. Huan, J., H. Li, F. Wu, W. Cao, 2020. Design of water quality monitoring system for aquaculture ponds based on NB-IoT. Aquacultural Engineering, 90. 14. Karplus, I., M. Gottdiener, B. Zion, 2003. Guidance of single guppies (Poecilia reticulata) to allow sorting by computer vision. Aquacultural Engineering, 27(3), 177-190. 15. Lawrence, C. 2007. The husbandry of zebrafish (Danio rerio): A review. Aquaculture, 269(1-4), 1-20. 16. Lawrence, C., T. Mason, 2012. Zebrafish housing systems: a review of basic operating principles and considerations for design and functionality. ILAR J, 53(2), 179-191. 17. Lee, P. G. 1995. A Review of Automated Control Systems for Aquaculture and Design Criteria for Their Implementation. Aquacultuml Engineering, 14, 205-277. 18. Lee, C., Y.-J. Wang, 2020. Development of a cloud-based IoT monitoring system for Fish metabolism and activity in aquaponics. Aquacultural Engineering, 90. 19. Lieschke, G. J., P. D. Currie, 2007. Animal models of human disease: zebrafish swim into view. Nat Rev Genet, 8(5), 353-367. 20. Liu, Z., X. Li, L. Fan, H. Lu, L. Liu, Y. Liu, 2014. Measuring feeding activity of fish in RAS using computer vision. Aquacultural Engineering, 60, 20-27. 21. Mal, B. C. 1996. Performance of Hawaii-type Automated Fish Feed Dispenser. Aquacultural Engineering, 15, 81-90. 22. Papandroulakis, N., P. Dimitris, D. Pascal, 2002. An automated feeding system for intensive hatcheries. 26, 13-26. 23. Papadakis, V. M., I. E. Papadakis, F. Lamprianidou, A. Glaropoulos, M. Kentouri, 2012. A computer-vision system and methodology for the analysis of fish behavior. Aquacultural Engineering, 46, 53-59. 24. Petrell, R. J., X. Shi, R. K. Ward, A. Naiberg, C. R. Savage, 1997. Determining fish size and swimming speed in cages and tanks using simple video techniques. Aquacultuml Engineering, 16, 63-84. 25. Stewart, A. M., O. Braubach, J. Spitsbergen, R. Gerlai, A. V. Kalueff, 2014. Zebrafish models for translational neuroscience research: from tank to bedside. Trends Neurosci, 37(5), 264-278. 26. Trevarrow, B. 2004. Zebrafish Facilities for Small and Large Laboratories. Methods in Cell Biology, 77, 565-591. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78984 | - |
dc.description.abstract | 商業型的斑馬魚養殖系統普遍用於養殖實驗用斑馬魚,雖然其具有多項優點,但在餵食管理上相當耗費人力,本研究針對商業型的斑馬魚養殖系統研發自動投餌機,能投餵斑馬魚顆粒飼料(乾料)和浮游水中的豐年蝦幼體(濕料),並能夠精準控制投餌量。其動作經由Arduino晶片控制,具有強大的控制彈性,可以成為未來智慧型斑馬魚養殖系統的基礎。本研究研發出基於氣壓輸送的乾濕料投餌機構,乾料投餌機構由Arduino晶片控制步進馬達,步進馬達精準的使轉桿旋轉一圈,利用轉桿的凹槽將定量飼料由上送到底部,再由壓縮空氣噴出。濕料投餌機構利用氣壓對飼料瓶打氣,以維持餌料生物的存活,並利用Arduino晶片控制電磁閥的開關,從而控制飼料瓶裡的空氣壓力,讓飼料瓶中的餌料生物及液體經管子噴出。本研究使用載台結合乾濕料投餌機構,利用光感開關控制載具移動,以測試其定位投餌之效果。測試結果顯示,乾料投餌機構能根據不同直徑凹槽的轉桿而精準的投出飼料,三種不同直徑轉桿(5,7,9毫米)的投餌量和誤差分別為0.04±0.004g、0.11±0.014g和0.21±0.022g,投餌精準度比人力投餌高許多(人手平均±誤差: 0.1±0.14g)。不僅證明此機構能達到斑馬魚每天的進食需求,而且每次完整的投餌動作由開始到完成只需要15秒,相較人手投餌更省時。濕料投餌機構的測試結果顯示,濕料投餌能穩定的把飼料瓶中的液體完全投出,投餌100次的誤差小於1%(平均±誤差: 10.1±0.1ml)。移動定位的測試結果顯示,在10次測試中只出現一次會影響投餌的偏離(偏離3.5mm),其餘的誤差介於0到1.5mm間,符合坊間商業型斑馬魚養殖系統的移動定位需求,本投餌機構不抵觸國外自動投餌機的專利,具有申請全新專利的價值,對未來建立本土化智慧型斑馬魚養殖系統極為有利。 | zh_TW |
dc.description.abstract | Commercial zebrafish culture systems are commonly used for culturing experimental zebrafish. Although they have many advantages, commercial culture systema lacking the automatic feeding capabilities are usually laborious to operate and could lead to poor and unstable qualities of the cultured fish. This research is aimed to develop an automatic feeding mechine, capable of feeding dry pallets or powders and liquids containing live brine shrimp larvae, accurately enough to replace human feeding, and can be adopted to the smart zebrafish culture systems. In this research, a dry and a wet feeding mechanisms are developed using air pressure to deliver the feed. The dry feeding mechanism has an Arduino chip that controls a stepping motor. The motor drives a rotating rod at the bottom of the feeder, a groove on the rotating rod transfers a fixed amount of a channel below it, allowing the feed to be blown out by the air pump. The wet feeding mechanism uses air bubbles to aerate the water in the feed bottle to keep brine shrimp nauplii, it uses the Arduino chip and a solenoid valve to control the air in to the fodder bottle, which then help to force the water comtaining the nauplii to spray out through a tube. Further, a light sensor is used to position a carrier for the dry and wet feeding mechanisms to test movement and positioning results. The results show that the dry feeding mechanism can accurately throw out fodder according to the sizes of the rotating rods. The feeding amount and error of the three different diameter rotating rods (5, 7, 9mm) are 0.04 ± 0.004g, 0.11 ± 0.014g and 0.21 ± 0.022g, this resprctively result is more accurate than manual feeding (manual:0.1±0.14g), proving that this mechanism can meet the daily feeding needs of zebrafish. Each complete feeding action takes only 15 seconds from start to finish, much more efficient than manual feeding. The test results of the wet feeding mechanism show that the wet feeding can completely throw out the liquid in the feed bottle at a very stable rate, with the errors of 100 feedings less than 1% (average ± error: 10.1 ± 0.1ml). The movement mechanism test results show that only one of the 10 tests deviated more than expected (3.5mm), and the remaining errors are between 0 and 1.5mm, meaning the feeding mechanism can deliver feed to fish tanks accurately for commercial zebrafish culture systems. The feeding mechanism does not conflict with the existing patents of other feeding machines, and can apply for new patents, this is extremely beneficial to the build up of a localized intelligent zebrafish culture system in the future. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T15:34:14Z (GMT). No. of bitstreams: 1 U0001-1808202012493800.pdf: 3210777 bytes, checksum: 628e23a24d3a805013296c7d48d56376 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii 目錄 v 圖目錄 vii 表目錄 ix 第一章 前言 1 第二章 文獻探討 2 2.1斑馬魚的重要性 2 2.2斑馬魚飼料 2 2.3斑馬魚養殖系統 3 2.3投餵機械化 3 2.4影像辨識應用於水產養殖 4 2.5智慧化養殖 5 第三章 材料與方法 6 3.1商業型斑馬魚養殖系統規格 6 3.2斑馬魚飼料 8 3.3自動投餌機設計概念 9 3.4投餌機構設計 10 3.5投餌動作 11 3.6乾料投餌機構 13 3.7濕料投餌機構 16 3.8控制和動力部件(共用部份) 18 3.9實驗項目 25 第四章 結果 27 4.1乾料投餌機構測試結果 27 4.2濕料投餌機構測試結果 30 4.3感測功能能否對準斑馬魚系統缸投餌之測試結果 35 第五章 討論 37 5.1乾料投餌機構測試結果討論 37 5.2濕料投餌機構測試結果討論 40 5.3感測功能能否對準斑馬魚系統缸投餌之測試結果討論 43 5.4 TRITION斑馬魚系統自動餵食機之分析和比較 44 5.5 專利要件討論 50 5.6對應國內斑馬魚養殖系統之討論 51 5.7未來商品化之考量 54 第六章 結論 59 第七章 參考文獻 60 | |
dc.language.iso | zh-TW | |
dc.title | 智慧型斑馬魚養殖系統自動投餌機之研發 | zh_TW |
dc.title | Development of an Automatic Feeding Mechine for Smart Zebrafish Culture Systems | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 劉擎華(Chyng-Hwa Liou),韓玉山(Yu-Shan Han) | |
dc.subject.keyword | 斑馬魚養殖系統,自動投餌機,氣動投餌,精準投餵,智慧化養殖, | zh_TW |
dc.subject.keyword | Commercial zebrafish culture systems,automatic feeding system,pneumatic feeding mechanism,IOT,Accurate feeding, | en |
dc.relation.page | 62 | |
dc.identifier.doi | 10.6342/NTU202003953 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-19 | |
dc.contributor.author-college | 生命科學院 | zh_TW |
dc.contributor.author-dept | 漁業科學研究所 | zh_TW |
顯示於系所單位: | 漁業科學研究所 |
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