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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73826完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德(Ta-Te Lin) | |
| dc.contributor.author | Cheng-Yu Kuan | en |
| dc.contributor.author | 官承譽 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:11:12Z | - |
| dc.date.available | 2022-08-22 | |
| dc.date.copyright | 2019-08-22 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-15 | |
| dc.identifier.citation | 蔡雨錡。2018。乳牛熱緊迫影像監控系統之建置與資料分析。碩士論文。台北:臺灣大學生物機電工程學研究所。
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Buffalo's biological functions as affected by heat stress—A review. Livestock Science, 127(2-3), 89-109. Mitlöhner, F. M., Morrow-Tesch, J. L., Wilson, S. C., Dailey, J. W., and McGlone, J. J. 2001. Behavioral sampling techniques for feedlot cattle. Journal of Animal Science, 79(5), 1189-1193. National Research Council. 1971 A Guide to Environmental Research on Animals. National Academy of Sciences, Washington, DC. Overton, M. W., Sischo, W. M., Temple, G. D., and Moore, D. A. 2002. Using time-lapse video photography to assess dairy cattle lying behavior in a free-stall barn. Journal of Dairy Science, 85(9), 2407-2413. Pegorer, M. F., Vasconcelos, J. L., Trinca, L. A., Hansen, P. J., and Barros, C. M. 2007. Influence of sire and sire breed (Gyr versus Holstein) on establishment of pregnancy and embryonic loss in lactating Holstein cows during summer heat stress. Theriogenology, 67(4), 692-697. Porto, S. M., Arcidiacono, C., Anguzza, U., and Cascone, G. 2013. A computer vision-based system for the automatic detection of lying behaviour of dairy cows in free-stall barns. Biosystems Engineering, 115(2), 184-194. Porto, S. M., Arcidiacono, C., Anguzza, U., and Cascone, G. 2015. The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system. Biosystems Engineering, 133, 46-55. Pragna, P., Archana, P. R., Aleena, J., Sejian, V., Krishnan, G., Bagath, M., Manimaran, A., Beena, V., Kurien, E. K., Varma, G., and Bhatta, R. 2017. Heat stress and dairy cow: impact on both milk yield and composition. Int J Dairy Sci, 12(1), 1-11. Proudfoot, K. L., Weary, D. M., and Von Keyserlingk, M. A. G. 2010. Behavior during transition differs for cows diagnosed with claw horn lesions in mid lactation. Journal of Dairy Science, 93(9), 3970-3978. Redmon, J., and Farhadi, A. 2017. YOLO9000: better, faster, stronger. arXiv preprint. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. 2016. 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Impaired reproduction in heat-stressed cattle: basic and applied aspects. Animal Reproduction Science, 60, 535-547. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73826 | - |
| dc.description.abstract | 處於熱帶及亞熱帶地區的國家,泌乳牛容易產生熱緊迫的現象,並因此影響牛隻的生長、繁殖和泌乳等行為。在舒適環境中泌乳牛有正常的採食及飲水行為,當泌乳牛受到熱緊迫影響時,其採食量的下降可達30%以上,因此若能了解其採食行為的改變,進而作為偵測泌乳牛熱緊迫現象的警訊,將可提供給管理者作為管理牛隻健康狀況的依據。本研究之目的為開發一泌乳牛採食行為的影像監測系統,影像監測系統為基於Raspberry pi 3B+ 開發板的嵌入式系統,並搭配Pi camera v2模組進行泌乳牛採食影像的擷取。採食行為的偵測使用基於深度學習的物件偵測模型Tiny-YOLO v2來偵測採食中的牛臉影像,並進行偵測模型訓練參數最佳化,最終模型F1-score為0.98。本研究的個別牛臉影像資料庫中共有19頭,並以MobileNets v1進行個別牛臉的辨識,辨識模型的平均F1-score為0.972,以人工計算驗證系統個別牛白天採食時間得到R^2為0.79,而系統辨識一完整採食行為需時27秒。
本研究最終得到個別泌乳牛的白天採食時間作為採食行為,以分娩日期將牛隻分為三類,並分析採食時間與溫濕度指標(Temperature and humidity index, THI)及產乳量的關聯性,將舒適溫濕度(THI<80)的平均採食時間、平均產乳量定為基準進行分析。分析結果得到,處於泌乳前期的牛隻THI對於採食及產乳量的影響趨勢較小,而處於泌乳中後期的牛隻,2天前平均THI對於採食時間有負相關性,對照於產乳量,2天前的平均THI對於產乳量亦有負相關性,然而不同牛隻有不同的耐熱性,因此根據THI對採食時間及產乳量的下降能夠找到對於熱緊迫較無耐受性的牛隻。 | zh_TW |
| dc.description.abstract | In sub-tropical countries, dairy cows tend to experience heat stress. This phenomenon may lead to declines in feed intake, milk production, and fertility. In a comfortable environment, dairy cows have normal feeding and drinking behaviors. Dry matter intake can drop by 30% when dairy cows are affected by heat stress. For this reason, changes in feeding behavior can be a possible indicator to detect heat stress. Alerts on the heat stress of dairy cows can be provided to farm managers as a basis for monitoring the health of dairy cows. In order to monitor and record the feeding behavior of dairy cows, an imaging system is proposed in this study. The imaging system uses Raspberry Pi 3B+ as the embedded system, and a Pi Camera v2 module can acquire images for dairy cow feeding behavior detection. The feeding behavior of dairy cow is detected through the deep learning based object detection model Tiny-YOLO v2. The detection model is optimized, and the final F1-score is 0.98. In this research, there are 19 individual cows in imaging database. Individual cow face recognition is achieved by MobileNet v1 and the recognition average F1-score is 0.972. To validate the feeding time correctness, the manual observed feeding time is compare to feeding time obtain from imaging system. The R^2 is 0.79. It takes 27 seconds to recognize one feeding behavior in imaging system.
Finally, the daytime food intake time of individual dairy cows is taken as the feeding behavior. The dairy cows are divided into 3 groups according to the calving date. The correlation between the feeding time, Temperature and Humidity index (THI) and milk production are analyzed individually. The influence of THI is not obviously in feeding time and milk production when the cow is in early stage of claving. As for the cow in late stage of claving, the 2 day backward shift of THI has negative correlation between their feeding time and milk production. As a result, since different dairy cow has different tolerance to hot weather, the relation between THI, feeding time and milk production can be used to find the cow which has the most tolerance to hot condition. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:11:12Z (GMT). No. of bitstreams: 1 ntu-108-R06631002-1.pdf: 5533163 bytes, checksum: 6ee5eb6fd0bc8e29fb23ada2997165a8 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 目錄
誌謝 i 摘要 ii Abstract iii 目錄 v 圖目錄 ix 表目錄 xii 第一章 緒論 1 1.1 前言 1 1.2 研究目的 3 第二章 文獻探討 5 2.1熱緊迫對泌乳牛的影響 5 2.1.1 熱緊迫對產乳量的影響 5 2.1.2 熱緊迫對牛隻行為的影響 6 2.2 泌乳牛行為的監測與分析 7 2.2.1 溫濕度指標(Temperature and Humidity index, THI) 7 2.2.2接觸式監測系統 8 2.2.3非接觸式監測系統 10 2.3 深度學習應用於物體偵測(Object detection) 13 2.3.1基於候選區域的物件偵測模型 13 2.3.2基於迴歸方法的物件偵測模型 14 2.4深度學習應用於物體辨識 16 2.5個體動物臉部辨識 17 2.5.1非牛臉-動物辨識 17 2.5.2牛臉-動物辨識 18 第三章 研究方法 19 3.1實驗動物與實驗場域 19 3.1.1泌乳牛 19 3.1.2台大泌乳牛餵飼規則 19 3.1.3實驗場域 19 3.2 影像監測系統 20 3.2.1系統架構 20 3.2.2嵌入式開發版 21 3.2.3影像模組 21 3.2.4採食影像監測系統 21 3.3訓練影像的資料蒐集 23 3.3.1牛臉偵測影像資料 23 3.3.2牛臉辨識影像資料 24 3.4泌乳牛採食監測系統軟體架構 25 3.4.1採食監測演算法 25 3.4.2牛臉偵測演算法 29 3.4.3牛臉辨識演算法 34 3.4.4軟體架構與實作說明 39 3.5農場環境資訊感測系統 43 3.6採食行為指標分析 44 第四章 結果與討論 45 4.1採食影像監測系統 45 4.2採食監測系統軟體成果 46 4.3牛臉偵測結果 48 4.3.1牛臉偵測模型之訓練 48 4.3.2牛臉偵測模型之測試 50 4.3.3群體牛採食時間驗證 53 4.4個別牛臉辨識結果 54 4.4.1MobileNet v1辨識模型之訓練 54 4.4.2牛臉辨識模型之測試 56 4.4.3個別牛採食時間驗證 60 4.5採食結果分析 61 4.5.1群體牛隻採食結果分析 61 4.5.2個別牛隻採食結果分析 64 第五章 結論與建議 79 5.1結論 79 5.2建議 81 參考文獻 83 | |
| dc.language.iso | zh-TW | |
| dc.subject | 嵌入式系統 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 熱緊迫 | zh_TW |
| dc.subject | 牛臉辨識 | zh_TW |
| dc.subject | 牛臉偵測 | zh_TW |
| dc.subject | Cow Face Recognition | en |
| dc.subject | Embedded System | en |
| dc.subject | Deep Learning | en |
| dc.subject | Heat Stress | en |
| dc.subject | Cow Face Detection | en |
| dc.title | 基於深度學習之泌乳牛採食行為影像監測系統 | zh_TW |
| dc.title | An Image Monitoring System for Feeding Behavior of Dairy Cows Based on Deep Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 徐濟泰,謝清祿 | |
| dc.subject.keyword | 熱緊迫,嵌入式系統,牛臉偵測,牛臉辨識,深度學習, | zh_TW |
| dc.subject.keyword | Heat Stress,Embedded System,Cow Face Detection,Cow Face Recognition,Deep Learning, | en |
| dc.relation.page | 87 | |
| dc.identifier.doi | 10.6342/NTU201903695 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-16 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
| 顯示於系所單位: | 生物機電工程學系 | |
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