Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89835Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 陳明汝 | zh_TW |
| dc.contributor.advisor | Ming-Ju Chen | en |
| dc.contributor.author | 陳冠伶 | zh_TW |
| dc.contributor.author | Kuan-Ling Chen | en |
| dc.date.accessioned | 2023-09-22T16:19:29Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-07 | - |
| dc.identifier.citation | Adviento-Borbe, M. A. A., Wheeler, E. F., Brown, N. E., Topper, P. A., Graves, R. E., Ishler, V. A., & Varga, G. A. (2010). Ammonia and Greenhouse Gas Flux from Manure in Freestall Barn with Dairy Cows on Precision Fed Rations. Transactions of the ASABE, 53(4), 1251-1266. https://doi.org/10.13031/2013.32590
Akbar, M. O., Shahbaz khan, M. S., Ali, M. J., Hussain, A., Qaiser, G., Pasha, M., Pasha, U., Missen, M. S., & Akhtar, N. (2020). IoT for Development of Smart Dairy Farming. Journal of Food Quality, 2020, 4242805. https://doi.org/10.1155/2020/4242805 Agreste, Ministère de l'Agriculture et de l'Alimentation, France (2022). Enquête annuelle laitière 2020 [Annual Dairy Survey 2020]. https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Chd2202/cd2022-02_EAL2020.pdf À l'heure du lait (n.d.) https://www.ferme-laitiere-france.com/en/the-milk-files/ Ali, T. E., & Schaeffer, L. R. (1987). Accounting for covariances among test day milk yields in dairy cows. Canadian Journal of Animal Science, 67(3), 637-644. https://doi.org/10.4141/cjas87-067 Allen, A., Golden, B., Taylor, M., Patterson, D., Henriksen, D., & Skuce, R. (2008). Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland. Livestock Science, 116(1), 42-52. https://doi.org/10.1016/j.livsci.2007.08.018 Andrew, W., Hannuna, S., Campbell, N., & Burghardt, T. (2016). Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA. https://doi.org/10.1109/ICIP.2016.7532404 Antanaitis, R., Juozaitienė, V., Urbonavičius, G., Malašauskienė, D., Televičius, M., Urbutis, M., & Baumgartner, W. (2021). Impact of Lameness on Attributes of Feeding Registered with Noseband Sensor in Fresh Dairy Cows. Agriculture, 11(9). https://doi.org/10.3390/agriculture11090851 Arbel, R., Bigun, Y., Ezra, E., Sturman, H., & Hojman, D. (2001). The Effect of Extended Calving Intervals in High Lactating Cows on Milk Production and Profitability. Journal of Dairy Science, 84(3), 600-608. https://doi.org/10.3168/jds.S0022-0302(01)74513-4 Armstrong, D. V. (1994). Heat Stress Interaction with Shade and Cooling. Journal of Dairy Science, 77(7), 2044-2050. https://doi.org/10.3168/jds.S0022-0302(94)77149-6 Avanzato, R., Beritelli, F., & Puglisi, V. F. (2022). Dairy Cow Behavior Recognition Using Computer Vision Techniques and CNN Networks. 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia. https://doi.org/10.1109/IoTaIS56727.2022.9975979 Awad, A. I. (2016). From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture, 123, 423-435. https://doi.org/10.1016/j.compag.2016.03.014 Awad, A. I., Zawbaa, H. M., Mahmoud, H. A., Nabi, E. H. H. A., Fayed, R. H., & Hassanien, A. E. (2013). A robust cattle identification scheme using muzzle print images. 2013 Federated Conference on Computer Science and Information Systems, Krakow, Poland. Awasthi, A., Awasthi, A., Riordan, D., & Walsh, J. (2016). Non-Invasive Sensor Technology for the Development of a Dairy Cattle Health Monitoring System. Computers, 5(4). https://doi.org/10.3390/computers5040023 Bach, A., & Cabrera, V. (2017). Robotic milking: Feeding strategies and economic returns. Journal of Dairy Science, 100(9), 7720-7728. https://doi.org/10.3168/jds.2016-11694 Barry, B., A. Gonzales-Barron, U., McDonnell, K., Butler, F., & Ward, S. (2007). Using Muzzle Pattern Recognition as a Biometric Approach for Cattle Identification. Transactions of the ASABE, 50(3), 1073-1080. https://doi.org/10.13031/2013.23121 Becker, C. A., Collier, R. J., & Stone, A. E. (2020). Invited review: Physiological and behavioral effects of heat stress in dairy cows. Journal of Dairy Science, 103(8), 6751-6770. https://doi.org/10.3168/jds.2019-17929 Bekara, M. E. A., Bareille, N., Bidan, F., Allain, C., & Disenhaus, C. (2017). An ex ante analysis of the economic profitability of automatic oestrus detection devices in different dairy farming systems in France. European Conference on Precision Livestock Farming (ECPLF), Nantes, France. https://hal.science/hal-01591150 Berry, D. P. (2015). Breeding the dairy cow of the future: what do we need? Animal Production Science, 55(7), 823-837. https://doi.org/10.1071/AN14835 Bewley, J. M., & Schutz, M. M. (2010). Recent studies using a reticular bolus system for monitoring dairy cattle core body temperature. The First North American Conference on Precision Dairy Management, Toronto, Canada. http://precisiondairy.com/proceedings/s11bewley.pdf Bohmanova, J., Misztal, I., & Cole, J. B. (2007). Temperature-Humidity Indices as Indicators of Milk Production Losses due to Heat Stress. Journal of Dairy Science, 90(4), 1947-1956. https://doi.org/10.3168/jds.2006-513 Bovo, M., Benni, S., Barbaresi, A., Santolini, E., Agrusti, M., Torreggiani, D., & Tassinari, P. (2020). A Smart Monitoring System for a Future Smarter Dairy Farming. 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento, Italy. https://doi.org/10.1109/MetroAgriFor50201.2020.9277547 Brombin, A., Pezzuolo, A., & Brščić, M. (2019). Are we ready for the big change in the dairy production system? Research in Veterinary Science, 126, 17-19. https://doi.org/10.1016/j.rvsc.2019.08.006 Bucklin, R. A., Turner, L. W., Beede, D. K., Bray, D. R., & Hemken, R. W. (1991). Methods to Relieve Heat Stress for Dairy Cows in Hot, Humid Climates. Applied Engineering in Agriculture, 7(2), 241-247. https://doi.org/10.13031/2013.26218 Buffington, D. E., Collier, R. J., & Canton, G. H. (1983). Shade Management Systems to Reduce Heat Stress for Dairy Cows in Hot, Humid Climates. Transactions of the ASAE, 26(6), 1798-1802. https://doi.org/10.13031/2013.33845 Chang, C. L., Tseng, C. Y., Lee, S .J., Chen, J. Y., Huang, Y. C., Lee, S. C., Chang, H. L., & Wu, M. C. (2001). Dairy Herd Improvement (DHI) Program in Taiwan. Taiwan Livestock Res., 34(4), 285-295. https://www.angrin.tlri.gov.tw/cow/Twndhi.htm Chiu, S. Y. (2009) 日本乳業概況系列（四）[Overview of the Japanese Dairy Industry Series (Four)]. https://www.angrin.tlri.gov.tw/cow/dhi75/dhi75P30.htm Cockburn, M. (2020). Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms. Animals, 10(9). https://doi.org/10.3390/ani10091690 Collier, R. J., Dahl, G. E., & VanBaale, M. J. (2006). Major Advances Associated with Environmental Effects on Dairy Cattle. Journal of Dairy Science, 89(4), 1244-1253. https://doi.org/10.3168/jds.S0022-0302(06)72193-2 Coppock, C. E., Bath, D. L., & Harris, B. (1981). From Feeding to Feeding Systems. Journal of Dairy Science, 64(6), 1230-1249. https://doi.org/10.3168/jds.S0022-0302(81)82698-7 Council of Agriculture, Executive Yuan, Taiwan. (2003, June). 台灣地區乳牛群性能改良執行成效及檢討 [Implementation effectiveness and review of Dairy Herd Improvement in Taiwan]. https://www.coa.gov.tw/ws.php?id=4450 Council of Agriculture, Executive Yuan, Taiwan. (2021). 農業統計年報（109年） [AG. Statistic Yearbook 2020]. https://agrstat.coa.gov.tw/sdweb/public/book/Book.aspx Da Borso, F., Chiumenti, A., Sigura, M., & Pezzuolo, A. (2017). Influence of automatic feeding systems on design and management of dairy farms. Journal of Agricultural Engineering, 48(s1), 48-52. https://doi.org/10.4081/jae.2017.642 Darwis, D., Mehta, A. R., Wati, N. E., Samsugi, S., & Swaminarayan, P. R. (2022). Digital Smart Collar: Monitoring Cow Health Using Internet of Things. 2022 International Symposium on Electronics and Smart Devices (ISESD), Bandung, Indonesia. https://doi.org/10.1109/ISESD56103.2022.9980682 de Haas, Y., Windig, J. J., Calus, M. P. L., Dijkstra, J., de Haan, M., Bannink, A., & Veerkamp, R. F. (2011). Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. Journal of Dairy Science, 94(12), 6122-6134. https://doi.org/10.3168/jds.2011-4439 de Koning, C. J. A. M. (2011). Milking Machines | Robotic Milking. In J. W. Fuquay (Ed.), Encyclopedia of Dairy Sciences (Second Edition) (pp. 952-958). Academic Press. https://doi.org/10.1016/B978-0-12-374407-4.00360-5 Dikmen, S., & Hansen, P. J. (2009). Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment? Journal of Dairy Science, 92(1), 109-116. https://doi.org/10.3168/jds.2008-1370 Dongre, V. B., Gandhi, R. S., Singh, A., & Ruhil, A. P. (2012). Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle. Livestock Science, 147(1), 192-197. https://doi.org/10.1016/j.livsci.2012.04.002 Eigenberg, R., Hahn, G., Nienaber, J., Brown-Brandl, T., & Spiers, D. (2000). Development of a new respiration rate monitor for cattle. Transactions of the ASAE, 43. https://doi.org/10.13031/2013.2755 Erickson, P. S., & Kalscheur, K. F. (2020). Chapter 9 - Nutrition and feeding of dairy cattle. In F. W. Bazer, G. C. Lamb, & G. Wu (Eds.), Animal Agriculture (pp. 157-180). Academic Press. https://doi.org/10.1016/B978-0-12-817052-6.00009-4 Eurostat (n.d.) Number of dairy cows [dataset]. https://ec.europa.eu/eurostat/databrowser/view/TAG00014/default/table?lang=en&category=agr.apro.apro_anip.apro_mt.apro_mt_ls Firk, R., Stamer, E., Junge, W., & Krieter, J. (2002). Automation of oestrus detection in dairy cows: a review. Livestock Production Science, 75(3), 219-232. https://doi.org/10.1016/S0301-6226(01)00323-2 Flamenbaum, I., & Galon, N. (2010). Management of Heat Stress to Improve Fertility in Dairy Cows in Israel. Journal of Reproduction and Development, 56(S), S36-S41. https://doi.org/10.1262/jrd.1056S36 Flint, A. P., & Woolliams, J. A. (2008). Precision animal breeding. Philos Trans R Soc Lond B Biol Sci, 363(1491), 573-590. https://doi.org/10.1098/rstb.2007.2171 Font-Palma, C. (2019). Methods for the Treatment of Cattle Manure—A Review. Journal of Carbon Research, 5(2). https://doi.org/10.3390/c5020027 Food and Agriculture Organization of the United Nations [FAO]. (n.d.). FAOSTAT statistical database. https://www.fao.org/faostat/en/#data Food and Agriculture Organization of the United Nations [FAO]. (2022). Food Outlook – Biannual Report on Global Food Markets, November 2022. https://doi.org/10.4060/cc2864en Foodnext. (2022). 乳業戰爭 九大擂台誰是贏家 [Dairy War: Who are the Winners among the Nine Major Contenders?]. Foodnext Quarterly Publication, 26. https://www.cunext.asia/products/vol26 Fortune Business Insights. (2020). Milking Robots Market Size, Share & Industry Analysis, By System Type (Single-Stall Unit, Multi-Stall Unit, Automated Milking Rotary), By Herd Size (Less than 100, 100-1000 and 1001 and Above), and Regional Forecast, 2020-2027. https://www.fortunebusinessinsights.com/milking-robots-market-102996 Fortune Business Insights. (2022). Dairy Foods Market Size, Share & COVID-19 Impact Analysis, By Source (Cattle, Sheep, Goat, and Camel), Type (Lactose and Lactose-free), Product Type (Milk, Cheese, Butter, Dessert, Yogurt, and Others), Distribution Channel (Supermarkets/Hypermarkets, Specialty Stores, Convenience Stores, and Online Retail), and Regional Forecast, 2021-2028. https://www.fortunebusinessinsights.com/dairy-foods-market-103890 Fournel, S., Rousseau, A. N., & Laberge, B. (2017). Rethinking environment control strategy of confined animal housing systems through precision livestock farming. Biosystems Engineering, 155, 96-123. https://doi.org/10.1016/j.biosystemseng.2016.12.005 Frazzi, E., Calamari, L., Calegari, F., & Stefanini, L. (2000). Behavior of Dairy Cows in Response to Different Barn Cooling Systems. Transactions of the ASAE, 43(2), 387-394. https://doi.org/10.13031/2013.2716 Garvey, M. (2022). Lameness in Dairy Cow Herds: Disease Aetiology, Prevention and Management. Dairy, 3(1), 199-210. https://doi.org/10.3390/dairy3010016 Gauly, M., & Ammer, S. (2020). Review: Challenges for dairy cow production systems arising from climate changes. animal, 14(S1), s196-s203. https://doi.org/10.1017/S1751731119003239 Gerber, P. J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A., & Tempio, G. (2013). Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO). Gokul, V., & Tadepalli, S. (2017). Implementation of smart infrastructure and non-invasive wearable for real time tracking and early identification of diseases in cattle farming using IoT. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India. https://doi.org/10.1109/I-SMAC.2017.8058394 Goswami, S. (2020). Temperature Based Automatic Cooling System for Livestock Farm in Western Region of Rajasthan using Arduino. International Advanced Research Journal in Science, Engineering and Technology, 7(11). https://doi.org/10.17148/IARJSET.2020.71114 Grzesiak, W., Błaszczyk, P., & Lacroix, R. (2006). Methods of predicting milk yield in dairy cows—Predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs). Computers and Electronics in Agriculture, 54(2), 69-83. https://doi.org/10.1016/j.compag.2006.08.004 Grzesiak, W., Lacroix, R., Wójcik, J., & Blaszczyk, P. (2003). A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Canadian Journal of Animal Science, 83(2), 307-310. https://doi.org/10.4141/A02-002 Hayes, B. J., Bowman, P. J., Chamberlain, A. J., & Goddard, M. E. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science, 92(2), 433-443. https://doi.org/10.3168/jds.2008-1646 Hayes, B. J., Lewin, H. A., & Goddard, M. E. (2013). The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genetics, 29(4), 206-214. https://doi.org/10.1016/j.tig.2012.11.009 Henchion, M. M., Regan, Á., Beecher, M., & MackenWalsh, Á. (2022). Developing ‘Smart’ Dairy Farming Responsive to Farmers and Consumer-Citizens: A Review. Animals, 12(3). https://doi.org/10.3390/ani12030360 Hsieh, C.-L., Hung, C.-Y., & Kuo, C.-Y. (2011). Quantization of Adulteration Ratio of Raw Cow Milk by Least Squares Support Vector Machines (LS-SVM) and Visible/Near Infrared Spectroscopy. Engineering Applications of Neural Networks, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_15 Hsu, J. (2019). Benefit analysis of dairy farm automation and smart system employment. Journal of the Agricultural Association of Taiwan, 20(1), 39-58. https://doi.org/10.6730/JAAT.201903_20(1).0004 Idris, M., Uddin, J., Sullivan, M., McNeill, D. M., & Phillips, C. J. C. (2021). Non-Invasive Physiological Indicators of Heat Stress in Cattle. Animals, 11(1). https://doi.org/10.3390/ani11010071 International Dairy Federation [IDF]. (2019). The Global Dairy Sector: Facts 2019. http://www.dairydeclaration.org/Portals/153/Content/Documents/DDOR%20Global%20Dairy%20Facts%202019.pdf Islam, M. M., & Scott, S. D. (2022). Exploring the Effects of Precision Livestock Farming Notification Mechanisms on Canadian Dairy Farmers. Science and Technologies for Smart Cities, Cham. https://doi.org/10.1007/978-3-031-06371-8_16 Jacobs, J. A., & Siegford, J. M. (2012). Invited review: The impact of automatic milking systems on dairy cow management, behavior, health, and welfare. Journal of Dairy Science, 95(5), 2227-2247. https://doi.org/10.3168/jds.2011-4943 Japan Dairy Council (n.d.). Japan Dairy Farming. https://www.dairy.co.jp/jp/engall.pdf Japan Livestock Products Export Promotion Council [J-LEC]. (n.d.). The Feature: Milk and Dairy Products. http://jlec-pr.jp/en/milk/milk-tokutyou/ Ji, B., Banhazi, T., Ghahramani, A., Bowtell, L., Wang, C., & Li, B. (2020). Modelling of heat stress in a robotic dairy farm. Part 1: Thermal comfort indices as the indicators of production loss. Biosystems Engineering, 199, 27-42. https://doi.org/10.1016/j.biosystemseng.2019.11.004 Ji, B., Banhazi, T., Phillips, C. J. C., Wang, C., & Li, B. (2022). A machine learning framework to predict the next month's daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm. Biosystems Engineering, 216, 186-197. https://doi.org/10.1016/j.biosystemseng.2022.02.013 Johnston, A. M., & Edwards, D. S. (1996). Welfare implications of identification of cattle by ear tags [10.1136/vr.138.25.612]. Veterinary Record, 138(25), 612-614. https://doi.org/10.1136/vr.138.25.612 Kadzere, C. T., Murphy, M. R., Silanikove, N., & Maltz, E. (2002). Heat stress in lactating dairy cows: a review. Livestock Production Science, 77(1), 59-91. https://doi.org/10.1016/S0301-6226(01)00330-X Kim, H. T., Ikeda, Y., & Choi, H. L. (2005). The Identification of Japanese Black Cattle by Their Faces. Asian-Australas J Anim Sci, 18(6), 868-872. https://doi.org/10.5713/ajas.2005.868 Klungel, G. H., Slaghuis, B. A., & Hogeveen, H. (2000). The Effect of the Introduction of Automatic Milking Systems on Milk Quality1. Journal of Dairy Science, 83(9), 1998-2003. https://doi.org/10.3168/jds.S0022-0302(00)75077-6 Kulatunga, C., Shalloo, L., Donnelly, W., Robson, E., & Ivanov, S. (2017). Opportunistic Wireless Networking for Smart Dairy Farming. IT Professional, 19(2), 16-23. https://doi.org/10.1109/MITP.2017.28 Kumar, S., Pandey, A., Sai Ram Satwik, K., Kumar, S., Singh, S. K., Singh, A. K., & Mohan, A. (2018). Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement, 116, 1-17. https://doi.org/10.1016/j.measurement.2017.10.064 Kumar, S., Singh, S. K., & Singh, A. K. (2017). Muzzle point pattern based techniques for individual cattle identification. IET Image Processing, 11(10), 805-814. https://doi.org/10.1049/iet-ipr.2016.0799 Kumar, S., Tiwari, S., & Singh, S. K. (2015). Face recognition for cattle. 2015 Third International Conference on Image Information Processing (ICIIP), Waknaghat, India. https://doi.org/10.1109/ICIIP.2015.7414742 Kusakunniran, W., Wiratsudakul, A., Chuachan, U., Kanchanapreechakorn, S., & Imaromkul, T. (2018). Automatic cattle identification based on fusion of texture features extracted from muzzle images. 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France. https://doi.org/10.1109/ICIT.2018.8352400 Lacroix, R., M. Wade, K., Kok, R., & F. Hayes, J. (1995). Prediction of Cow Performance with a Connectionist Model. Transactions of the ASAE, 38(5), 1573-1579. https://doi.org/10.13031/2013.27984 Larregui, J. I., Cazzato, D., & Castro, S. M. (2019). An image processing pipeline to segment iris for unconstrained cow identification system. 9(1), 145-159. https://doi.org/10.1515/comp-2019-0010 (Open Computer Science) Laurs, A., & Priekulis, J. (2008). Robotic milking of dairy cows. Agronomy Research, 6, 241–247. https://agronomy.emu.ee/vol06Spec/p6S08.pdf Lees, A. M., Sejian, V., Lees, J. C., Sullivan, M. L., Lisle, A. T., & Gaughan, J. B. (2019). Evaluating rumen temperature as an estimate of core body temperature in Angus feedlot cattle during summer. International Journal of Biometeorology, 63(7), 939-947. https://doi.org/10.1007/s00484-019-01706-0 Leon-Velarde, C. U., McMillan, I., Gentry, R. D., & Wilton, J. W. (1995). Models for estimating typical lactation curves in dairy cattle. Journal of Animal Breeding and Genetics, 112(1-6), 333-340. https://doi.org/10.1111/j.1439-0388.1995.tb00575.x Liseune, A., Salamone, M., Van den Poel, D., Van Ranst, B., & Hostens, M. (2020). Leveraging latent representations for milk yield prediction and interpolation using deep learning. Computers and Electronics in Agriculture, 175, 105600. https://doi.org/10.1016/j.compag.2020.105600 Liseune, A., Salamone, M., Van den Poel, D., Van Ranst, B., & Hostens, M. (2021). Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Computers and Electronics in Agriculture, 180, 105904. https://doi.org/10.1016/j.compag.2020.105904 Lu, Y., He, X., Wen, Y., & Wang, P. S. P. (2014). A new cow identification system based on iris analysis and recognition. International Journal of Biometrics, 6(1), 18-32. https://doi.org/10.1504/IJBM.2014.059639 Markets and Markets. (2022). Livestock Monitoring Market by Livestock Type (Cattle, Poultry, Swine, Equine), Application (Milk Harvesting Management, Heat Detection Monitoring, Feeding Management, Health Monitoring Management), Offering and Geography (2022-2030). https://www.marketsandmarkets.com/Market-Reports/livestock-monitoring-market-72634532.html Martinez, A., Schoenig, S., Andresen, D., & Warren, S. (2006). Ingestible Pill for Heart Rate and Core Temperature Measurement in Cattle. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, USA. https://doi.org/10.1109/IEMBS.2006.259226 Mayo, L. M., Silvia, W. J., Ray, D. L., Jones, B. W., Stone, A. E., Tsai, I. C., Clark, J. D., Bewley, J. M., & Heersche, G. (2019). Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows. Journal of Dairy Science, 102(3), 2645-2656. https://doi.org/https://doi.org/10.3168/jds.2018-14738 Milan, H. F. M., Maia, A. S. C., & Gebremedhin, K. G. (2016). Technical note: Device for measuring respiration rate of cattle under field conditions1. Journal of Animal Science, 94(12), 5434-5438. https://doi.org/10.2527/jas.2016-0904 Ministère de l'Agriculture et de la Souveraineté alimentaire, France. (2022) Robots agricoles : où en est-on ? [Agricultural Robots: Where Are We?]. https://agriculture.gouv.fr/robots-agricoles-ou-en-est Ministry of Agriculture, Forestry and Fishery [MAFF], Japan. (2023). The 96th Statistical Yearbook of Ministry of Agriculture, Forestry and Fisheries. https://www.maff.go.jp/e/data/stat/96th/index.html Monteiro, A., Santos, S., & Gonçalves, P. (2021). Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals, 11(8). https://doi.org/10.3390/ani11082345 Moretti, R., Biffani, S., Chessa, S., & Bozzi, R. (2017). Heat stress effects on Holstein dairy cows’ rumination. animal, 11(12), 2320-2325. https://doi.org/10.1017/S1751731117001173 Murphy, M. D., O’Mahony, M. J., Shalloo, L., French, P., & Upton, J. (2014). Comparison of modelling techniques for milk-production forecasting. Journal of Dairy Science, 97(6), 3352-3363. https://doi.org/10.3168/jds.2013-7451 Nabokov, V. I., Novopashin, L. A., Denyozhko, L. V., Sadov, A. A., Ziablitckaia, N. V., Volkova, S. A., & Speshilova, I. V. (2020). Applications of feed pusher robots on cattle farmings and its economic efficiency. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies 11. https://doi.org/10.14456/ITJEMAST.2020.270 Nguyen, Q. T., Fouchereau, R., Frénod, E., Gerard, C., & Sincholle, V. (2020). Comparison of forecast models of production of dairy cows combining animal and diet parameters. Computers and Electronics in Agriculture, 170, 105258. https://doi.org/10.1016/j.compag.2020.105258 Nienaber, J. A., & Hahn, G. L. (2007). Livestock production system management responses to thermal challenges. International Journal of Biometeorology, 52(2), 149-157. https://doi.org/10.1007/s00484-007-0103-x Niloofar, P., Francis, D. P., Lazarova-Molnar, S., Vulpe, A., Vochin, M.-C., Suciu, G., Balanescu, M., Anestis, V., & Bartzanas, T. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, 190, 106406. https://doi.org/10.1016/j.compag.2021.106406 Nleya, S. M., & Ndlovu, S. (2021). Smart Dairy Farming Overview: Innovation, Algorithms and Challenges. In A. Choudhury, A. Biswas, T. P. Singh, & S. K. Ghosh (Eds.), Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT (pp. 35-59). Springer Singapore. https://doi.org/10.1007/978-981-16-6124-2_3 Numbeo (n.d.) Price Rankings by Country of Milk (regular), (1 liter) (Markets). Retreived May 25, 2023. https://www.numbeo.com/cost-of-living/country_price_rankings?itemId=8&displayCurrency=USD Okura, F., Ikuma, S., Makihara, Y., Muramatsu, D., Nakada, K., & Yagi, Y. (2019). RGB-D video-based individual identification of dairy cows using gait and texture analyses. Computers and Electronics in Agriculture, 165, 104944. https://doi.org/10.1016/j.compag.2019.104944 Olori, V. E., Brotherstone, S., Hill, W. G., & McGuirk, B. J. (1999). Fit of standard models of the lactation curve to weekly records of milk production of cows in a single herd. Livestock Production Science, 58(1), 55-63. https://doi.org/10.1016/S0301-6226(98)00194-8 Organization for Economic Co-operation and Development [OECD]. & Food and Agriculture Organization of the United Nations [FAO]. (2022). OECD-FAO Agricultural Outlook 2022-2031. https://doi.org/10.1787/f1b0b29c-en Pereira, P. C. (2014). Milk nutritional composition and its role in human health. Nutrition, 30(6), 619-627. https://doi.org/10.1016/j.nut.2013.10.011 Peyraud, J. L., & Delagarde, R. (2013). Managing variations in dairy cow nutrient supply under grazing. animal, 7(s1), 57-67. https://doi.org/10.1017/S1751731111002394 Porto, S. M. C., Arcidiacono, C., Anguzza, U., & 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. https://doi.org/10.1016/j.biosystemseng.2013.03.002 Porto, S. M. C., Arcidiacono, C., Anguzza, U., & 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. https://doi.org/10.1016/j.biosystemseng.2015.02.012 Qiao, Y., Kong, H., Clark, C., Lomax, S., Su, D., Eiffert, S., & Sukkarieh, S. (2021). Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation. Computers and Electronics in Agriculture, 185, 106143. https://doi.org/10.1016/j.compag.2021.106143 Ratnakaran, A., Sejian, V., Jose, V., Vaswani, S., Madiajagan, B., Krishnan, G., Vakayil, B., Devi, P., Varma, G., & Bhatta, R. (2016). Behavioral Responses to Livestock Adaptation to Heat Stress Challenges. Asian Journal of Animal Sciences, 11, 1-13. https://doi.org/10.3923/ajas.2017.1.13 Rhoads, M. L., Rhoads, R. P., VanBaale, M. J., Collier, R. J., Sanders, S. R., Weber, W. J., Crooker, B. A., & Baumgard, L. H. (2009). Effects of heat stress and plane of nutrition on lactating Holstein cows: I. Production, metabolism, and aspects of circulating somatotropin1. Journal of Dairy Science, 92(5), 1986-1997. https://doi.org/10.3168/jds.2008-1641 Roelofs, J. B., van Eerdenburg, F. J. C. M., Soede, N. M., & Kemp, B. (2005). Pedometer readings for estrous detection and as predictor for time of ovulation in dairy cattle. Theriogenology, 64(8), 1690-1703. https://doi.org/10.1016/j.theriogenology.2005.04.004 Rossing, W., & Hogewerf, P. H. (1997). State of the art of automatic milking systems. Computers and Electronics in Agriculture, 17(1), 1-17. https://doi.org/10.1016/S0168-1699(96)01229-X Ruegg, P. L., & Reinemann, D. J. (2002). Milk quality and mastitis tests. The Bovine Practitioner, 36(1), 41–54. https://doi.org/10.21423/bovine-vol36no1p41-54 Ruiz-Garcia, L., & Lunadei, L. (2011). The role of RFID in agriculture: Applications, limitations and challenges. Computers and Electronics in Agriculture, 79(1), 42-50. https://doi.org/10.1016/j.compag.2011.08.010 Ruuska, S., Kajava, S., Mughal, M., Zehner, N., & Mononen, J. (2016). Validation of a pressure sensor-based system for measuring eating, rumination and drinking behaviour of dairy cattle. Applied Animal Behaviour Science, 174, 19-23. https://doi.org/https://doi.org/10.1016/j.applanim.2015.11.005 Sato, S., Mizuguchi, H., Ito, K., Ikuta, K., Kimura, A., & Okada, K. (2012). Technical note: Development and testing of a radio transmission pH measurement system for continuous monitoring of ruminal pH in cows. Preventive Veterinary Medicine, 103(4), 274-279. https://doi.org/10.1016/j.prevetmed.2011.09.004 Schaefer, A. L., Cook, N. J., Bench, C., Chabot, J. B., Colyn, J., Liu, T., Okine, E. K., Stewart, M., & Webster, J. R. (2012). The non-invasive and automated detection of bovine respiratory disease onset in receiver calves using infrared thermography. Research in Veterinary Science, 93(2), 928-935. https://doi.org/10.1016/j.rvsc.2011.09.021 Schaeffer, L. R., & Jamrozik, J. (1996). Multiple-Trait Prediction of Lactation Yields for Dairy Cows. Journal of Dairy Science, 79(11), 2044-2055. https://doi.org/10.3168/jds.S0022-0302(96)76578-5 Schwartzkopf-Genswein, K. S., Stookey, J. M., & Welford, R. (1997). Behavior of cattle during hot-iron and freeze branding and the effects on subsequent handling ease1. Journal of Animal Science, 75(8), 2064-2072. https://doi.org/10.2527/1997.7582064x Seegers, H., Fourichon, C., & Beaudeau, F. (2003). Production effects related to mastitis and mastitis economics in dairy cattle herds. Veterinary Research, 34(5), 475-491. https://doi.org/10.1051/vetres:2003027 Sharma, A. K., Sharma, R. K., & Kasana, H. S. (2006). Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows. Neural Computing & Applications, 15(3), 359-365. https://doi.org/10.1007/s00521-006-0037-y Sharma, A. K., Sharma, R. K., & Kasana, H. S. (2007). Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Applied Soft Computing, 7(3), 1112-1120. https://doi.org/10.1016/j.asoc.2006.07.002 Sharma, B., & Koundal, D. (2018). Cattle health monitoring system using wireless sensor network: a survey from innovation perspective. IET Wireless Sensor Systems, 8(4), 143-151. https://doi.org/10.1049/iet-wss.2017.0060 Šístkova, M., Pšenka, M., Kaplan, V., Potěšil, J., & Černín, J. (2015). The effect of individual components of total mixed ration (TMR) on precision dosing to mixer feeder wagons. Journal of Microbiology, Biotechnology and Food Sciences, 5(1), 60-63. Smith, K., Martinez, A., Craddolph, R., Erickson, H., Andresen, D., & Warren, S. (2006). An Integrated Cattle Health Monitoring System. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, USA. https://doi.org/10.1109/IEMBS.2006.259693 Smith, L. P. (1968). Forecasting annual milk yields. Agricultural Meteorology, 5(3), 209-214. https://doi.org/10.1016/0002-1571(68)90004-6 Stankovski, S., Ostojic, G., Senk, I., Rakic-Skokovic, M., Trivunovic, S., & Kucevic, D. (2012). Dairy cow monitoring by RFID. Scientia Agricola, 69(1), 75-80. https://doi.org/10.1590/S0103-90162012000100011 Strutzke, S., Fiske, D., Hoffmann, G., Ammon, C., Heuwieser, W., & Amon, T. (2019). Technical note: Development of a noninvasive respiration rate sensor for cattle. Journal of Dairy Science, 102(1), 690-695. https://doi.org/10.3168/jds.2018-14999 Sun, S., Yang, S., & Zhao, L. (2013). Noncooperative bovine iris recognition via SIFT. Neurocomputing, 120, 310-317. https://doi.org/10.1016/j.neucom.2012.08.068 Tran, D. N., Nguyen, T. N., Khanh, P. C. P., & Tran, D. T. (2022). An IoT-Based Design Using Accelerometers in Animal Behavior Recognition Systems. IEEE Sensors Journal, 22(18), 17515-17528. https://doi.org/10.1109/JSEN.2021.3051194 Tuan, S.-A., Rustia, D. J. A., Hsu, J.-T., & Lin, T.-T. (2022). Frequency modulated continuous wave radar-based system for monitoring dairy cow respiration rate. Computers and Electronics in Agriculture, 196, 106913. https://doi.org/https://doi.org/10.1016/j.compag.2022.106913 Ungar, E. D., Henkin, Z., Gutman, M., Dolev, A., Genizi, A., & Ganskopp, D. (2005). Inference of Animal Activity From GPS Collar Data on Free-Ranging Cattle. Rangeland Ecology & Management, 58(3), 256-266. https://doi.org/10.2111/1551-5028(2005)58[256:IOAAFG]2.0.CO;2 U.S. Department of Agriculture [USDA]. (2022). Taiwan: Dairy and Products Annual. https://www.fas.usda.gov/data/taiwan-dairy-and-products-annual-3 Vitali, A., Segnalini, M., Bertocchi, L., Bernabucci, U., Nardone, A., & Lacetera, N. (2009). Seasonal pattern of mortality and relationships between mortality and temperature-humidity index in dairy cows. Journal of Dairy Science, 92(8), 3781-3790. https://doi.org/10.3168/jds.2009-2127 Voulodimos, A. S., Patrikakis, C. Z., Sideridis, A. B., Ntafis, V. A., & Xylouri, E. M. (2010). A complete farm management system based on animal identification using RFID technology. Computers and Electronics in Agriculture, 70(2), 380-388. https://doi.org/10.1016/j.compag.2009.07.009 Wallace, T. C., Bailey, R. L., Lappe, J., O’Brien, K. O., Wang, D. D., Sahni, S., & Weaver, C. M. (2021). Dairy intake and bone health across the lifespan: a systematic review and expert narrative. Critical Reviews in Food Science and Nutrition, 61(21), 3661-3707. https://doi.org/10.1080/10408398.2020.1810624 Wang, H., Qin, J., Hou, Q., & Gong, S. (2020). Cattle Face Recognition Method Based on Parameter Transfer and Deep Learning. Journal of Physics: Conference Series, 1453(1), 012054. https://doi.org/10.1088/1742-6596/1453/1/012054 Wang, T., Xu, X., Wang, C., Li, Z., & Li, D. (2021). From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production. Agriculture, 11(2). https://doi.org/10.3390/agriculture11020145 Warnick, L. D., Janssen, D., Guard, C. L., & Gröhn, Y. T. (2001). The Effect of Lameness on Milk Production in Dairy Cows. Journal of Dairy Science, 84(9), 1988-1997. https://doi.org/10.3168/jds.S0022-0302(01)74642-5 Weng, Z., Meng, F., Liu, S., Zhang, Y., Zheng, Z., & Gong, C. (2022). Cattle face recognition based on a Two-Branch convolutional neural network. Computers and Electronics in Agriculture, 196, 106871. https://doi.org/10.1016/j.compag.2022.106871 West, J. W. (2003). Effects of Heat-Stress on Production in Dairy Cattle. Journal of Dairy Science, 86(6), 2131-2144. https://doi.org/10.3168/jds.S0022-0302(03)73803-X Wood, P. D. P. (1967). Algebraic Model of the Lactation Curve in Cattle. Nature, 216(5111), 164-165. https://doi.org/10.1038/216164a0 Wu, D., Wang, Y., Han, M., Song, L., Shang, Y., Zhang, X., & Song, H. (2021). Using a CNN-LSTM for basic behaviors detection of a single dairy cow in a complex environment. Computers and Electronics in Agriculture, 182, 106016. https://doi.org/10.1016/j.compag.2021.106016 Xia, L., Chunxia, S., & Ming, T. (2020). Design and Implementation of Intelligent Ear Tag for Dairy Cows in Farms Proceedings of the 2020 9th International Conference on Software and Computer Applications, Langkawi, Malaysia. https://doi.org/10.1145/3384544.3384574 Yao, L., Hu, Z., Liu, C., Liu, H., Kuang, Y., & Gao, Y. (2019). Cow face detection and recognition based on automatic feature extraction algorithm Proceedings of the ACM Turing Celebration Conference - China, Chengdu, China. https://doi.org/10.1145/3321408.3322628 Zehner, N., Niederhauser, J. J., Nydegger, F., Grothmann, A., Keller, M., Hoch, M., Haeussermann, A., & Schick, M. (2016). Validation of a new health monitoring system (RumiWatch) for combined automatic measurement of rumination, feed intake, water intake and locomotion in dairy cows. Proceedings of the International Conference of Agricultural Engineering -CIGR-AgEng 2012: Agriculture and Engineering for a Healthier Life, Valencia, Spain. Zhang, W., Yang, K., Yu, N., Cheng, T., & Liu, J. (2020, 6-9 Dec. 2020). Daily milk yield prediction of dairy cows based on the GA-LSTM algorithm. 2020 15th IEEE International Conference on Signal Processing (ICSP), Beijing, China. https://doi.org/10.1109/ICSP48669.2020.9320926. 酪農PLUS+. (2020). 日本における搾乳ロボットの普及と地域による利用特性 [Diffusion of Milking Robots in Japan and Regional Characteristics of Their Utilization]. https://rp.rakuno.ac.jp/archives/feature/3377.html | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89835 | - |
| dc.description.abstract | 牛奶和乳製品在人類社會中扮演著重要的角色。然而，酪農業目前正面臨許多挑戰，例如需求增加、勞動力短缺和氣候變遷等。為了應對這些問題，牛奶生產系統有待改進。智慧酪農業將物聯網（IoT）、大數據分析、人工智慧（AI）等技術應用於牧場，為這些挑戰提供創新的解決方案。智慧酪農業包含四個要素：環境控制、個體動物資訊、自動飼養系統和牧場管理，涉及熱緊迫控制、個體動物辨識、動物健康監測、自動榨乳系統、精準餵飼和精準育種等主題。乳量預測是智慧酪農業中的關鍵技術，它可以幫助農民預測農場收入、監測動物健康並優化育種選擇。本研究的目標是建立機器學習模型來預測乳量，並評估乳量與其他特徵之間的關係。本研究的資料來源為台灣乳牛群性能改良（DHI）計畫的資料庫，包含了33,185筆自2013年至2018年、共1,818頭荷蘭牛的榨乳記錄。經過數據清理和分析後，本研究使用不同的機器學習演算法來建立預測模型，包括支持向量機（SVM）、隨機森林（random forest）和XGBoost。研究結果顯示，最終的XGBoost模型在三種演算法中表現最佳，其對未來乳量預測的準確率達到76.33%。此外，本研究還發現影響乳量的重要因素為過去平均產量、泌乳天數、與前一胎間隔和月齡。這些結果對乳量預測的發展具有重要意義。 | zh_TW |
| dc.description.abstract | Milk and dairy products play a significant role in human society. However, milk production is currently facing several challenges, such as an increasing demand, labor shortages, and climate changes. To address these issues, improved milk production systems are required. Smart dairy farming incorporates the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and other technologies in dairy farms, offering innovative solutions to these challenges. Smart dairy farming is made up of four elements: environmental control, single animal information, automatic rearing systems, and management, involving topics like heat stress control, individual animal identification, animal health monitoring, automatic milking systems, precision feeding, and precision breeding. One crucial aspect of smart dairy farming is milk yield prediction, which allows farmers to get a projection of farm income, monitor animal health, and optimize breeding selection. The objectives of this study were to develop machine learning models for milk yield prediction and to assess the relationship between milk yield and other features. The data used in this study were obtained from Dairy Herd Improvement (DHI) database in Taiwan, which included 33,185 milking records from 2013 to 2018 involving 1,818 Holstein cattle. After data cleaning and analysis, prediction models were built using various machine learning algorithms, including support vector machine (SVM), random forest, and extreme gradient boosting machine (XGBoost). The findings of this study demonstrated that the final XGBoost model exhibited the highest performance among the three algorithms, attaining an impressive 76.33% accuracy in predicting future milk yield. Moreover, it was revealed that specific factors, including average yield, days of lactation, calving interval, and age in months, significantly influenced milk yield. These insights serve as valuable contributions to the advancement of milk yield prediction. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T16:19:29Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T16:19:29Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭 i
中文摘要 ii Abstract iii Table of Contents v List of Figures vi Introduction 1 Background 3 A. Cattle Milk Production in France, Japan, and Taiwan 3 B. Smart Dairy Farming 5 C. Milk Yield Prediction 13 Materials and Methods 15 A. Data Sources 15 B. Data Processing 16 C. Machine Learning Models 18 Results 18 A. Model Performance 18 B. Important Features 19 C. Relationship Between Milk Yield and the Top Four Important Features 19 Discussion 20 Conclusion 22 Reference 23 Appendix 45 | - |
| dc.language.iso | en | - |
| dc.title | 智慧酪農業—應用機器學習於牛乳產量預測 | zh_TW |
| dc.title | Smart Dairy Farming Focusing on Cattle Milk Yield Prediction Using Machine Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 劉嚞睿;王聖耀 | zh_TW |
| dc.contributor.oralexamcommittee | Je-Ruei Liu;Sheng-Yao Wang | en |
| dc.subject.keyword | 智慧酪農業,精準畜禽飼養管理,動物健康監測,乳量預測,機器學習, | zh_TW |
| dc.subject.keyword | smart dairy farming,precision livestock farming,animal health monitoring,milk yield prediction,machine learning, | en |
| dc.relation.page | 49 | - |
| dc.identifier.doi | 10.6342/NTU202303425 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-08 | - |
| dc.contributor.author-college | 醫學院 | - |
| dc.contributor.author-dept | 國際三校農業生技與健康醫療碩士學位學程 | - |
| Appears in Collections: | 國際三校農業生技與健康醫療碩士學位學程 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-111-2.pdf | 3 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.