跳到主要內容

臺灣博碩士論文加值系統

(44.200.194.255) 您好!臺灣時間:2024/07/20 16:13
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:阮有山
研究生(外文):Nguyen, Huu-Son
論文名稱:農業4.0之紫式決策架構與肉雞重量預測提升聰明生產之實證研究
論文名稱(外文):UNISON Decision Framework to empower Agriculture 4.0 and An empirical study for the broiler weight prediction for smart production
指導教授:簡禎富簡禎富引用關係
指導教授(外文):Chien, Chen-Fu
口試委員:陳暎仁許嘉裕
口試委員(外文):Chen, Ying-JenHsu, Chia-Yu
口試日期:2022-01-27
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:34
中文關鍵詞:農業 4.0肉雞屠宰體重預測精準畜牧業肉雞採食計劃決策分析肉雞供應鏈
外文關鍵詞:Agriculture 4.0Broiler weight predictionPrecision livestock farmingBroiler harvesting planningDecision analysisBroiler supply chain
相關次數:
  • 被引用被引用:0
  • 點閱點閱:87
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
全球對農產品的需求增加,預計十年後增長 15%,且於2029年肉類需求量將提升約 12%。其中,肉雞產品需求預計將在2050年提升一倍。為符合市場需求,肉雞飼養策略和飼養週齡即為挑戰。肉雞屠宰體重準確性為肉雞採食計劃過程中的關鍵問題之一,且為肉雞供應鏈資金比重佔據最高的一環。為解決肉雞供應鏈問題,本研究建立紫式決策架構(UNISON FRAMEWORK)並結合人工智慧與數學模型,用於環境參數進行肉雞屠宰體重預測用於精準預測家畜養殖。本研究架構為提前7天的滾動預測肉雞屠宰體重機制,用以輔助肉雞屠宰過程中的決策,以優化肉雞供應鏈。一項實證研究於台灣的肉雞養殖場進行,以驗證本研究架構的有效性。結果指出本研究架構能提供有效與較低成本的飼養方案,以解決肉雞體重的長期滾動預測問題,同時防止因超餵、異常屠宰體重、運營費用和客戶關係而造成的損失。
Global demand for agricultural products has been increasing and is projected to rise by 15% over the next decade, and meat consumption is predicted globally to grow about 12% by 2029. In particular, the global demand for broiler chicken products is expected to double by 2050. The high demand for broiler chicken is a great opportunity for the development of the broiler farming industry, and also is a big challenge to improve broiler rearing efficiency and productivity to meet the huge demand from customers. The accuracy of the weight prediction of broilers is the major issue leading to serious problems for the broiler harvesting planning process, and costs a lot of money in the whole broiler supply chain. To address realistic needs, this study aims to develop a UNISON decision framework that integrated artificial intelligence methodology and mathematical growth function for future broiler weight prediction based on the environmental parameters for precision livestock farming. The framework provides the prediction mechanism for the long rolling-forecast period of 7 days ahead to support decisions in the broiler harvesting production processes for broiler supply chain optimization. An empirical study was conducted on broiler farms in Taiwan to demonstrate the validity of this framework. The results have shown that the proposed framework is an effective and low-cost solution to address the long-term rolling-forecast problem for broiler weight prediction and prevent profit loss from overfeeding, abnormal weight, operation expenses, and losing key customers.
List of Tables---------------------------------------------------iii
List of Figures--------------------------------------------------iv
Chapter 1 Introduction-------------------------------------1
1.1 Research Background and Motivation-----------------------1
1.2 Research Objective---------------------------------------2
1.3 Thesis Organization--------------------------------------2
Chapter 2 Literature Review--------------------------------3
2.1 Agriculture 4.0------------------------------------------3
2.2 Precision livestock farming------------------------------4
2.3 Broiler weight prediction--------------------------------6
2.4 Multilayer perceptron neural network---------------------9
2.5 Long short term memory network---------------------------9
Chapter 3 UNISON decision framework for broiler weight prediction------------------------------------------------------11
3.1 Understand and define the problem---------------------- 11
3.2 Identify niches for decision quality improvement------- 12
3.3 Structure the influence relationship--------------------13
3.4 Sense and describe the expected results-----------------14
3.5 Overall judgments and measurements----------------------16
3.6 Tradeoff and making decision----------------------------17
Chapter 4 Empirical Research------------------------------18
4.1 Understand and define the problem-----------------------18
4.2 Identify niches for decision quality improvement--------18
4.3 Structure the influence relationship--------------------20
4.4 Sense and describe the expected results-----------------22
4.5 Overall judgments and measurements----------------------26
4.6 Tradeoff and making decision----------------------------28
Chapter 5 Conclusion--------------------------------------30
5.1 Conclusion----------------------------------------------30
5.2 Future Directions---------------------------------------30
References------------------------------------------------------32
Amraei, S., Mehdizadeh, S. A., & Sallary, S. (2017). Application of computer vision and support vector regression for weight prediction of live broiler chicken. Engineering in agriculture, environment and food, 10(4), 266-271.
Astill, J., Dara, R. A., Fraser, E. D. G., Roberts, B., & Sharif, S. (2020). Smart poultry management: Smart sensors, big data, and the internet of things. Computers and Electronics in Agriculture, 170. doi:10.1016/j.compag.2020.105291
Cândido, M., Tinôco, I., Albino, L., Freitas, L., Santos, T., Cecon, P., & Gates, R. S. (2020). Effects of heat stress on pullet cloacal and body temperature. Poultry Science, 99(5), 2469-2477.
Chen, Y.-J., & Chien, C.-F. (2018). An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing. International Journal of Production Research, 56(13), 4629-4643.
Chien, C.-F., Chen, Y.-J., & Peng, J.-T. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496-509.
Chien, C.-F., & Hsu, C.-Y. (2011). UNISON analysis to model and reduce step-and-scan overlay errors for semiconductor manufacturing. Journal of Intelligent Manufacturing, 22(3), 399-412.
Chien, C.-F., Lin, Y.-S., & Lin, S.-K. (2020). Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor. International Journal of Production Research, 58(9), 2784-2804.
Chien, C.-F., Wang, H.-J., & Wang, M. (2007). A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness. International Journal of Production Economics, 107(1), 20-30.
De Clercq, M., Vats, A., & Biel, A. (2018). Agriculture 4.0: The future of farming technology. Proceedings of the World Government Summit, Dubai, UAE, 11-13.
Demmers, T. G., Cao, Y., Gauss, S., Lowe, J. C., Parsons, D. J., & Wathes, C. M. (2010). Neural predictive control of broiler chicken growth. IFAC Proceedings Volumes, 43(6), 311-316.
Diez-Olivan, A., Averós, X., Sanz, R., Sierra, B., & Estevez, I. (2019). Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming. Computers and Electronics in Agriculture, 161, 141-150. doi:10.1016/j.compag.2018.03.025
El, C., & Akram, M. (2016). Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks. International Journal of Computer Applications, 143(11), 7-11. doi:10.5120/ijca2016910497
Fu, W., & Chien, C.-F. (2019). UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution. Computers & Industrial Engineering, 135, 940-949.
Ghazanfari, S. (2014). Application of linear regression and artificial neural network for broiler chicken growth performance prediction. Iranian Journal of Applied Animal Science, 4, 411-416.
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855-868. doi:10.1109/TPAMI.2008.137
Halachmi, I., Guarino, M., Bewley, J., & Pastell, M. (2019). Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annual review of animal biosciences, 7, 403-425.
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735
Hu, Y.-F., Hou, J.-L., & Chien, C.-F. (2019). A UNISON framework for knowledge management of university–industry collaboration and an illustration. Computers & Industrial Engineering, 129, 31-43.
Johansen, S. V., Bendtsen, J. D., Martin, R., & Mogensen, J. (2019). Broiler weight forecasting using dynamic neural network models with input variable selection. Computers and Electronics in Agriculture, 159, 97-109.
Kaewtapee, C., Khetchaturat, C., & Bunchasak, C. (2011). Comparison of growth models between artificial neural networks and nonlinear regression analysis in Cherry Valley ducks. Journal of Applied Poultry Research, 20(4), 421-428. doi:10.3382/japr.2010-00223
Kim, K., Kim, D. K., Noh, J., & Kim, M. (2018). Stable Forecasting of Environmental Time Series via Long Short Term Memory Recurrent Neural Network. IEEE Access, 6, 75216-75228. doi:10.1109/ACCESS.2018.2884827
Kuhi, H. D., Porter, T., López, S., Kebreab, E., Strathe, A., Dumas, A., . . . France, J. (2010). A review of mathematical functions for the analysis of growth in poultry. World's Poultry Science Journal, 66(2), 227-240.
Kuo, C.-J., Chien, C.-F., & Chen, J.-D. (2011). Manufacturing Intelligence to Exploit the Value of Production and Tool Data to Reduce Cycle Time. IEEE Transactions on Automation Science and Engineering, 8(1), 103-111. doi:10.1109/tase.2010.2040999
Lin, K.-Y., Chien, C.-F., & Kerh, R. (2016). UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices. Computers & Industrial Engineering, 99, 487-502.
Lin, Y.-H., Chien, C.-F., & Yu, C.-M. (2015). UNISON DECISION ANALYSIS FRAMEWORK FOR WORKFORCE PLANNING FOR SEMICONDUCTOR FABS AND AN EMPIRICAL STUDY. International Journal of Industrial Engineering, 22(5).
Liu, Y., Ma, X., Shu, L., Hancke, G. P., & Abu-Mahfouz, A. M. (2021). From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Transactions on Industrial Informatics, 17(6), 4322-4334. doi:10.1109/tii.2020.3003910
Lopes, A. Z., Ferreira, L., Junior, T. Y., & Lacerda, W. S. (2009). Modeling productive performance of broiler chickens with artificial neural network. Paper presented at the Livestock Environment VIII, 31 August–4 September 2008, Iguassu Falls, Brazil.
Mahale, R. B., & Sonavane, S. (2016). Smart Poultry Farm Monitoring Using IOT and Wireless Sensor Networks. International Journal of Advanced Research in Computer Science, 7(3), 187-190.
Mollah, M. B. R., Hasan, M. A., Salam, M. A., & Ali, M. A. (2010). Digital image analysis to estimate the live weight of broiler. Computers and Electronics in Agriculture, 72(1), 48-52. doi:10.1016/j.compag.2010.02.002
Mortensen, A. K., Lisouski, P., & Ahrendt, P. (2016). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 123, 319-326.
OECD, Food, & Nations, A. O. o. t. U. (2019). OECD-FAO Agricultural Outlook 2019-2028.
OECD, Food, & Nations, A. O. o. t. U. (2020). OECD-FAO Agricultural Outlook 2020-2029.
Park, Kim, Lee, Kim, Song, & Kim. (2019). Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network. Atmosphere, 10(11). doi:10.3390/atmos10110718
Raj, A. A. G., & Jayanthi, J. G. (2018). IoT-based real-time poultry monitoring and health status identification. Paper presented at the 2018 11th International Symposium on Mechatronics and its Applications (ISMA).
Rapela, M. A. (2019). Fostering Innovation for Agriculture 4.0: Springer.
Ribeiro, R., Casanova, D., Teixeira, M., Wirth, A., Gomes, H. M., Borges, A. P., & Enembreck, F. (2019). Generating action plans for poultry management using artificial neural networks. Computers and Electronics in Agriculture, 161, 131-140. doi:10.1016/j.compag.2018.02.017
Rojas-Downing, M. M., Nejadhashemi, A. P., Harrigan, T., & Woznicki, S. A. (2017). Climate change and livestock: Impacts, adaptation, and mitigation. Climate Risk Management, 16, 145-163. doi:10.1016/j.crm.2017.02.001
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. doi:10.1038/323533a0
Sakomura, N., Longo, F., Oviedo-Rondon, E., Boa-Viagem, C., & Ferraudo, A. (2005). Modeling energy utilization and growth parameter description for broiler chickens. Poultry Science, 84(9), 1363-1369.
Van der Vorst, J. G., Dijk, S. J. v., & Beulens, A. J. (2001). Supply chain design in the food industry. The International Journal of Logistics Management, 12(2), 73-86.
Wang, C.-Y., Chen, Y.-J., & Chien, C.-F. (2021). Industry 3.5 to empower smart production for poultry farming and an empirical study for broiler live weight prediction. Computers & Industrial Engineering, 151. doi:10.1016/j.cie.2020.106931
Wathes, C. M., Kristensen, H. H., Aerts, J. M., & Berckmans, D. (2008). Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall? Computers and Electronics in Agriculture, 64(1), 2-10. doi:10.1016/j.compag.2008.05.005
Wilson, W. O. (1948). Some effects of increasing environmental temperatures on pullets. Poultry Science, 27(6), 813-817.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023
Yandun Narvaez, F., Reina, G., Torres-Torriti, M., Kantor, G., & Cheein, F. A. (2017). A Survey of Ranging and Imaging Techniques for Precision Agriculture Phenotyping. IEEE/ASME Transactions on Mechatronics, 22(6), 2428-2439. doi:10.1109/tmech.2017.2760866
Yu, C.-M., Chien, C.-F., & Kuo, C.-J. (2017). Exploit the Value of Production Data to Discover Opportunities for Saving Power Consumption of Production Tools. IEEE Transactions on Semiconductor Manufacturing, 30(4), 345-350. doi:10.1109/tsm.2017.2750712
電子全文 電子全文(網際網路公開日期:20270206)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top