Knowledge-Based Decision Support System Framework for Swine Productive Management

Knowledge-Based Decision Support System Framework for Swine Productive Management
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-5
Year of Publication : 2024
Author : Evelyn M. Baesa, Thelma D. Palaoag
DOI : 10.14445/22315381/IJETT-V72I5P120

How to Cite?

Evelyn M. Baesa, Thelma D. Palaoag, "Knowledge-Based Decision Support System Framework for Swine Productive Management," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 194-202, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P120

Abstract
The swine industry plays a vital role in global food production, and efficient swine management is essential for ensuring sustainability and profitability. This research presents the design and development of a robust Decision Support System (DSS) framework tailored to the specific needs of swine productive management. The proposed framework integrates data analytics, predictive modeling, and expert knowledge to optimize breeding efficiency, minimize resource waste, and enhance overall farm productivity. This article discusses the theoretical foundations, architecture, and key components of the DSS framework, providing insights into its potential applications within the swine industry. The framework's adaptability and scalability make it a valuable tool for swine producers and researchers alike, offering a comprehensive solution to address the complex challenges of swine management. Furthermore, this provides to equip swine farmers technological advantage and enables strategic methods in managing their farms. Through trend analysis, recommendation models, and user feedback, the developed system empowers farmers with actionable insights to improve breeding efficiency and overall farm productivity.

Keywords
Decision Support System, Agriculture, Swine production, Farming, Technology.

References
[1] Food and Agriculture Organization, World Food and Agriculture - Statistical Yearbook 2022, FAO, pp. 1-382, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Shad Mahfuz et al., “Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming,” Sustainability, vol. 14, no. 5, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Francisco Gutiérrez et al., “A Review of Visualisations in Agricultural Decision Support Systems: An HCI Perspective,” Computers and Electronics in Agriculture, vol. 163, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] P. Boettcher et al., “World Livestock: Transforming the Livestock Sector through the Sustainable Development Goals,” Food and Agriculture Organization of the United Nations, pp. 1-220, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Francisco Tardelli da Silva et al., “Open Innovation in Agribusiness: Barriers and Challenges in the Transition to Agriculture 4.0,” Sustainability, vol. 15, no. 11, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Zhaoyu Zhai et al., “Decision Support Systems for Agriculture 4.0: Survey and Challenges,” Computers and Electronics in Agriculture, vol. 170, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Fahim Bin Alam et al., “Analysis of the Drivers of Agriculture 4.0 Implementation in the Emerging Economies: Implications towards Sustainability and Food Security,” Green Technologies and Sustainability, vol. 1, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Franco da Silveira et al., “Farmers’ Perception of the Barriers that Hinder the Implementation of Agriculture 4.0,” Agricultural Systems, vol. 208, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Charvi Arora et al., “Integrating Agriculture and Industry 4.0 under ‘Agri-Food 4.0’ to Analyze Suitable Technologies to Overcome Agronomical Barriers,” British Food Journal, vol. 124, no. 7, pp. 2061-2095, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] William L. Flowers, “Reproductive Management of Swine,” Animal Agriculture, pp. 283-297, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] R.D. Boyd et al., “Review: Innovation through Research in the North American Pork Industry,” Animal, vol. 13, no. 12, pp. 2951-2966, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Shunli Wang et al., “The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming,” Sensors, vol. 22, no. 17, pp. 1-23, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Mireia Moix Atienza, “eFat, a Prototype of a Cloud Decision Support System for Optimal Delivery of Fattened Pigs to the Abattoir,” University of Lleida, pp. 1-48, 2019.
[Google Scholar] [Publisher Link]
[14] Margaret Rouse, What is a Decision Support System Mean?, Decision Support System, Techopedia, 2018. [Online]. Available: https://www.techopedia.com/definition/770/decision-support-system-dss
[15] Ting-Peng Liang, “Recommendation Systems for Decision Support: An Editorial Introduction,” Decision Support Systems, vol. 45, no. 3, pp. 385-386, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Henderi et al., “Covid-19 Series: A Rule-Based Decision Support System for Analysis Behavior of People while Working from Home,” IOP Conference Series: Materials Science and Engineering, vol. 1007, pp. 1-7, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Lea D. Austero, Michael Angelo D. Brogada, and Rommel Evan J. Paje, “Determining Resource Capacity in Disaster Relief through a Model-Driven Decision Support System,” 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, pp. 225-228, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Hao Wang et al., “Smart Decision-Support System for Pig Farming,” Drones, vol. 6, no. 12, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Betty R. McConn et al., “A Behavior and Physiology-Based Decision Support Tool to Predict Thermal Comfort and Stress in Non-Pregnant, Mid-Gestation, and Late-Gestation Sows,” Journal of Animal Science and Biotechnology, vol. 13, no. 135, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Paolo Aversa, Laure Cabantous, and Stefan Haefliger, “When Decision Support Systems Fail: Insights for Strategic Information Systems from Formula 1,” The Journal of Strategic Information Systems, vol. 27, no. 3, pp. 221-236, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Sarah Tifany Silva, Francini Hak, and José Machado, “Rule-based Clinical Decision Support System using the OpenEHR Standard,” Procedia Computer Science, vol. 201, pp. 726-731, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] 8 Decision Support System Examples to Guide Decision-Making, Indeed.com, 2023. [Online]. Available: https://www.indeed.com/career-advice/career-development/decision-support-system-examples
[23] Kwok W. Cheungl, and Mei-Peng Cheong, “Intelligent On-Line Decision Support Tools for Market Operators,” 2007 International Conference on Intelligent Systems Applications to Power Systems, Kaohsiung, Taiwan, pp. 1-6, 2007.
[CrossRef] [Google Scholar] [Publisher Link]