Strategies in Disease Detection for Precision Livestock Farming

Authors

  • Brenda Jonathan, Larry Justin, Jose Noah Department of Mechanical Engineering, Oregon State University Author

Keywords:

Precision Livestock Farming, AI, Machine Learning, Data Analysis, Livestock Health Monitoring, Disease Detection, Sustainable Livestock Production, Proactive Intervention, Resource Allocation Optimization.

Abstract

Precision Livestock Farming (PLF) has witnessed significant advancements through the integration of Artificial Intelligence (AI), Machine Learning (ML), and Data Analysis. This research explores the transformative impact of these technologies on disease detection in livestock, aiming to enhance health monitoring, improve management practices, and ensure sustainable livestock production. Leveraging AI algorithms, ML models, and sophisticated data analytics, the proposed PLF system enables real-time detection of subtle health anomalies, allowing for proactive intervention and optimized resource allocation. This abstract provides insights into the integration of AI, ML, and data analysis in PLF, highlighting its potential to revolutionize disease management and elevate the overall health and productivity of livestock.

 

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Published

2019-12-23

How to Cite

Strategies in Disease Detection for Precision Livestock Farming. (2019). International Journal of Advanced Engineering Technologies and Innovations, 1(1), 48-65. https://ijaeti.com/index.php/Journal/article/view/58

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