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When Everything Becomes Bigger: Big Data for Big Poultry Production.

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Big data and artificial intelligence (AI) offer solutions for sustainable poultry farming by optimizing efficiency and improving animal health. These technologies, including sensors and machine learning, help predict diseases and enhance farm profitability while reducing environmental impact.

Keywords:
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Area of Science:

  • Agricultural Science
  • Data Science
  • Veterinary Medicine

Background:

  • Increasing global demand for poultry products necessitates enhanced production efficiency.
  • Poultry farming faces challenges including environmental impact, resource competition, animal welfare, and disease management.
  • The rise of big data and advanced analytics presents opportunities to address these challenges.

Purpose of the Study:

  • To review the applications of sensor technologies and advanced statistical techniques in poultry farming.
  • To discuss the role of artificial intelligence (AI) and big data in optimizing poultry production and health.
  • To highlight the potential of these technologies in disease prediction and sustainable farming practices.

Main Methods:

  • Review of sensor technologies: optical, acoustic, wearable sensors, infrared thermal imaging, and optical flow.
  • Discussion of advanced statistical techniques: machine learning and deep learning for classification and prediction models.
  • Analysis of pathogen genome sequencing and its application in epidemiological tracking and disease spread reconstruction.

Main Results:

  • Sensor technologies and AI can significantly improve farm profitability and reduce socio-environmental impacts.
  • Machine learning and deep learning models offer effective tools for classification and prediction in poultry systems.
  • Genomic analysis aids in tracking pathogens, understanding disease dynamics, and evaluating control strategies.

Conclusions:

  • AI and sensing technologies are crucial for identifying patterns and solving problems in modern animal farming.
  • Integration of diverse data sources and molecular epidemiology can lead to predictive models for disease anticipation.
  • These technological advancements hold the potential to enhance food production sustainability, safety, and animal welfare.