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Machine Learning in Agriculture: A Review.

Konstantinos G Liakos1, Patrizia Busato2, Dimitrios Moshou3,4

  • 1Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece. k.liakos@certh.gr.

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Machine learning (ML) advances agricultural production by integrating big data and computing power. This review categorizes ML applications in crop, livestock, water, and soil management, highlighting its role in enhancing farmer decision-making.

Keywords:
artificial intelligencecrop managementlivestock managementplanningprecision agriculturesoil managementwater management

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Big data and high-performance computing are driving innovation in agri-technologies.
  • Machine learning (ML) offers new opportunities for data-intensive scientific research in agriculture.
  • The agricultural sector is increasingly leveraging advanced computational tools.

Purpose of the Study:

  • To conduct a comprehensive review of machine learning applications in agricultural production systems.
  • To categorize existing research on ML in agriculture.
  • To demonstrate the benefits of ML for the agri-technologies domain.

Main Methods:

  • Systematic review and analysis of research articles on machine learning in agriculture.
  • Categorization of reviewed works into key agricultural management areas.
  • Filtering and classification of studies based on their application and impact.

Main Results:

  • ML applications were categorized into crop management (yield prediction, disease/weed detection, quality assessment, species recognition), livestock management (animal welfare, production), water management, and soil management.
  • The reviewed literature demonstrates significant benefits of ML technologies across various agricultural domains.
  • Sensor data combined with ML enables real-time, AI-enabled farm management systems.

Conclusions:

  • Machine learning is a transformative technology for modern agriculture.
  • ML provides actionable insights and decision support for farmers through intelligent farm management systems.
  • The integration of ML with sensor data is crucial for advancing agricultural production systems.