Machine Learning in Agriculture: A Review.

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.