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Artificial intelligence

Artificial Intelligence in Sustainable Farming: A Growing Imperative for Efficient Agriculture

Strategic Insights

— 1 min read

Researchers from the Department of Computer Science and Engineering have highlighted the crucial role of artificial intelligence (AI) and machine learning (ML) in sustainable farming systems. The study emphasizes the need for data-driven decision support systems that can effectively manage soil fertility and environmental conditions. The current state of AI-based technologies, including distant sensing methods, sensor networks, and robotics, is being explored for their potential in precise diagnoses and efficient soil management.

Key Takeaways:

  • Traditional fertility management methods have significant drawbacks, including excessive chemical use, lack of real-time data, and resource waste, leading to soil weakening and lower crop yields.
  • AI and ML can help mitigate these issues by providing location-specific data and enabling data-driven decisions in soil management.
  • The study focuses on distant sensing methods, sensor networks, and robotics as key technologies for sustainable farming systems.
  • The current state of AI-based technologies is being addressed, with a focus on their increasing use in more precise diagnoses and effective soil management.
  • The research highlights the importance of Machine Learning model awareness, worldwide standards, and soil data availability in making data-driven decisions in soil management.
  • A study, titled "Artificial Intelligence-based Green Technologies for Efficient Agriculture," was recently published in the ITM Web of Conferences, 2025, with the article number 79():01027.
  • The study's findings suggest that AI and ML can play a crucial role in efficient agriculture, particularly in managing soil fertility and environmental conditions.

Statistics:

  • The study emphasizes the need for data-driven decision support systems in efficient agriculture.
  • AI and ML can help mitigate the drawbacks of traditional fertility management methods, including excessive chemical use and resource waste.
  • The study highlights the importance of location-specific data in soil management, with AI and ML enabling data-driven decisions.
  • The current state of AI-based technologies is being explored, with applications in precise diagnoses and efficient soil management.
  • Worldwide standards and Machine Learning model awareness are essential for effective soil management.

Sources:

  • Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management
  • ITM Web of Conferences, 2025, article number 79():01027
  • EDP Sciences, publisher of ITM Web of Conferences
  • Researcher A Mamatha, Department of Computer Science and Engineering
  • Researcher S Shalini, co-author of the study
  • NewsRx. Studies from Department of Computer Science and Engineering Further Understanding of Artificial Intelligence (Artificial Intelligence-based Green Technologies for Efficient Agriculture). Information Technology Newsweekly. October 28, 2025; p 825.

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