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AI-Driven Crop Monitoring and Management for Sustainable Agriculture

In: Transforming Agriculture through Artificial Intelligence for Sustainable Food Systems

Author

Listed:
  • Pritha Ghosh

    (Lovely Professional University)

  • Ajinkya Markad

    (Lovely Professional University)

  • Chandan Maharana

    (Murdoch University)

Abstract

Agriculture 5.0 seeks to create a sustainable food production system that balances affordability with ecosystem protection. Advancements in artificial intelligence (AI) are transforming agriculture by improving crop yield predictions and pest management. AI-based systems utilize diverse data sources and predictive models to assist farmers in strategic planning and informed decision-making. One notable innovation is AI-powered pest detection, which employs acoustic methods for pest classification and enables automated pesticide application. Farmers can remotely access these systems, reducing the need for field visits. Additionally, robotics and drones equipped with cameras and sensors enhance crop monitoring by providing real-time insights into crop health and identifying potential pest or disease threats. These technologies improve precision, save labor costs, and streamline crop management practices. In India, AI is becoming crucial for ensuring food security, enhancing yields, and minimizing economic losses. However, challenges remain, including the need for more accurate datasets, transparent models to build user trust, and the scalability of AI tools across various crops and farming practices. This chapter explores AI-based solutions for real-time crop monitoring and pest management, emphasizing their potential to revolutionize agriculture while addressing operational and scalability challenges.

Suggested Citation

  • Pritha Ghosh & Ajinkya Markad & Chandan Maharana, 2025. "AI-Driven Crop Monitoring and Management for Sustainable Agriculture," Springer Books, in: Priyanka Lal & Pradeep Mishra (ed.), Transforming Agriculture through Artificial Intelligence for Sustainable Food Systems, chapter 0, pages 39-53, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-4795-8_3
    DOI: 10.1007/978-981-96-4795-8_3
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