ICT-agriculture research in India: a bibliometric analysis and review

What is it about?

The bibliometric analysis and review of ICT-agriculture research in the context of India were conducted using data from 533 studies published between 2005 and 2025. The findings reveal a significant surge in research output, reflecting heightened academic and practical interest in ICT-driven agricultural solutions. Machine learning (especially neural networks) and deep learning dominate yield prediction models, while IoT and drones are increasingly used for real-time monitoring. Emerging frontiers include big data analytics and district-level predictive modeling to address spatial variability in crop performance. High costs, poor rural internet connectivity, low digital literacy, and resistance to change hinder widespread ICT adoption among smallholder farmers. Policy gaps, such as inconsistent government support, further exacerbate these challenges. India’s research partnerships with the U.S., Saudi Arabia, and the EU highlight opportunities for knowledge exchange, particularly in AI and precision agriculture. Finally, there exists a large potential for AI and ML applications in biodiversity conservation, managing forests, and related services in India.

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