{
  "id": 14640,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1808028",
  "title": "Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India",
  "summary": "Agriculture is increasingly characterized by a data paradox, while the sector generates massive volumes of genomic, climatic, remote sensing, and field data. Translating this information into actionable, farm-level insights remains a critical bottleneck. Traditional advisory mechanisms cannot operate at the spatial scales or provide the context-specificity needed for climate adaptation and food security. The work presents GenAI as a transformative interface that makes advanced agricultural scien",
  "authors": "Shalini Gakhar",
  "category": "research",
  "topics": "environment,biotech",
  "orgs": null,
  "regions": "india",
  "published_at": "2026-07-29T00:00:00.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/14640",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1808028",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}