{
  "id": 17705,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11659-7",
  "title": "Deep learning in precision phytopathology: a comprehensive survey of CNN architectures for disease detection and severity quantification",
  "summary": "Plant disease detection and severity estimation are crucial to sustainable agricultural productivity and global food security, necessitating the need for efficient and accurate diagnostic tools. This paper systematically analyzes 137 studies using the PRISMA 2020 framework, focusing on deep learning methods used in detecting and estimating plant disease severity. The review covers classification, detection, segmentation, and regression approaches to diagnosing plant diseases and quantifying seve",
  "authors": null,
  "category": "research",
  "topics": "jobs-economy,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-08T00:00:00.000Z",
  "fetched_at": "2026-08-09T05:10:05.170Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/17705",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11659-7",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}