{
  "id": 9820,
  "url": "https://doi.org/10.1007/s44279-025-00247-y",
  "title": "Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, and future prospects",
  "summary": "The integration of Internet of Things (IoT) sensors and Machine Learning (ML) technologies has transformed precision agriculture by enabling data-driven, adaptive, and efficient farming practices. IoT sensors provide continuous, high-resolution monitoring of critical agricultural parameters, including soil health, crop growth, and environmental conditions. Coupled with advanced ML algorithms, this data facilitates predictive analytics and real-time decision-making, optimizing resource utilizatio",
  "authors": "Val Hyginus Udoka Eze, Esther Chidinma Eze, George Uwadiegwu Alaneme, Pius Erheyovwe Bubu, Ezekiel Oluwaseun Ejiofor Nnadi, Michael Ben Okon",
  "category": "research",
  "topics": "healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2025-05-26T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:34:00.825Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9820",
  "original_url": "https://doi.org/10.1007/s44279-025-00247-y",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}