{
  "abstract": "Efficient irrigation management requires complementary information on atmospheric demand, soil conditions, and crop water stress. This study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within a unified monitoring and control framework. The system combines LoRa communication, ESP32-based sensor nodes, soil and meteorological sensing, FAO-56 reference evapotranspiration (ET0), and canopy-temperature-based Crop Water Stress Index (CWSI). Irrigation decisions rely on the complementary use of ET0, in situ soil measurements, and CWSI rather than on a single indicator. A hybrid time-, event-, and query-driven acquisition strategy was implemented to adapt node activity and limit communication overhead. The system was deployed under outdoor conditions in Oujda, Morocco, demonstrating integrated sensing, wireless data transmission, crop-stress monitoring, and automated irrigation control. Energy characterization further showed distinct consumption profiles across sensing, communication, actuation, and low-power operating states, supporting the use of duty cycling to limit active node operation. The results demonstrate the feasibility of integrating environmental, soil, and crop-level information within a low-power IoT framework for adaptive irrigation management.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Yassine Ayat"
    },
    {
      "name": "Ali El Moussati"
    },
    {
      "name": "Oumayma Rachdi"
    },
    {
      "name": "Maryem Dinar"
    },
    {
      "name": "Abdelaziz El Aouni"
    },
    {
      "name": "Hajar Karkri"
    },
    {
      "name": "Mohammed Benzaouia"
    },
    {
      "name": "Wiame Benzekri"
    },
    {
      "name": "Ismail Mir"
    },
    {
      "name": "Aumeur El Amrani"
    },
    {
      "name": "Abdelmalek Mimouni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0ph2r5pm74xnw.bib",
  "cited_by": 0,
  "doi": "10.3390/iot7030059",
  "fulltext_url": null,
  "id": "0ph2r5pm74xnw",
  "issns": [
    "2624-831X"
  ],
  "landing_url": "https://hal.science/hal-05736703v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sde"
    ],
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7171362850",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-27",
  "publisher": "MDPI",
  "ris": "https://research.draconitic.com/paper/0ph2r5pm74xnw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:27Z",
      "native_id": "hal-05736703",
      "source": "hal",
      "url": "https://hal.science/hal-05736703v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:0.sde",
      "subfield": "environmental-science"
    }
  ],
  "title": "Design and Experimental Validation of a Low-Power IoT-Based Smart Irrigation System Using LoRa, ET0, and Crop Water Stress Index for Precision Agriculture",
  "title_text": "Design and Experimental Validation of a Low-Power IoT-Based Smart Irrigation System Using LoRa, ET0, and Crop Water Stress Index for Precision Agriculture",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:27Z",
  "url": "https://research.draconitic.com/paper/0ph2r5pm74xnw",
  "venue": "IoT",
  "versions": []
}