{
  "abstract": "Agricultural productivity in developing regions is significantly affected by plant diseases and pest infestations, making early detection and timely intervention essential for improving crop yield and food security. This study proposes a multimodal context-aware AI recommendation framework that integrates maize leaf image analysis and environmental conditions to support intelligent agricultural decision-making. The framework combines a deep learning-based image classification model with weather variables, including temperature, humidity, rainfall, and solar radiation, through a multimodal fusion model for maize disease diagnosis. Based on the predicted crop condition, a context-aware recommendation engine integrates rule-based agronomic knowledge with the Qwen2.5:7B large language model to generate reliable and actionable natural-language recommendations for farmers. The generated recommendations are subsequently evaluated using Llama 3.1:8B in a blind LLM-based evaluation framework. Experiments were conducted using maize leaf images from the YEESI Lab dataset and a 61-day environmental dataset from the NASA POWER database, obtained for the geographic coordinates of Morogoro, Tanzania. Three maize conditions were considered: healthy plants, aphid infestation, and maize streak virus infection. The image-based model achieved an accuracy of 91%, while the weather-based model showed lower performance due to overlapping environmental characteristics among disease classes. The proposed multimodal fusion model achieved a classification accuracy of 94%, demonstrating the effectiveness of integrating visual and environmental information for disease diagnosis. The recommendation engine achieved an overall evaluation score of 4.80/5.00 across five quality dimensions—Safety, Technical Accuracy, Relevance, Actionability, and Clarity—indicating that the generated recommendations were agronomically consistent, context-aware, and actionable. The proposed framework provides an end-to-end AI-driven decision-support solution that integrates multimodal disease diagnosis, and context-aware recommendation for precision agriculture.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, Haryana, India",
    "Department of Research and Innovation, Mtech Mavens Company Limited, Dar es Salaam, Tanzania"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "John Telesphory Mhagama"
    },
    {
      "affs": [
        1
      ],
      "name": "Kanwal Garg"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ksp060b4fatx.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359596",
  "fulltext_url": null,
  "id": "8ksp060b4fatx",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0359596",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ],
    [
      "plos",
      "/Biology and life sciences/Agriculture/Agronomy"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Grasses/Maize"
    ],
    [
      "plos",
      "/Biology and life sciences/Plant science/Plant anatomy/Leaves"
    ],
    [
      "plos",
      "/Biology and life sciences/Plant science/Plant pathology"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology/Humidity"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Electromagnetic radiation/Solar radiation"
    ],
    [
      "plos",
      "/Research and analysis methods/Animal studies/Experimental organism systems/Model organisms/Maize"
    ],
    [
      "plos",
      "/Research and analysis methods/Animal studies/Experimental organism systems/Plant and algal models/Maize"
    ],
    [
      "plos",
      "/Research and analysis methods/Database and informatics methods/Information retrieval"
    ],
    [
      "plos",
      "/Research and analysis methods/Model organisms/Maize"
    ]
  ],
  "openalex": "W7221033947",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359596&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/8ksp060b4fatx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0359596",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359596"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:plant science",
      "subfield": "plant-biology"
    },
    {
      "field": "earth",
      "origin": "plos:meteorology",
      "subfield": "atmospheric"
    }
  ],
  "title": "A multimodal context-aware AI recommender for smart farming",
  "title_text": "A multimodal context-aware AI recommender for smart farming",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/8ksp060b4fatx",
  "venue": "PLOS One",
  "versions": []
}