{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:59:45Z","timestamp":1791482385702,"version":"4.3.4"},"reference-count":41,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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\u2014Safety, Technical Accuracy, Relevance, Actionability, and Clarity\u2014indicating 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0359596","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:46:41Z","timestamp":1791481601000},"page":"e0359596","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multimodal context-aware AI recommender for smart farming"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4103-5344","authenticated-orcid":true,"given":"John Telesphory","family":"Mhagama","sequence":"first","affiliation":[{"name":"Kurukshetra University","place":["Kurukshetra, Haryana, India"],"department":["Department of Computer Science and Applications"]},{"name":"Mtech Mavens Company Limited","place":["Dar es Salaam, Tanzania"],"department":["Department of Research and Innovation"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanwal","family":"Garg","sequence":"additional","affiliation":[{"name":"Kurukshetra University","place":["Kurukshetra, Haryana, India"],"department":["Department of Computer Science and Applications"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"pone.0359596.ref001"},{"key":"pone.0359596.ref002","first-page":"100556","article-title":"A deep learning approach for maize lethal necrosis and maize streak virus disease detection","volume":"16","author":"T O\u2019Halloran","year":"2024","journal-title":"Mach Learn Appl"},{"key":"pone.0359596.ref003","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using Deep Learning for Image-Based Plant Disease Detection","volume":"7","author":"SP Mohanty","year":"2016","journal-title":"Front Plant Sci"},{"key":"pone.0359596.ref004"},{"issue":"3","key":"pone.0359596.ref005","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1038\/s41559-018-0793-y","article-title":"The global burden of pathogens and pests on major food crops","volume":"3","author":"S Savary","year":"2019","journal-title":"Nat Ecol Evol"},{"key":"pone.0359596.ref006","doi-asserted-by":"crossref","first-page":"1384709","DOI":"10.3389\/frai.2024.1384709","article-title":"Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania","volume":"7","author":"F Mayo","year":"2024","journal-title":"Front Artif Intell"},{"issue":"22","key":"pone.0359596.ref007","first-page":"3151","article-title":"A Deep Learning Model for Accurate Maize Disease Detection Based on State-Space Attention and Feature Fusion","volume":"13","author":"T Zhu","year":"2024","journal-title":"Plants (Basel)"},{"key":"pone.0359596.ref008","doi-asserted-by":"crossref","first-page":"1590870","DOI":"10.3389\/fpls.2025.1590870","article-title":"Breeding for resistance to maize streak virus: challenges, progress and future directions: a review","volume":"16","author":"M Mushayi","year":"2025","journal-title":"Front Plant Sci"},{"key":"pone.0359596.ref009","unstructured":"Mohanty SP, Hughes D, Salath\u00e9 M. 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