International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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An AI-Driven Integrated Platform for Rural Farmer Healthcare and Precision Agricultural Advisory

Author(s) Ms. Teesha Dembla, Ms. Puja Gupta, Mr. Yash Seth, Mr. Vandit Kothe, Prof. Neeraj Kumar Rathore
Country India
Abstract Rural farming communities in India face a persistent dual burden: inadequate access to healthcare professionals and limited availability of expert agricultural advisory services. This paper presents the design, implementation, and evaluation of AgriHealth AI, a full-stack web application that addresses both problems through a unified artificial intelligence platform. The system integrates Google’s Gemini 2.0 Flash multimodal model with a React 18 and TypeScript frontend, an Express.js backend, and a MongoDB Atlas cloud database to deliver five core AI-powered services: symptom-based health triage, personalized diet planning, image-based crop disease detection, soil report analysis, and a conversational agricultural health assistant. A structured prompt-engineering methodology guides the AI model toward domain-specific, farmer-appropriate outputs across all five modules. The authentication layer uses a dual-token JWT strategy with bcryptjs password hashing to ensure data security. Experimental results from controlled testing confirm that the system returns health severity assessments, structured crop disease diagnoses, and soil fertility recommendations within an average of two seconds. The platform is designed to function on standard mobile browsers, removing the barrier of specialized hardware for its intended rural user base. This work demonstrates that off-the-shelf large language model APIs, combined with disciplined software engineering, can close critical service gaps for underserved agricultural populations
Keywords precision agriculture, rural digital health, large language models, crop disease detection, soil analysis, AI-powered triage, Gemini 2.0 Flash, full-stack web application, multimodal AI, diet planning.
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-10
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E-ISSN 2582-2160
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