Open Access
Research Paper
Peer Reviewed

Paper Title

AI-Driven Precision Farming and Farmer Support Ecosystem

Article Identifiers

Registration ID: IJNRD_328384

Published ID: IJNRD2609118

: https://doi.org/10.56975/ijnrd.v11i9.328384

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Keywords

Precision Agriculture, Artificial Intelligence, MERN Stack, Crop Disease Detection, Agricultural Decision Support System, Multilingual Chatbot, Role-Based Access Control, Smart Farming, Recommendation System, Web-Based Agriculture Platform.

Abstract

Agriculture continues to play a vital role in the economies of many developing countries, where millions of people depend on farming for their livelihoods. Despite technological advancements, farmers often struggle to obtain timely agricultural advice, reliable weather information, accurate disease diagnosis, and up-to-date market prices through a single accessible platform. Existing digital solutions usually address only one of these challenges, requiring farmers to switch between multiple applications and services. This paper presents the design and implementation of an AI-Driven Precision Farming and Farmer Support Ecosystem, a unified web-based platform that integrates essential agricultural services within a single environment. The system is developed using the MERN stack (MongoDB, Express.js, React, and Node.js) and incorporates artificial intelligence to improve decision-making and user interaction. The platform supports three user roles—Farmer, Agriculture Expert, and Administrator—through secure JSON Web Token (JWT) authentication and Role-Based Access Control (RBAC). Key features include a multilingual AI chatbot powered by the Gemini API, image-based crop disease identification, weather forecasting, market price analysis, crop and fertilizer recommendation, irrigation guidance, yield estimation, real-time expert consultation using Socket.IO, and an administrative analytics dashboard. The platform follows an iterative software development approach with modular architecture and API-driven service integration to ensure scalability and maintainability. Rather than reporting experimentally validated performance metrics, this work focuses on demonstrating the successful integration of multiple AI-enabled agricultural services into a single functional prototype. The developed system illustrates how an intelligent, role-aware digital platform can simplify access to agricultural information, reduce dependence on fragmented tools, and support farmers in making more informed decisions throughout the cultivation process.

How To Cite (APA)

Dr. Shweta Marigoudar & Rachana R Jadav (September-2026). AI-Driven Precision Farming and Farmer Support Ecosystem. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(9), b145-b171. https://doi.org/10.56975/ijnrd.v11i9.328384

Citation

Issue

Other Publication Details

Paper Reg. ID: IJNRD_328384

Published Paper Id: IJNRD2609118

Research Area: Other area not in list

Author Type: Indian Author

Country: Davangere, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2609118.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2609118

Crossref DOI: https://doi.org/10.56975/ijnrd.v11i9.328384

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Publication Timeline

Paper Submission
08-09-2026
Peer Review
Through Scholar9.com Platform
Paper Acceptance
13-09-2026
Paper Publication
15-09-2026

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