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The agriculture sector remains the backbone of the Indian economy, supporting the livelihoods of more than 270 million people. The sector remains vulnerable to persistent stressors such as climate variability and soil degradation, pest outbreaks, and an absence of real-time actionable insights for farmers, leading to low crop yield and sustainability. The present paper proposes Krushi-Verse, a novel smart farming web application targeted at bridging this technological gap by integrating AI, IoT, and multilingual access to comprehensive agricultural insights designed to suit various linguistic communities. It offers real-time analytics on soil and weather conditions, AI-powered crop and fertilizer recommendations, localized market trend analysis, and a multilingual interactive chatbot for seamless farmer engagement.
The architecture of the system is based on a modular microservices approach, which includes a frontend developed in React.js for responsive and user-friendly dashboards; a Python FastAPI-based backend for AI-driven computations and API integrations; and a hybrid database model initially leveraging PostgreSQL and MongoDB, later evolving to API-centered real-time data ingestion from authoritative sources such as ICAR, IMD Pune, and AgMarkNet. The chatbot component, infused with Google Gemini AI and LangChain, offers support in the Marathi, Hindi, and English languages, facilitating inclusive accessibility in addressing literacy barriers with voice and text interfaces.
Pilot deployments with 50 farmers in Pune district recorded significant improvement in user engagement-92% satisfaction, data accuracy, and query responsiveness with average chatbot replies within 2.3 seconds. Krushi-Verse successfully demonstrates how AI-driven precision agriculture tools can empower farmers to better optimize resource use, improve productivity, and enable sustainable farming. This sets the benchmark for scalable, multilingual smart farming solutions in India and similar agro-climatic contexts worldwide.
Technical Keywords: React.js, Python FastAPI, PostgreSQL, MongoDB, Google Gemini AI, LangChain, Retrieval-Augmented Generation (RAG), Microservices Architecture, API Integration, Web Speech API, ChromaDB Vector Database, HTTPS/TLS Encryption, JWT Authentication, Docker Containerization, Redis Caching, ARIA Accessibility Attributes, Natural Language Processing (NLP), Machine Learning (ML), Computer Vision, Semantic Search, Real-time Data Processing.
Domain-Specific/Application Keywords: Smart Farming, Precision Agriculture, Agricultural Advisory Systems, Crop Recommendation Engine, Soil Nutrient Analysis, Fertilizer Management, Weather Analytics, Market Price Prediction, IoT Sensors, Sustainable Agriculture, Farmer Empowerment, Rural Digital Inclusion, Multilingual Chatbot, Voice-Based Interaction, Location-Specific Insights, Taluka-Wise Agricultural Profiles, Crop Yield Optimization, Pest Detection, Disease Management, Mandi Price Tracking.
"A.I POWERED FARMING SYSTEM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b291-b296, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511136.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator