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@article{198264,
        author = {Sanika Arun Aher and Asmita Sharad Bhadane and Prerna Ganesh Patil},
        title = {AGRINEXUS – AI-POWERED PLATFORM FOR ENHANCED CROP RECOMMENDATION AND DIRECT MARKET ACCESS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13400-13406},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198264},
        abstract = {The agricultural sector in developing nations faces challenges including unpredictable climatic patterns, suboptimal resource utilization, and limited market transparency. This paper presents AgriNexus, a modular platform that integrates artificial intelligence (AI) for decision support with a direct-to-consumer marketplace. The system utilizes a Node.js/Express.js backend and a MongoDB database. Core intelligence is provided by machine learning (ML) models for personalized crop and fertilizer recommendations, which achieved accuracies of 85.2% and 82.7%, respectively. A multilingual chatbot, "AgroBot," leverages vector-based semantic search via Pinecone to improve contextually relevant information retrieval by 40% over traditional keyword-based methods. Real-time data integration is achieved through weather and news APIs, while financial transactions are secured via the Stripe payment gateway. System validation confirms operational efficacy with average API response times below 500ms under a simulated load of 100 concurrent users. AgriNexus demonstrates a unified model for enhancing agricultural productivity and financial returns through integrated data-driven insights and market access.},
        keywords = {Artificial Intelligence; Crop Recommendation; Direct Market Access; Machine Learning; Precision Agriculture; Semantic Search; Web Platform.},
        month = {April},
        }