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@article{206824,
        author = {Kiran D N and Dr. Suma R},
        title = {Real-Time Data Driven Precision Agriculture: A Big Data Framework for Location-Specific Crop Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2946-2953},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206824},
        abstract = {Traditional agricultural decision-making often relies on historical, static environmental data, which fails to capture rapid, real-time micro-climatic shifts and localized soil variations. To transition toward true precision agriculture, there is a critical need for systems that can ingest streaming environmental data and provide instantaneous, hyper-localized insights. Objective: This paper proposes a novel, scalable Big Data framework designed to process high-throughput, real-time agricultural streams and deliver context-aware, location-specific crop recommendations.
The proposed architecture integrates a distributed data ingestion layer to accept live streams from IoT sensors, weather APIs, and geospatial feeds. A specialized stream-processing layer handles the high velocity of incoming data, applying location-based filtering to match environmental parameters such as soil moisture, temperature, pH, and humidity with localized geographic coordinates. Leveraging machine learning models optimized for streaming data, the framework dynamically evaluates soil and climatic compatibility to predict the most viable crops for a specific coordinate.
Experimental evaluations demonstrate that the proposed Big Data framework achieves low-latency data processing and high throughput, successfully handling large volumes of concurrent sensor inputs with minimal computational overhead. The recommendation engine shows high accuracy in matching real-time environmental profiles with optimal crop varieties. Significance: By bridging the gap between real-time data streaming and localized agricultural decision support, this framework provides a robust blueprint for scalable, data-driven smart farming, ultimately assisting farmers in optimizing yield and resource efficiency.},
        keywords = {Big Data Framework; Real-Time Stream Processing; Precision Agriculture; Crop Recommendation System; Location-Based Services; IoT in Farming.},
        month = {July},
        }