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@article{189891,
        author = {Kashish Aswani and Shreya Ambat and Om Bokare and Gaurav Chavhan and Ms. N. L. Pariyal and Mr. M. R. Chennoji},
        title = {MRIDGUN – SMART FERTILIZER RECOMMENDATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2638-2643},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189891},
        abstract = {Agriculture faces persistent challenges related to inefficient fertilizer usage, declining soil fertility, and environmental degradation caused by excessive chemical inputs. Limited access to real-time soil nutrient information often leads to generalized fertilizer application practices, resulting in reduced crop productivity and sustainability. This paper presents MRIDGUN – Smart Fertilizer Recommendation System, a practical framework that integrates real-time soil NPK (Nitrogen, Phosphorus, and Potassium) sensing with embedded processing, secure backend data management, and analytical support. The system collects soil nutrient data through sensor-based monitoring, validates and stores it for historical analysis, and assists in identifying nutrient imbalance patterns using data-driven techniques. Secure user authentication ensures controlled access to agricultural data, while the system design emphasizes affordability, scalability, and real-world applicability. The conceptualization and refinement of MRIDGUN were influenced by real-world farmer interactions and academic guidance, ensuring practical relevance. Experimental observations indicate that the proposed system provides timely and reliable soil nutrient insights, supporting informed fertilizer application and sustainable agricultural practices.},
        keywords = {Smart Agriculture, Precision Farming, Soil Nutrient Monitoring, Fertilizer Recommendation System, IoT, Machine Learning, Sustainable Agriculture.},
        month = {January},
        }