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@article{187505,
        author = {Shristi and Shreya KV and Raveena Kumari and DR. Sreenivasa B C},
        title = {Smart Crop Recommendation Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5636-5643},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187505},
        abstract = {The Smart Crop Recommendation System is a data-driven agricultural decision-support framework that leverages machine learning to identify the most suitable and economically beneficial crops for a given region. The model evaluates key soil attributes such as nitrogen, phosphorus, potassium, pH, along with meteorological variables including temperature, humidity, and rainfall. Using a Random Forest–based prediction engine, the system processes these multi-dimensional inputs to generate the top three crop options best aligned with local conditions. Real-time weather information is incorporated through the OpenWeatherMap API, enabling adaptive and location-specific recommendations. The system also provides additional insights through profit estimation, irrigation guidance, and farmer feedback analysis, ensuring a comprehensive decision-making workflow. By integrating environmental intelligence with predictive analytics, the proposed system supports sustainable farming practices, improves resource utilization, and empowers farmers to make informed and profitable crop choices.},
        keywords = {Machine Learning, Random Forest Classifier, Real-Time Weather Integration, Smart Crop Recommendation, Soil Nutrient Analysis.},
        month = {November},
        }