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@article{184328,
        author = {Varsha K A and Kishor Kumar K and Ujwal U J and Prajna S N},
        title = {Artificial Intelligence Driven Crop Yield Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {951-956},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184328},
        abstract = {This paper presents an AI-based approach for predicting crop yield using Support Vector Machine (SVM). Key features include rainfall, temperature, pH, and fertilizer input. The model achieved an R² score of 0.91 and RMSE of 125.64. The dataset was processed with standard normalization, and a Flask-based web interface was developed. The system supports scalable smart agriculture through real-time, data-driven decision-making.},
        keywords = {Crop Yield, Machine Learning (ML), Support Vector Machine (SVM), Agriculture, Flask, Smart Farming.},
        month = {September},
        }