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@article{190213,
        author = {Adishakti and Priyanka Mashetty and Pallavi and Spurti},
        title = {SmartAgriPredict: Machine Learning & AI-Enhanced Crop Price Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2417-2426},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190213},
        abstract = {SmartAgriPredict is a sophisticated agricultural intelligence platform aimed at revolutionizing conventional farming decision-making through precise crop price predictions utilizing Machine Learning (ML) and Artificial Intelligence (AI). The solution incorporates Random Forest Regression, Decision Tree Regression, historical datasets, and real-time AI-driven market intelligence with Gemini 2.5 Flash. In contrast to traditional systems, SmartAgriPredict provides hybrid insights by integrating statistical forecasts with real-time mandi market trends, Minimum Support Price (MSP) updates, and commodities news sourced from the internet. The platform features a responsive front-end developed with React.js with Tailwind CSS, a Node.js backend, and bespoke JavaScript implementations of machine learning algorithms. The system also illustrates multi-year price trends, computes profitability scores, and suggests appropriate crops based on environmental and economic variables. The suggested approach seeks to assist farmers in mitigating risk, enhancing profitability, and making educated agricultural decisions.},
        keywords = {Tailwind CSS, Crop recommendation, Price prediction, Profitability Score},
        month = {January},
        }