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@article{197992,
        author = {B.Satyanarayana and P.Bala Venkat and G.T.S.V.S.Reddy and K.Heavasanth and A.N.S.Karthikeya},
        title = {CropConnect: An Intelligent Agriculture Marketplace for Price Prediction and Smart Contract Farming},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9756-9760},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197992},
        abstract = {The farming sector is crucial in driving economic development and guaranteeing food availability, especially in emerging nations where many individuals rely on agriculture for their income. Nevertheless, farmers often encounter obstacles such as fluctuating prices, lack of market clarity, restricted access to trustworthy buyers, and excessive reliance on middlemen, which ultimately diminishes their profits. To tackle these challenges, this document introduces CropConnect, a smart online agricultural platform aimed at enabling direct communication between farmers and purchasers through informed decision-making based on data.
The suggested system incorporates sophisticated machine learning methods, such as Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM), to achieve precise crop price forecasts and risk assessments grounded in both historical and current agricultural information. Moreover, a mixed recommendation framework that integrates content-based and collaborative filtering approaches is established to efficiently connect farmers with appropriate buyers. The platform is created using contemporary web technologies, ensuring it is scalable, capable of real-time interactions, and features an intuitive user interface.
Tests reveal that the proposed system delivers enhanced prediction accuracy and improves transaction effectiveness when compared to conventional approaches. By minimizing reliance on middlemen and offering dependable predictive insights, CropConnect aids in fostering transparency, lowering financial uncertainties, and encouraging sustainable and scalable practices in contract farming.},
        keywords = {Contract farming, XGBoost, LSTM, machine learning, agriculture, price prediction, and recommendation systems},
        month = {April},
        }