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@article{199331,
        author = {Shubham Karale and Mansi Lakade and Prof Jyoti Manoorkar},
        title = {Smart Agri Chain: An Integrated Machine Learning Framework for Agricultural Price Prediction and Supply Chain Transparency},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15579-15584},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199331},
        abstract = {Agriculture continues to be one of the most important sectors in developing countries, especially in terms of food production, employment, and economic stability. However, despite its importance, farmers still face several challenges when it comes to selling their produce effectively. One of the major issues is the lack of transparency in the agricultural supply chain, which often results in limited awareness about actual market prices and increased dependence on intermediaries.
Another important concern is the fluctuation in crop prices. These variations usually happen due to seasonal changes, demand differences, arrival quantities, and other external conditions. Because of this, farmers often find it difficult to decide the right time and place to sell their crops.
The idea behind this work is to develop a system that can assist farmers in such situations. This paper presents Smart Agri Chain, which combines machine learning techniques with a digital platform to predict crop prices and improve transparency. The system makes use of historical market data to understand pricing patterns and generate future estimates. In most cases, these predictions provide a reasonable idea of how prices might behave.
The platform is designed as a web-based application so that it remains simple and accessible. Farmers can enter crop-related information and get predicted prices in an easy-to-understand format. The system follows multiple steps including data collection, preprocessing, model training, and prediction generation.
The results show that the system is able to identify useful patterns in the data and provide meaningful predictions. While the predictions may not always be exact, they can still help farmers make better decisions and reduce uncertainty in the selling process.},
        keywords = {},
        month = {April},
        }