Keskin, AbdulkadirErsin, IrfanAtalan, Abdulkadir2025-01-272025-01-2720241018-70812309-8694https://doi.org/10.36899/JAPS.2024.5.0811https://hdl.handle.net/20.500.12428/21127This study aims to estimate the price fluctuations of essential grain products, namely bread wheat (Triticum aestivum), durum wheat (Triticum durum), barley (Hordeum vulgare), and corn (Zea mays), in T & uuml;rkiye using machine learning (ML) algorithms. Using data from January 2, 2020, to January 10, 2023, the study employs algorithms such as random forest (RF), neural network (NN), support vector machine (SVM), and linear regression (LR). Independent variables include oil prices, currency exchange rates, and grain production volumes. The random forest (RF) algorithm provided the best results with the highest R2 values, while NN and LR showed relatively lower performance. The study highlights the significant impact of production and consumption volumes on grain prices and underscores the importance of ML algorithms in predicting these prices amidst changing conditions. Investments in agricultural technologies should be increased to improve data collection and analysis processes, as this is crucial for preventing price fluctuations in the agricultural sector.eninfo:eu-repo/semantics/closedAccessAgricultural productsgrainsdurum wheatbread wheatcornbarleymachine learning algorithmsprice estimationArticle3451290130210.36899/JAPS.2024.5.0811N/AWOS:0013566399000192-s2.0-85209904344Q3