In this study, agricultural crop classification for 2020 was carried out in Çivril-Baklan Plain, which is located between the borders of Denizli Province, Baklan, Çal and Çivril districts. The open-source Eo-Learn library that uses machine learning and deep learning algorithms in remote sensing studies and multi-temporal Sentinel-2 images was utilized in the classification process. In this study, the parcels registered in the Farmer Registration System (FRS) were used as reference parcels and before using FRS data as ground truth data, pre-editing and rule-based deletion processes were performed. By using Light Gradient Boosting Machines (LightGBM) algorithm, agricultural product pattern classification was carried out including cereal, maize, sugar beet, sunflower, hash, vineyard, fruit tree and clover crops. Classification results were evaluated using k-fold cross-validation with an overall accuracy of %93.5. A second accuracy assessment was performed with Agricultural Insurance Parcels (TARSİM) that were not included in the classification process as training data, achieving an overall accuracy of %91.1 and Kappa coefficient of 0.89.

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