Turkoglu, MuammerHanbay, Davut2024-08-042024-08-042019https://doi.org/10.1109/idap.2019.8875911https://hdl.handle.net/11616/102623International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEYRecently, Convolutional Neural Networks (CNN), which is used in the solution of many image processing problems, has been used successfully for many problems in the agricultural field. In this study, for classification of plant species is proposed an approach based on the combination of deep architectures. Deep features were extracted from the plant leaves using the fc6 layer of the previously trained AlexNet and VGG16 models. Then, the reduction of the number of deep features by using the Principal Component Analysis (PCA) method was done quickly and the best distinguishing features were obtained. Finally, the classification performances were calculated using the K-Nearest Neighbor (KNN) method. Flavia and Swedish plant leaf data sets were used to test the proposed system. According to the experimental results, the accuracy scores for Flavia and Swedish data sets was obtained as 99.42% and 99.64%, respectively.trinfo:eu-repo/semantics/closedAccessPlant ClassificationConvolutional Neural NetworksAlexNet modelVGG16 modelKNN classifierConference Object10.1109/idap.2019.8875911WOS:000591781100041N/A