The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss ofproductivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods basedon machine learning can be used. In recent years, deep learning, which is especially widely used in image processing,offers many new applications related to precision agriculture. In this study, we evaluated the performance resultsusing different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transferlearning and deep feature extraction methods are used, which adapt these deep learning models to the problem athand. The utilized pretrained deep models are considered in the presented work for feature extraction and for furtherfine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM),extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out usingdata consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score areall calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELMclassification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, andVGG19 models produced better accuracy scores when compared to the other layers.