Narenciye Ağaç Yaprak Hastalıklarının Evrişimli Sinir Ağları ile Sınıflandırılması

Disease factor in citrus trees greatly affects productivity and quality of the products. Consequently, diseases in the citrus trees harm the farmers financially. Early detection of diseases allows taking necessary precautions and increasing the productivity. As a result, the financial losses of the farmers will be minimized. In this study, a convolutional neural network model, which provides classification of leaf images, has been proposed in order to detect citrus diseases on leaf images. In addition, the classification performances of VGG16 and AlexNet architectures in terms of transfer learning idea for citrus leaf diseases were measured and compared with the custom model. The performances of the models were obtained on a leaf dataset containing images of four different disease and healthy citrus leaf classes. The created ESA model and transfer learning architectures as VGG16 and AlexNet classified the dataset with the accuracy rates of 82.64%, 93.39% and 92.56%, respectively.