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Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models

Yunus Camgözlü*, Yakup Kutlu

DOI: 10.28978/nesciences.1405175


It is important to identify a high-performance model that can classify all leaves and even differentiate according to regional variations of the same leaf type. In this study, a leaf classification model was created using 5 different datasets with different number of images and compared with models. For this purpose, 4 different pre-trained models called VGG16, InceptionV3, MobileNet and DenseNet are used. In addition, a new model was proposed and model training was carried out using these datasets . Using the all models, inputs are transformed into feature vectors by parameter transfer method and used for classification with the nearest neighbor algorithm and support vector machine. The performance of the classifications were compared with similar studies in the literature.


Artificial intelligence, machine learning, cuda, deep learning, parameter transfer, pre-trained model, feature transformation

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