Convolutional Neural Network (CNN) has become one of the most popular techniques in image classification. Usually CNN models are trained on a large amount of data, but in this paper, it is discussed CNN usage on data shortage and class imbalance issues. The study is conducted on a small dataset of vine leaves images on a classification task with five classes using two different approaches. In the first approach, a simple CNN model is used, while in the second approach, the Visual Geometry Group

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... (VGG) model with transfer learning is used. It is shown that using different deep learning techniques such as transfer learning, stratified sampling, data augmentation, and the state of arts CNN models such as VGG gives a relatively very good model performance with up to 87% accuracy.