We identified tree species based on leaf images with a convolutional neural network (CNN). We sampled approximately 200 to 300 leaves per tree from five tree species at Kyoto University Campus. Twenty to thirty 1.0 × 1.0 cm (256 × 256 pixel) leaf images were taken per leaf, from which 10,000 leaf images (2,000 × 5 individual tree species) were prepared for the sample data. Color, grayscale, and binary images were used as image types. We constructed 36 learning models using based on differences in learning patterns, image types, and learning iterations. Performance evaluation of the proposed model was conducted using the Matthews correlation coefficient (MCC). Both training and test data had high classification accuracy. The mean MCC of the five tree species ranged from 0.881 to 0.998 for the training data and 0.851 to 0.994 for the test data. Classification accuracy was generally high for color images and low for grayscale images. We found that there were many cases where Cinnamomum camphora was misclassified as Quercus myrsinifolia, or Quercus myrsinifolia was misclassified as Quercus glauca; most Quercus glauca, Ilex integra, and Pittosporum tobira trees were correctly classified using the training data; and misclassification using test data for Ilex integra was very low.