Abstract: Against apple fruit varieties identification and classification problem, a original data set containing more than one apple fruit varieties of leaf image is provided, a new convolution model of neural network classification is built to verify the classification accuracy, generalization performance and stability, which provides theoretical basis and technical support for apple cultivars’ simple, rapid, accurate and reliable identification and classification. Taking the apple fine seedling breeding base of Jingning Fruit Research Institute of Pingliang City of Gansu Province as the experimental base, 14 apple tree varieties are selected. For each variety, about 10 fruit trees with different tree ages, tree size and growth status were selected, and about 100 mature leaves without mechanical damage are picked, and then leaf images are taken to form a data set. Then the convolutional neural network is used to train the recognition and classification model. Aiming at the recognition and classification of apple cultivars, this paper provides an original data set containing 14394 leaf images of 14 apple and fruit cultivars, and designs and implements a recognition and classification model based on convolutional neural network. The experimental results show that the model has high accuracy. The training accuracy of the training set can reach 99.88%, the verification accuracy of the verification set is 94.36%, and the test accuracy of the independent test set is 90.49%. The results of this study can help the modern apple field planting, scientific research experiments and other practical scenarios, and provide a reference for the practical application of deep convolutional neural network technology in plant variety identification and classification, and enrich the application of deep learning in agriculture.

Key words: convolutional neural network, apple tree species, blade image, identification and classification