Cerasus Humilis Cultivars Identification With Small-Sample and Unbalanced Dataset Based on Efficient Net-B0+ranger Networks

Abstract

Because of the high similarity of leaves of different Cerasus humilis varieties, it is difficult to identify them with the naked eye. In this study, the leaves of four different Cerasus humilis varieties collected in the field were used as the research objects, and a new leaf recognition model based on the improved lightweight convolution neural network model EfficientNet-B0 was proposed. Firstly, the performance of the network models Efficientnet-B0 and ResNet50, GoogleNet, ShuffleNet, and MobileNetV3 were compared based on two different learning methods. Then, the influence of different optimizers on model recognition accuracy was compared based on the optimal model. Finally, different learning rates were used to optimize the optimal model. The results show that the recognition rate of the proposed Efficientnet-B0 +Ranger+0.0005 model was up to 86.9%, which was 2.23% higher than that of the original Efficientnet-B0 model. The results show that this method can effectively improve the recognition accuracy of Cerasus humilis auriculate leaves, which can provide a reference for the deployment of the leaf identification model of Cerasus humilis variety on the mobile terminal.