Authors

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.

Abstract in Chinese

针对不同欧李品种叶片相似度高，用肉眼难以鉴别的问题。本研究以田间收集的4种不同欧李品种叶片为研究对象，提出一种基于改进轻量级卷积神经网络模型EfficientNet-B0的欧李品种叶片识别模型。首先，基于2种不同学习方式对比EfficientNet-B0与ResNet50, GoogleNet, ShuffleNet, MobileNetV3等网络模型的性能；然后，基于最优模型对比不同优化器对模型识别准确率的影响；最后采用不同学习率（0.0001, 0.0005, 0.001, 0.005, 0.05)对优选出的模型进行优化。结果表明该研究提出的EfficientNet-B0+Ranger+0.0005模型识别率达到86.9%，相比于改进前的EfficientNet-B0模型，其识别率提高了2.23%。研究结果表明，该方法可有效提高欧李品种叶片的识别准确率，可为移动端部署欧李品种叶片识别模型提供参考。