关键词: 槟榔, 卷积神经网络, Adam优化器, 分级, ROC曲线

Abstract: Aiming at the problem of low manual classification labor productivity and classification accuracy of betel nut, a betel nut classification model based on convolutional neural network was constructed. A betel nut image feature extraction network composed of two convolution blocks was designed, each with two convolutional layers and one pooling layer respectively. The second convolutional block connected a multilayer perceptron composed of flat layer, hidden layer and output layer. The feature extraction network and perceptron formed a shallow convolutional neural network (SCNN). A comparative analysis of network training, validation and testing was completed through six optimization methods such as SGD, Adam and etc., and manual adjustment of the learning rate. Adam was selected as the optimization method for SCNN and the learning rate was set to 0.001. The SCNN based on Adam optimization was applied to the betel nut classification, the classification accuracy reached 98.05%,  and the AUC value of the network ROC curve was equal to 1. The grading of betel nut could be completed quickly and more accurately by using the SCNN model, which provides the basis for the betel nut machine grading research.

Key words: betel nut, convolutional neural network, Adam optimizer, grading, ROC curve