ABSTRACT
It is difficult for humans to recognize recessive diseases in navel oranges. Therefore, deep neural networks are applied to plant disease identification. To improve the feature extraction ability of convolutional neural networks, the Parameter Exponential Nonlinear Activation Unit (PENLU) is proposed to replace the activated function of the neural network. This function not only adds multiple parameters but also brings better generalization ability to the neural network. In addition, the proposed function parameters can be updated by the inverse Stochastic Gradient Descent (SGD) algorithm, which has unparalleled advantages over the existing activated functions. The Residual Network (ResNet), improved by PENLU, is applied to navel orange lesion recognition and achieves the most advanced accuracy compared with traditional lesion recognition methods. It is worth mentioning that the data set of navel orange leaf images proposed in this paper will provide samples for subsequent research. The code and model are available at the website https://github.com/xncaffe/caffe_penlu.
KEYWORDS
neural networks; activation function; plant image classification; lesion detection
