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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.