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• Wild grapes have strong stress resistance, which is of great significance for broadening the gene pool of grape resources. • There are many wild grape resources in China, which are widely distributed and need real-time and rapid identification methods. • The application of deep learning technology in variety identification has the advantages of non-destructive, efficient and labor-saving. There are more than 70 species of Vitis in the world, and 40 species, 1 subspecies and 13 varieties of Vitis native from China. Wild grapes are diverse and complex, and it is difficult to identify. As an important research direction, deep learning can quickly extract the deep features of images under the premise of ensuring high accuracy, and is widely used in the field of image classification and recognition. In this study, the images of mature leaves of 23 species of wild grapes under natural conditions were selected as the research objects, and the images of leaves were collected by smart phones to construct a wild grape leaf data set with a number of 10,077 images. Four deep learning networks (GoogleNet, ResNet-50, ResNet-101, VGG-16) were used to identify the leaf images of wild grapes. The classification network with the highest average accuracy is ResNet-101, average accuracy and recall rate are 98.64 % and 95.98 %, respectively. Among the 23 categories of the detection model, the prediction accuracy of 12 categories reached 100 %, and the recognition recall rate of 13 categories reached 100 %. It proves the feasibility of deep learning network model to identify leaf species in natural environment, and realizes automatic real-time identification of wild grapes, so as to provide reference for the protection, utilization, classification research of wild grapes and variety identification of other crops. [ABSTRACT FROM AUTHOR]

This research aimed to examine the effects of paternalistic leadership on the safety participation of high-speed railway drivers. Survey data were collected from 601 drivers in major Chinese rail companies. Structural equation modeling was conducted to analyze the influence of paternalistic leadership on safety participation via leader–member exchange (LMX). The results indicated that moral leadership directly promotes safety participation. Besides, benevolent leadership was positively associated with safety participation. Also, LMX partially mediates the positive relationship between benevolent leadership, moral leadership, and safety participation. Therefore, paternalistic leadership promotes the safety participation of high-speed railway drivers. [ABSTRACT FROM AUTHOR]