Abstract: In view of the shortcomings such as low efficiency and easy miss detection when manually selecting moldy tobacco leaves online, this paper proposes a method for screening, classifying and recognizing moldy tobacco leaf images based on convolutional neural network model. Firstly, the tobacco leaf data set is built up. Secondly, a convolutional neural network model is built to initially extract features, screen and extract the main features, then summarize the features of each part, and finally realize the image classification, thereby achieving fast and accurate identification of moldy tobacco leaf images and normal tobacco leaf images. The experimental results show that compared with the artificial moldy tobacco leaves selection method and the traditional image classification algorithm of tobacco leaves, the convolution neural network not only has a high recognition accuracy, but also simplifies the complex process of artificial extraction of image features.

Key words: moldy tobacco leaves, convolutional neural network, image classification