Using a multi-convolutional neural network to automatically identify small-sample tea leaf diseases

Abstract

Tea leaf disease is a factor leading to the decline of tea quality and yield. In this study, a multi-convolutional neural network (CNN) model, MergeModel, combined with diseased leaf segmentation and a weight initialization method, was used for automatic identification of tea leaf diseases in small samples. Diseased leaf segmentation could reduce the impact of complex backgrounds on the identification results. The integration of multiple CNN modules enabled MergeModel to extract a variety of discriminative features. The identification ability of MergeModel was improved compared with a single neural network. The weight initialization method encoded the diseased leaf features into the convolution filter, which helped the model to concentrate on learning important features at the beginning of training. In addition, this study used a small number of diseased tea leaf images as the original training samples and generated new training samples through the unconditional generation model, namely, SinGAN, for data augmentation. Experimental results showed that MergeModel could effectively distinguish diseased tea leaves from healthy tea leaves and identify common tea leaf diseases such as tea white scab, tea leaf blight, tea red scab, and tea sooty mould. Compared with the existing methods, the proposed method had higher accuracy in identifying tea leaf diseases in small samples.