Detection of soybean mildew infection at early stage based on optical coherence tomography and deep learning methods

Abstract

Soybean can be easily contaminated by Aspergillus flavus which can generate toxigenic and endanger human life and health. Due to the difficulty in detecting moldy phenomena at early stage by the naked eye and traditional machine vision technique, this paper proposes a classification method based on deep learning and optical coherence (OCT) techniques to detect moldy phenomenon of soybeans at early stage. The proposed method mainly includes three stages: the first stage is mildew information extraction, we use convolutional neural network (CNN) to extract image features. The input of traditional CNN is usually the whole image, and the output can not to reflect the fine-grained information. On this basis, we use the features extracted from the patch for the perception of fine-grained information (such as tiny mildew pixels). In the second stage, the features of the two channels are fused using the self-attention mechanism. In the third stage, the fused feature vectors containing the region information of moldy spots are used for classification. The results show that the proposed method is superior to the traditional CNN model in early mildew identification, with an average accuracy of 99.5% and have 15 points increasing to traditional CNN model, which proves the effectiveness of the method.