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.

大豆极易被黄曲霉污染，产生毒素，危害人类生命健康。针对肉眼和传统机器视觉技术难以检测早期发霉现象的问题，提出一种基于深度学习和光学相干（OCT）技术的大豆早期发霉现象分类方法。该方法主要包括三个阶段：第一阶段是霉菌信息提取，我们使用卷积神经网络（CNN）来提取图像特征。传统CNN的输入通常是整幅图像，输出不能反映细粒度的信息。在此基础上，我们利用从补丁中提取的特征来感知细粒度的信息（例如微小的霉菌像素）。在第二阶段，使用自注意力机制融合两个通道的特征。第三阶段，利用包含霉斑区域信息的融合特征向量进行分类。结果表明，该方法在早期霉菌识别方面优于传统CNN模型，平均准确率达到99.5%，比传统CNN模型提高了15分，证明了该方法的有效性。