Bolete is well-known and widely consumed mushroom in the world. However, its medicinal properties and nutritional are completely different from one species to another. Therefore, the consumers need a fast and effective detection method to discriminate their species. A new method using directly digital images of two-dimensional correlation spectroscopy (2DCOS) for the species discrimination with deep learning is proposed in this paper. In our study, a total of 2054 fruiting bodies of 21 wild-grown bolete species were collected in 52 regions from 2011 to 2014. Firstly, we intercepted 1750-400 cm^-1 fingerprint regions of each species from their mid-infrared (MIR) spectra, and converted them to 2DCOS spectra with matlab2017b. At the same time, we developed a specific method for the calculation of the 2DCOS spectra. Secondly, we established a deep residual convolutional neural network (Resnet) with 1848 (90%) 2DCOS spectral images. Therein, the discrimination of the bolete species using directly 2DCOS spectral images instead of data matric from the spectra was first to be reported. The results displayed that the respective identification accuracy of these samples was 100% in the training set and 99.76% in the test set. Then, 203 samples were accurately discriminated in 206 (10%) samples of external validation set. Thirdly, we employed t-SNE method to visualize and evaluate the spectral dataset. The result indicated that most samples can be clustered according to different species. Finally, a smartphone applications (APP) was developed based on the established 2DCOS spectral images strategy, which can make the discrimination of bolete mushrooms more easily in practice. In conclusion, deep learning method by using directly 2DCOS spectral image was considered to be an innovative and feasible way for the species discrimination of bolete mushrooms. Moreover, this method may be generalized to other edible mushrooms, food, herb and agricultural products in the further research.

牛肝菌是世界知名的食用蘑菇。但是，它的药用特性和营养在一个物种与另一个物种之间完全不同。因此，消费者需要一种快速有效的检测方法来区分其种类。提出了一种直接使用二维相关光谱数字图像（2DCOS）进行深度学习的物种识别的新方法。在我们的研究中，从2011年到2014年，在52个地区共收集了2054种21种野生牛肝菌子实体。首先，我们截获了1750-400 cm ^-1从每个物种的中红外（MIR）光谱中提取指纹区域，并使用matlab2017b将其转换为2DCOS光谱。同时，我们开发了一种用于计算2DCOS光谱的特定方法。其次，我们用1848年（90％）的2DCOS光谱图像建立了一个深度残差卷积神经网络（Resnet）。其中，首先报道了使用直接2DCOS光谱图像而不是来自光谱的数据矩阵对牛肝菌种的区分。结果显示，这些样本在训练集中的识别准确率分别为100％和测试集中的99.76％。然后，在206个（10％）外部验证集样本中准确区分了203个样本。第三，我们采用t-SNE方法可视化和评估光谱数据集。结果表明，大多数样本可以根据不同的物种进行聚类。最后，智能手机应用（ APP）是基于已建立的2DCOS光谱图像策略开发的，可在实践中使牛肝菌的鉴别更加容易。综上所述，直接利用2DCOS光谱图像进行深度学习被认为是一种新颖，可行的牛肝菌菌种判别方法。而且，在进一步的研究中，该方法可以推广到其他食用菌，食品，药草和农产品。