鉴于国内野生菌中毒事件频发，拟采用深度学习技术来解决这一社会问题。但由于野生菌图像的类间差异较小，图像背景复杂，细粒度识别精度较低。针对这一问题，提出了一种改进的ResNeXt50网络。首先设计了一种多尺度特征引导（MSFG）模块，通过短连接引导网络充分学习和利用低级特征与高级特征；接着采用改进的注意力机制模块来减少网络对复杂背景的学习；最后对模型中的不同层级特征进行融合，利用得到的联合特征进行识别。实验结果表明，所提改进模型在测试集上的准确率可以达到96.47%，较未改进的ResNeXt50网络，在准确率上提升了2.64个百分点。对比结果显示，相较VGG19、DenseNet121、Inception_v3、ResNet50、ShuffleNet_v2这5个网络模型，所提改进模型在准确率上分别提升了8.10个百分点、5.13个百分点、3.24个百分点、3.30个百分点、4.25个百分点。

Deep learning technology is proposed to solve the social problem of the frequent occurrences of wild mushroom poisoning in China. However, due to the small difference between classes and complex image backgrounds, fine-grained recognition accuracy is low. To solve this problem, this paper proposes an improved ResNeXt50 network. First, a multiscale feature guide (MSFG) module is designed, which guides the network to learn and use low and high-level features fully through short connections. Then, the improved attention mechanism module is used to reduce the network’s learning for complex backgrounds. Finally, the different hierarchical features in the model are fused, and the obtained joint features are used for recognition. Experimental results show that the accuracy of the proposed network on the test set can reach 96.47%, which is 2.64 percentage points higher than the unimproved ResNeXt50 network. Comparison results show that the accuracy of the improved network model is 8.10 percentage points, 5.13 percentage points, 3.24 percentage points, 3.30 percentage points, and 4.25 percentage points better than VGG19, DenseNet121, Inception_v3, ResNet50, and ShuffleNet_v2, respectively.