深度特征融合方法及其在叶片病害识别中的应用

农作物叶片病害的自动识别是计算机视觉技术在农业领域的一个重要应用. 近年来, 深度学习在农作物叶片病害识别上取得了一些进展, 但这些方法都是采用基于单一深度卷积神经网络模型的深度特征表示. 而不同的深度卷积神经网络模型对图像的表征能力的互补性这一有用的特性, 还没有得到关注和研究. 本文提出一种用于融合不同深度特征的网络模型MDFF-Net. MDFF-Net将两个预训练的深度卷积神经网络模型进行并联, 再为各个模型分别设置一个具有相同神经元个数的全连接层, 以将不同模型输出的深度特征变换成相同维度的特征, 再通过2个全连接层的非线性变换, 进一步提升特征融合的效果. 我们选取VGG-16和ResNet-50作为MDFF-Net网络的并联骨干网络, 在一个包含5种苹果叶片病害的公开数据集上进行实验. 实验结果显示, MDFF-Net网络的识别精度为96.59%, 取得了比VGG-16和ResNet-50单一网络更好的识别效果, 证明了该深度特征融合方法的有效性.

Deep Feature Fusion Method and Its Application in Leaf Disease Recognition

Automatic recognition of crop leaf diseases is an important application of computer vision technology in agriculture. In recent years, deep learning methods have made some progress in the recognition of crop leaf diseases, and they are all based on deep feature representations of a single deep convolutional neural network (CNN) model. However, the useful fact that the image representation ability of different deep CNN models is complementary has not received attention for research. Thus, this study proposes a network model MDFF-Net for fusing different deep features. MDFF-Net connects two pre-trained deep CNN models in parallel and then sets a fully connected layer with the same number of neurons for each model to transform the deep features output by different models into features with the same dimension. Then, through the non-linear transform of two fully connected layers, the effect of feature fusion is further improved. We choose VGG-16 and ResNet-50 as the feature extractors of MDFF-Net and conduct experiments on a public dataset containing five apple leaf diseases. The experimental results show that the recognition accuracy of MDFF-Net is 96.59%, which is better than the results achieved by VGG-16 or ResNet-50 alone and thus proves the effectiveness of the deep feature fusion method.