番茄叶片病害种类具有差异较小、肉眼难以辨别的特点. 针对经典卷积神经网络参数多、计算量巨大、模型识别率较低以及预测误差较大等问题, 提出一种改进MobileNetV2网络的病害识别方法. 在适当的网络层加入通道和空间注意力机制增强网络对于病叶片特征的细化能力以及减少无关特征的干扰, 使用Ghost模块替换原模型中部分倒残差块, 保证模型精度的同时减少参数量. 利用LeakyReLU激活函数保留特征图中更多的正负特征信息, 增强模型的鲁棒性. 在公共数据集PlantVillage选取早疫病, 晚疫病, 班枯病, 细菌性溃疡病, 红斑叶螨病, 叶霉病, 细菌性斑点病等10种番茄病叶片作为数据集进行实验. 实验结果表明, 改进MobileNetV2网络分类准确率达到98.57%, 相较于原MobileNetV2, 准确率提高了2.29%, 模型大小减小了22.52%, 优化效果较为显著.
The varieties of tomato leaf diseases are of small differences and are hard to distinguish with the naked eye. Given that the classical convolutional neural network is exposed to various problems, such as a large number of parameters, heavy computation burden, a low identification rate of the model, and a large prediction error, this study proposes a disease identification method based on an improved MobileNetV2 network. A channel and a spatial attention mechanism are added to the right network layer to enhance the ability of the network to specify the features of diseased leaves and reduce the interference of irrelevant features. The Ghost module is used to replace some of the inverted residual blocks in the original model to ensure the accuracy of the model and reduce the number of parameters. The LeakyReLU activation function is employed to retain more positive and negative feature information in the feature map and thereby enhance the robustness of the model. Ten tomato leaf diseases, including early blight, late blight, spot blight, bacterial canker, erythema tetranycariasis, leaf mildew, and bacterial spot, are selected from the public dataset PlantVillage to serve as the experimental dataset. The experimental results show that the classification accuracy of the improved MobileNetV2 network reaches 98.57%, which is 2.29% higher than that of the original MobileNetV2, and the model size is reduced by 22.52%, representing a remarkable optimization effect.