Abstract: Aiming at the problems of low accuracy and poor real-time performance of plant leaf disease recognition in the field of agricultural information, a plant leaf disease recognition method based on lightweight Convolutional Neural Network (CNN) was proposed. The Depthwise Separable Convolution (DSC) and Global Average Pooling (GAP) methods were introduced in the original network to replace the standard convolution operation and the fully connected layer part at the end of the network respectively. At the same time, the technique of batch normalization was also applied to the process of training network to improve the intermediate layer data distribution and increase the convergence speed. In order to comprehensively and reliably evaluate the performance of the proposed method, experiments were conducted on the open plant leaf disease image dataset PlantVillage, and loss function convergence curve, test accuracy, parameter memory demand and other indicators were selected to verify the effectiveness of the improved strategy. Experimental results show that the improved network has higher disease recognition accuracy (99.427%) and smaller memory space occupation (6.47 MB), showing that it is superior to other leaf recognition technologies based on neural network, and has strong engineering practicability.

Key words: Convolutional Neural Network (CNN), plant leaf disease, image classification, Depthwise Separable Convolution (DSC), Global Average Pooling (GAP)

摘要： 针对目前农业信息领域植物病害识别精度较低、实时性较差的问题，提出了一种基于轻量级卷积神经网络（CNN）的植物叶片病害识别方法。在原有网络中引入深度可分离卷积（DSC）和全局平均池化（GAP）方法，分别用来代替标准卷积运算操作并对网络末端的全连接层部分进行替换。同时，批归一化的技巧也被运用到训练网络的过程中，以改善中间层数据分布并提高收敛速度。为全面而可靠地评估所提方法的性能，在公开的植物叶片病害图像数据集PlantVillage上进行实验，选取损失函数收敛曲线、测试精度、参数内存需求等指标来验证改进策略的有效性。实验结果表明，改进后的网络具有较高的病害识别精度（99.427%）以及较小的内存空间占用（6.47 MB），可见其与其他基于神经网络的叶片识别技术相比具有优势，工程实用性较强。

关键词: 卷积神经网络, 植物叶片病害, 图像分类, 深度可分离卷积, 全局平均池化