基于RSTCNN的小麦叶片病害严重度估计  

Severity Estimation of Wheat Leaf Diseases Based on RSTCNN

作　　者：鲍文霞[1] 林泽 胡根生[1] 梁栋[1] 黄林生[1] 杨先军[2] BAO Wenxia;LIN Ze;HU Gensheng;LIANG Dong;HUANG Linsheng;YANG Xianjun(National Engineering Research Center for Agro-ecological Big Data Analysis and Application,Anhui University,Hefei 230601,China;Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China)

机构地区：[1]安徽大学农业生态大数据分析与应用技术国家地方联合工程研究中心,合肥230601 [2]中国科学院合肥物质科学研究院,合肥230031

出　　处：《农业机械学报》2021年第12期242-252,263,共12页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(61672032、41771463);安徽省科技重大专项(16030701091)。

摘　　要：以小麦叶片条锈病和白粉病为研究对象,针对同类型病害的不同严重度之间的图像颜色及纹理特征差异较小,传统方法病害严重度估计准确率不高的问题,提出一种基于循环空间变换的卷积神经网络(Recurrent spatial transformer convolutional neural network,RSTCNN)对小麦叶片病害进行严重度估计。RSTCNN包含3个尺度网络,并由区域检测子网络进行连接。每个尺度网络以VGG19作为基础网络以提取病害的特征,同时为了统一区域检测过程中前后特征图的维度,在全连接层前引入空间金字塔池化(Spatial pyramid pooling,SPP);区域检测子网络则采用空间变换(Spatial transformer,ST)有效提取尺度网络特征图中病害的注意力区域。小麦叶片病害图像通过每个尺度网络中卷积池化层得到的特征图,一方面可作为预测病害严重度类别概率的依据,另一方面通过ST进行注意力区域检测并将检测到的区域作为下一个尺度网络的输入,通过交替促进的方式对注意力区域检测和局部细粒度特征表达进行联合优化和递归学习,最后对不同尺度网络的输出特征进行融合再并入到全连接层和Softmax层进行分类,从而实现小麦叶片病害严重度的估计。本文对采集的患有条锈病和白粉病的小麦叶片图像结合数据增强方法构建病害数据集,实验验证了改进后的RSTCNN在3层尺度融合的网络对病害严重度估计准确率较佳,达到了95.8%。相较于基础分类网络模型,RSTCNN准确率提升了7~9个百分点,相较于传统的基于颜色和纹理特征的机器学习算法,RSTCNN准确率提升了9~20个百分点。结果表明,本文方法显著提高了小麦叶片病害严重度估计的准确率。Accurately estimating the severity of wheat leaf diseases can reduce planting costs and agricultural ecological environment pollution by the targeted application of pesticides,and contribute to the precise prevention and control of diseases in wheat field,while reducing the cost of the pesticide and the pollution of agricultural ecological environment.The stripe rust and powdery of wheat leaves were taken as the research object.For the problems of small differences in color and texture characteristics of images with different severities of the same diseases,and low classification accuracy of traditional methods,an improved convolutional neural network was proposed based on recurrent spatial transform(Recurrent spatial transformer convolutional neural network,RSTCNN),which was conductive for severity assessment of wheat leaf diseases.RSTCNN consisted of three scale networks which were connected by regional detection sub-networks.In each scale network,VGG19 was used as the basic classification sub-network to extract the disease features.Furthermore,in order to fix the dimensions of the front and behind feature maps in the region detection process,the spatial pyramid pooling(SPP)was introduced before the fully connected layer.Spatial transform(ST)was used by region detection sub-network to effectively extract the attention region of the upper scale network feature map.The feature map of the disease image obtained by the convolutional pooling layer can be used as a key for predicting the category probability of the severities at multiple scales.Besides,ST were performed for the detection of region attention to serve as the input of the next scale network.The joint optimization and recursive learning of attention region detection and local fine-grained feature representation were carried out by means of alternating promotion.Finally,the output features of the different scale networks were merged,and then incorporated into the fully connected layer and the Softmax layer for classification,so as to realize the estimatio

关 键 词：小麦 叶片病害 严重度估计 循环空间变换卷积神经网络 

分 类 号：S435.121[农业科学—农业昆虫与害虫防治] TP391.41[农业科学—植物保护]