基于改进卷积神经网络的苹果叶部病害识别

Apple leaf disease recognition based on improved convolutional neural network

针对苹果病害叶片图像病斑区域较小导致的传统卷积神经网络不能准确快速识别的问题,提出基于改进卷积神经网络的苹果叶部病害识别的网络模型.首先,将VGG16网络模型从ImageNet数据集上学习到的先验知识迁移到苹果病害叶片数据集上;然后,在瓶颈层后采用选择性核(selective kernel,简称SK)卷积模块;最后,使用全局平均池化代替全连接层.实验结果表明:与其他传统网络模型相比,该模型能更准确快速捕获苹果病害叶片上微小的病斑.

Aiming at the problem of small disease spots in apple leaf images,which couldn’t be accurately and quickly identified by using traditional convolutional neural networks,a network model based on improved convolutional neural network for apple leaf disease identification was proposed.First,the prior knowledge learned from the VGG16 network model was transferred from the ImageNet dataset to the apple disease leaf dataset.Then,after the bottleneck layer the selective kernel(SK)convolution module was adopted.Finally,the fully connected layer was replaced by Global average pooling.The experimental result showed that compared with other traditional network models,this model could capture the tiny spots on the diseased leaves of apples more accurately and quickly.