摘要
在玉米生长初期,不能及时知道玉米所患病害从而无法及时医治,将导致玉米产量和质量下降。而人工分辨玉米病害耗费大量人力和时间,判断准确率也不高。因此文章提出了基于卷积神经网络的玉米病害识别模型,模型主要有12个网络层,其中包含输入层、4个卷积层、4个池化层、2个全连接层和输出层。通过调整参数和模型优化等操作,最终分类准确率在95%左右。模型具有一定的实际意义,可为玉米病害防治提供理论依据。
In the early growth of corn,we can not know the diseases of corn in time and thus cannot be cured in time,which leads to a decline in the yield and quality of corn.However,it takes a lot of manpower and time to distinguish corn diseases manually,and the accuracy of judgment is not high.Therefore,a corn disease recognition model based on convolutional neural network is proposed in this paper.The model mainly has 12 network layers,including input layer,4 convolutional layers,4 pooling layers,2 fully connected layers and output layer.By adjusting the parameters and the optimization model,the final classification accuracy is about 95%.The model has certain practical significance and can provide a theoretical basis for corn disease control.
作者
吴淑琦
WU Shuqi(Ji Xianlin Honors School of Liaocheng University,Liaocheng 252000,China)
出处
《现代信息科技》
2021年第9期6-9,共4页
Modern Information Technology
关键词
玉米
卷积神经网络
模型
病害
corn
convolutional neural network
model
disease
