基于卷积神经网络的葡萄病毒病的图像识别  

Image Identification of Grapevine Virus Disease Based on Convolution Neural Network

作　　者：吴雪琦 WU Xueqi(Ji Xianlin Honors School of Liaocheng University,Liaocheng 252000,China)

机构地区：[1]聊城大学季羡林学院,山东聊城252000

出　　处：《现代信息科技》2021年第10期30-33,共4页Modern Information Technology

摘　　要：葡萄在世界历史上源远流长,科技的发展与人口的流动让葡萄的种植范围不断扩大,位居世界首位,这其中也带来了许多问题,例如葡萄病毒病。提前发现病害可以扭转葡萄产量下降趋势。针对人们肉眼判断准确率低的问题,文章提出了基于CNN的葡萄病毒病的图像识别模型,网络包含一个输入层、四个卷积层、四个池化层、两个全连接层和一个输出层,对于文章选取的数据,该模型的精确度达到了97.25%,损失率达到9.77%。Grapes have a long history in the world.With the development of science and technology and the movement of population,the planting range of grape has been continuously expanded,ranking first in the world.However,this also brings many problems,such as grapevine virus disease.Early detection of diseases can reverse the downward trend of grape yield.Aiming at the problem of low judgment accuracy of human eyes,this paper proposes the image identification model of grapevine virus disease based on CNN,including an input layer,four convolution layers,four pooling layers,two fully connected layers and one output layer.For the data selected in this paper,the accuracy of this model reaches 97.25%,and the loss rate is 9.77%.

关 键 词：葡萄病毒病 卷积神经网络 模型 图像识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]