摘要
针对在黄瓜叶部病害识别过程中使用传统卷积神经网络存在模型训练时间长、识别准确率低等问题,提出一种迁移学习和改进残差神经网络相结合的方法对黄瓜叶部病害进行识别。首先对数据集图像进行预处理,将数据集划分为训练集和测试集;然后对传统残差神经网络进行改进;最后使用迁移学习的方式对网络模型进行训练。利用该研究方法对不同的黄瓜叶部病害进行识别试验,结果表明该方法具有较高的识别准确率,可为其他作物的识别方法研究提供参考。
Aiming at problems that traditional convolutional neural network has long training time and low recognition accuracy in the process of cucumber leaf disease identification,a combination of transfer learning and improved residual neural network was proposed to identify cucumber leaf diseases.Firstly,the data set image was subjected to pre-processing,and the data set was divided into a training set and a test set.Secondly,traditional residual neural network was improved.Finally,the network model was trained by means of transfer learning.Identification tests of different cucumber leaf diseases using this research method were done.And it indicated that the method has higher recognition accuracy and could provide reference for research of identification methods of other crops.
作者
李丹
LI Dan(College of Information Engineering,Shaanxi Polytechnic Institute,Xianyang Shaanxi 712000,China)
出处
《农业工程》
2020年第6期36-40,共5页
AGRICULTURAL ENGINEERING
关键词
图像识别
黄瓜叶部病害
残差神经网络
迁移学习
image recognition
cucumber leaf disease
residual neural network
transfer learning
