[1]张善文,谢泽奇,张晴晴.卷积神经网络在黄瓜叶部病害识别中的应用[J].江苏农业学报,2018,(01):56-61.[doi:doi:10.3969/j.issn.1000-4440.2018.01.008]
　ZHANG Shan-wen,XIE Ze-qi,ZHANG Qing-qing.Application research on convolutional neural network for cucumber leaf disease recognition[J].,2018,(01):56-61.[doi:doi:10.3969/j.issn.1000-4440.2018.01.008]
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卷积神经网络在黄瓜叶部病害识别中的应用()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2018年01期
页码:
56-61
栏目:
植物保护
出版日期:
2018-02-25

文章信息/Info

Title:
Application research on convolutional neural network for cucumber leaf disease recognition
作者:
张善文; 谢泽奇; 张晴晴
(郑州大学西亚斯国际学院，河南郑州451150)
Author(s):
ZHANG Shan-wen; XIE Ze-qi; ZHANG Qing-qing
(SIAS International University, Zhengzhou University, Zhengzhou 451150, China)
关键词:
黄瓜; 病害识别; 卷积神经网络; 特征提取; Softmax分类器
Keywords:
cucumber; disease recognition; convolutional neural network (CNN); feature extraction; Softmax classifier
分类号:
S436.421.1
DOI:
doi:10.3969/j.issn.1000-4440.2018.01.008
文献标志码:
A
摘要:
针对传统黄瓜病害识别方法中提取到的分类特征容易受病害叶片形态多样性、光照和背景影响的问题，提出了一种基于卷积神经网络的黄瓜病害识别方法，并建立了一个具有6种黄瓜病害的155 000多幅训练叶片图像数据库。根据病害叶片图像的复杂性，利用卷积神经网络从该数据库中自动学习黄瓜病害叶片图像的属性特征，再利用Softmax分类器进行分类。试验结果表明，与基于特征提取的传统病害识别方法相比，该方法的识别性能较高。
Abstract:
Focused on the problem of the traditional cucumber disease recognition methods that the extracted classifying features were more susceptible to diversity of the diseased leaf image, illumination and background, a cucumber disease recognition system was proposed based on convolutional neural network (CNN), and a cucumber disease leaf image database that contained more than 155 000 training images from six kinds of leaf diseases was established. Duo to the complexity of cucumber leaf images, the method of CNN was used to learn the recognition features adaptively from the database, and cucumber diseases were classified by Softmax classifier. Experimental results showed that the proposed method could achieve better performance in terms of classification than the traditional feature extraction based cucumber disease recognition methods．

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2017-06-10 基金项目：国家自然科学基金项目(61473237)；河南省科技厅基础与前沿技术研究计划项目(172102210512、172102210510)；河南省教育厅高等学校重点科研项目(16A520095、16A510034) 作者简介：张善文(1965-)，男，陕西西安人，博士，教授，博士生导师，研究领域为模式识别及其应用。(E-mail)wjdw716@163.com
更新日期/Last Update: 2018-03-06