[1]孙云云,江朝晖,董伟,等.基于卷积神经网络和小样本的茶树病害图像识别[J].江苏农业学报,2019,(01):48-55.[doi:doi:10.3969/j.issn.1000-4440.2019.01.007]
　SUN Yun-yun,JIANG Zhao-hui,DONG Wei,et al.Image recognition of tea plant disease based on convolutional neural network and small samples[J].,2019,(01):48-55.[doi:doi:10.3969/j.issn.1000-4440.2019.01.007]
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基于卷积神经网络和小样本的茶树病害图像识别()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年01期
页码:
48-55
栏目:
植物保护
出版日期:
2019-02-26

文章信息/Info

Title:
Image recognition of tea plant disease based on convolutional neural network and small samples
作者:
孙云云1; 江朝晖1; 董伟2; 张立平2; 饶元1; 李绍稳1
(1.安徽农业大学信息与计算机学院，安徽合肥230036；2.安徽省农业科学院农业经济与信息研究所，安徽合肥230036)
Author(s):
SUN Yun-yun1; JIANG Zhao-hui1; DONG Wei2; ZHNAG Li-ping2; RAO Yuan1; LI Shao-wen1
(1.School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China;2.Institute of Agricultural Economics and Information, Anhui Academy of Agricultural Sciences, Hefei 230036, China)
关键词:
茶叶病害; 图像识别; 卷积神经网络; 小样本
Keywords:
tea plant leaf disease; image recognition; convolutional neural network; small samples
分类号:
S126
DOI:
doi:10.3969/j.issn.1000-4440.2019.01.007
文献标志码:
A
摘要:
以常见且特征相似的茶轮斑病、炭疽病和云纹叶枯病为对象，研究在小样本情况下利用卷积神经网络进行病害图像识别问题。运用7种模式的预处理方法对茶树叶部病害图像样本进行处理，并采用AlexNet经典网络模型进行学习实验，比较、分析其训练及识别效果。结果显示，模式7训练模型精度为933%，平均测试准确率为90%，且对茶轮斑病、炭疽病和云纹叶枯病的正确区分率分别为85%、90%和85%，在预测值和真实值一致性方面优于其他预处理方法。在小样本情况下，该预处理方法可有效区分、识别3种易混病害，且识别精度高，性能好。
Abstract:
Three kinds of common and similar tea diseases including pestalotiopsis theae, tea anthracnose and tea brown blight have been identified by the convolutional neural network(CNN) under the condition of small samples. Seven preprocessing modes were designed and used to process original tea plant leaf disease images automatically. The classic AlexNet network model was used to carry out the learning experiment, and the training and recognition effect was compared and analyzed. The result showed that the accuracy of training model under mode 7 was 93.3%, and the average test accuracy was 90%. And the correct recognition rates of the three diseases (pestalotiopsis theae, tea anthracnose and tea brown blight) were 85%, 90% and 85%, respectively, which were superior to the conventional pretreatment methods in terms of consistency between the predicted value and the true value. In the case of small samples, the proposed pretreatment method can effectively distinguish and identify three kinds of similar diseases, and has high recognition accuracy and good performance.

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备注/Memo

备注/Memo:
收稿日期：2018-06-08 基金项目：农业部农业物联网技术集成与应用重点实验室开放基金项目（2016KL01）；国际先进农业科技计划的引进与创新项目（No.2016-X34）；安徽农业大学2018年度研究生创新基金项目(2018yjs-63) 作者简介：孙云云（1992-），女，安徽界首人，硕士研究生，研究方向为作物信息处理，(E-mail)sunyunyun0910@sina.com 通讯作者：江朝晖，(E-mail)jiangzh@ahau.edu.cn
更新日期/Last Update: 2019-02-27