A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

文献类型： 外文期刊

第一作者： Ma, Juncheng

作者： Ma, Juncheng;Du, Keming;Zheng, Feixiang;Sun, Zhongfu;Zhang, Lingxian;Gong, Zhihong

作者机构：

关键词： Cucumber; Diseases; Deep convolutional neural network; Symptom images; Recognition

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2018 年 154 卷

页码：

收录情况： SCI

摘要： Manual approaches to recognize cucumber diseases are often time-consuming, laborious and subjective. A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildew, and target leaf spots. The symptom images were segmented from cucumber leaf images captured under field conditions. In order to decrease the chance of overfitting, data augmentation methods were utilized to enlarge the datasets formed by the segmented symptom images. With the augmented datasets containing 14,208 symptom images, the DCNN achieved good recognition results, with an accuracy of 93.4%. In order to compare the results of the DCNN, comparative experiments were conducted using conventional classifiers (Random Forest and Support Vector Machines), as well as AlexNet. Results showed that the DCNN was a robust tool for recognizing the cucumber diseases in field conditions.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序