Knowledge Commons of Institute of Automation,CAS
Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network | |
An, Jiangyong1; Li, Wanyi2; Li, Maosong1; Cui, Sanrong1; Yue, Huanran1 | |
发表期刊 | SYMMETRY-BASEL |
ISSN | 2073-8994 |
2019-02-01 | |
卷号 | 11期号:2页码:14 |
通讯作者 | Li, Maosong(limaosong@caas.cn) |
摘要 | Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images. |
关键词 | drought identification drought classification phenotype drought stress maize deep convolutional neural network traditional machine learning |
DOI | 10.3390/sym11020256 |
关键词[WOS] | WATER-STRESS ; PLANT ; TOLERANCE ; IRRIGATION ; TEXTURE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Projects in National Science Technology Pillar Program during the Twelfth Five-year Plan Period[2012BAD20B01] ; National Natural Science Foundation of China[61771471] ; Projects in National Science Technology Pillar Program during the Twelfth Five-year Plan Period[2012BAD20B01] ; National Natural Science Foundation of China[61771471] |
项目资助者 | Projects in National Science Technology Pillar Program during the Twelfth Five-year Plan Period ; National Natural Science Foundation of China |
WOS研究方向 | Science & Technology - Other Topics |
WOS类目 | Multidisciplinary Sciences |
WOS记录号 | WOS:000460767300134 |
出版者 | MDPI |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/24991 |
专题 | 智能机器人系统研究 |
通讯作者 | Li, Maosong |
作者单位 | 1.Chinese Acad Agr Sci, Key Lab Agr Remote Sensing, Minist Agr, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | An, Jiangyong,Li, Wanyi,Li, Maosong,et al. Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network[J]. SYMMETRY-BASEL,2019,11(2):14. |
APA | An, Jiangyong,Li, Wanyi,Li, Maosong,Cui, Sanrong,&Yue, Huanran.(2019).Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network.SYMMETRY-BASEL,11(2),14. |
MLA | An, Jiangyong,et al."Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network".SYMMETRY-BASEL 11.2(2019):14. |
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