CASIA OpenIR
Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network
An, Jiangyong1; Li, Wanyi2; Li, Maosong1; Cui, Sanrong1; Yue, Huanran1
Source PublicationSYMMETRY-BASEL
ISSN2073-8994
2019-02-01
Volume11Issue:2Pages:14
Corresponding AuthorLi, Maosong(limaosong@caas.cn)
AbstractDrought 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.
Keyworddrought identification drought classification phenotype drought stress maize deep convolutional neural network traditional machine learning
DOI10.3390/sym11020256
WOS KeywordWATER-STRESS ; PLANT ; TOLERANCE ; IRRIGATION ; TEXTURE
Indexed BySCI
Language英语
Funding ProjectProjects in National Science Technology Pillar Program during the Twelfth Five-year Plan Period[2012BAD20B01] ; National Natural Science Foundation of China[61771471]
Funding OrganizationProjects in National Science Technology Pillar Program during the Twelfth Five-year Plan Period ; National Natural Science Foundation of China
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000460767300134
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24991
Collection中国科学院自动化研究所
Corresponding AuthorLi, Maosong
Affiliation1.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
Recommended Citation
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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