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Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network

文献类型：期刊论文

作者An, Jiangyong1; Li, Wanyi2; Li, Maosong1; Cui, Sanrong1; Yue, Huanran1
刊名SYMMETRY-BASEL
出版日期2019-02-01
卷号11期号:2页码:14
关键词drought identification drought classification phenotype drought stress maize deep convolutional neural network traditional machine learning
ISSN号2073-8994
DOI10.3390/sym11020256
通讯作者Li, Maosong(limaosong@caas.cn)
英文摘要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.
WOS关键词WATER-STRESS ; PLANT ; TOLERANCE ; IRRIGATION ; TEXTURE
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]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000460767300134
出版者MDPI
资助机构Projects in National Science Technology Pillar Program during the Twelfth Five-year Plan Period ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/24990]  
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.

入库方式： OAI收割

来源：自动化研究所

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