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Image recognition and empirical application of desert plant species based on convolutional neural network

LI Jicai SUN Shiding JIANG Haoran TIAN Yingjie XU Xiaoliang

干旱区科学：英文版2022，Vol.14Issue(12)：P.1440-1455,16.

干旱区科学：英文版2022，Vol.14Issue(12)：P.1440-1455,16.DOI:10.1007/s40333-022-0086-9

Image recognition and empirical application of desert plant species based on convolutional neural network

LI Jicai ¹SUN Shiding ²JIANG Haoran ²TIAN Yingjie ³XU Xiaoliang⁴

作者信息

1. School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China
2. School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China
3. School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China Key Laboratory of Big Data Mining and Knowledge Management,Chinese Academy of Sciences,Beijing 100190,China Research Center on Fictitious Economy and Data Science,Chinese Academy of Sciences,Beijing 100190,China
4. Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China
折叠

摘要

关键词

desert plants/image recognition/deep learning/convolutional neural network/Residual Network X_8GF(RegNetX_8GF)/Mobile Network V2(MobileNetV2)/nature reserves

分类

信息技术与安全科学

引用本文复制引用

LI Jicai,SUN Shiding,JIANG Haoran,TIAN Yingjie,XU Xiaoliang..Image recognition and empirical application of desert plant species based on convolutional neural network[J].干旱区科学：英文版,2022,14(12):P.1440-1455,16.

基金项目

supported by the West Light Foundation of the Chinese Academy of Sciences(2019-XBQNXZ-A-007) （2019-XBQNXZ-A-007）

the National Natural Science Foundation of China(12071458,71731009). （12071458,71731009）

干旱区科学：英文版

OACSCDCSTPCDSCI

ISSN：1674-6767

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