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Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 4, 页码: 2644-2658
Authors:  Jin, Shichao;  Su, Yanjun;  Gao, Shang;  Wu, Fangfang;  Ma, Qin;  Xu, Kexin;  Hu, Tianyu;  Liu, Jin;  Pang, Shuxin;  Guan, Hongcan;  Zhang, Jing;  Guo, Qinghua
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Classification  deep learning  LiDAR  phenotype  segmentation  structural components  
Application of deep learning in ecological resource research: Theories, methods, and challenges 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2020, 卷号: 63, 期号: 10, 页码: 1457-1474
Authors:  Guo, Qinghua;  Jin, Shichao;  Li, Min;  Yang, Qiuli;  Xu, Kexin;  Ju, Yuanzhen;  Zhang, Jing;  Xuan, Jing;  Liu, Jin;  Su, Yanjun;  Xu, Qiang;  Liu, Yu
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Ecological resources  Deep learning  Neural network  Big data  Theory and tools  Application and challenge  
Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level 期刊论文
PLANT METHODS, 2020, 卷号: 16, 期号: 1
Authors:  Jin, Shichao;  Su, Yanjun;  Song, Shilin;  Xu, Kexin;  Hu, Tianyu;  Yang, Qiuli;  Wu, Fangfang;  Xu, Guangcai;  Ma, Qin;  Guan, Hongcan;  Pang, Shuxin;  Li, Yumei;  Guo, Qinghua
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Biomass  Phenotype  Machine learning  Terrestrial lidar  Precision agriculture  
Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data 期刊论文
ECOLOGICAL INDICATORS, 2020, 卷号: 108
Authors:  Xu, Kexin;  Su, Yanjun;  Liu, Jin;  Hu, Tianyu;  Jin, Shichao;  Ma, Qin;  Zhai, Qiuping;  Wang, Rui;  Zhang, Jing;  Li, Yumei;  Liu, Hon An;  Guo, Qinghua
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Aboveground biomass (AGB)  Degraded grassland  Machine learning  Northern agro-pastoral ecotone  Terrestrial laser scanning (TLS)