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A novel light interception trait of a hybrid rice ideotype indicative of leaf to panicle ratio
期刊论文
FIELD CROPS RESEARCH, 2021, 卷号: 274
Authors:
Xiao, Feng
;
Li, Weiwei
;
Xiao, Minghui
;
Yang, Zongfeng
;
Cheng, Wangda
;
Gao, Shang
;
Li, Ganghua
;
Ding, Yanfeng
;
Paul, Matthew J.
;
Liu, Zhenghui
Adobe PDF(35045Kb)
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View/Download:3/0
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Submit date:2023/02/24
Ideotype
Leaf to panicle ratio
Light interception
Rice breeding
Dynamic canopy light interception simulating
device
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
Adobe PDF(14928Kb)
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Submit date:2022/03/01
Classification
deep learning
LiDAR
phenotype
segmentation
structural components
Systematic Identification of the Light-quality Responding Anthocyanin Synthesis-related Transcripts in Petunia Petals
期刊论文
HORTICULTURAL PLANT JOURNAL, 2020, 卷号: 6, 期号: 6, 页码: 428-438
Authors:
Fu, Zhenzhu
;
Shang, Hongquan
;
Jiang, Hui
;
Gao, Jie
;
Dong, Xiaoyu
;
Wang, Huijuan
;
Li, Yanmin
;
Wang, Limin
;
Zhang, Jing
;
Shu, Qingyan
;
Chao, Yacong
;
Xu, Menglan
;
Wang, Rui
;
Wang, Liangsheng
;
Zhang, Hechen
Adobe PDF(3671Kb)
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Submit date:2022/03/01
Petunia
Anthocyanin
Transcription factor
Light quality
Transcriptome analysis
Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning
期刊论文
PLANT METHODS, 2020, 卷号: 16, 期号: 1
Authors:
Yang, Zongfeng
;
Gao, Shang
;
Xiao, Feng
;
Li, Ganghua
;
Ding, Yangfeng
;
Guo, Qinghua
;
Paul, Matthew J.
;
Liu, Zhenghui
Adobe PDF(4429Kb)
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View/Download:24/0
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Submit date:2022/03/01
Plant phenotyping
Leaf and panicle detection
Deep learning
Physiological trait
Leaf to panicle ratio (LPR)
Japonica rice
Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data
期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 卷号: 57, 期号: 3, 页码: 1336-1346
Authors:
Jin, Shichao
;
Su, Yanjun
;
Wu, Fangfang
;
Pang, Shuxin
;
Gao, Shang
;
Hu, Tianyu
;
Liu, Jin
;
Guo, Qinghua
Adobe PDF(7737Kb)
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View/Download:39/0
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Submit date:2022/01/06
Light detection and ranging (LiDAR)
phenotypic traits
regional growth
segmentation
skeleton
Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms
期刊论文
FRONTIERS IN PLANT SCIENCE, 2018, 卷号: 9
Authors:
Jin, Shichao
;
Su, Yanjun
;
Gao, Shang
;
Wu, Fangfang
;
Hu, Tianyu
;
Liu, Jin
;
Li, Wankai
;
Wang, Dingchang
;
Chen, Shaojiang
;
Jiang, Yuanxi
;
Pang, Shuxin
;
Guo, Qinghua
Adobe PDF(2278Kb)
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Submit date:2022/02/25
deep learning
detection
classification
segmentation
phenotype
Lidar (light detection and ranging)
A global corrected SRTM DEM product for vegetated areas
期刊论文
REMOTE SENSING LETTERS, 2018, 卷号: 9, 期号: 4, 页码: 393-402
Authors:
Zhao, Xiaoqian
;
Su, Yanjun
;
Hu, Tianyu
;
Chen, Linhai
;
Gao, Shang
;
Wang, Rui
;
Jin, Shichao
;
Guo, Qinghua
Adobe PDF(2084Kb)
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View/Download:30/0
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Submit date:2022/02/25
The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data
期刊论文
REMOTE SENSING, 2018, 卷号: 10, 期号: 8
Authors:
Jin, Shichao
;
Su, Yanjun
;
Gao, Shang
;
Hu, Tianyu
;
Liu, Jin
;
Guo, Qinghua
Adobe PDF(4477Kb)
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View/Download:22/0
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Submit date:2022/02/25
canopy height
Random Forest
LiDAR
multi-source
vegetation type
location
scale