scholarly journals Responses of Nitrogen Metabolism, Uptake and Translocation of Maize to Waterlogging at Different Growth Stages

2017 ◽  
Vol 8 ◽  
Author(s):  
Baizhao Ren ◽  
Shuting Dong ◽  
Bin Zhao ◽  
Peng Liu ◽  
Jiwang Zhang
Keyword(s):  
Nitrogen Metabolism ◽  
Growth Stages ◽  
Different Growth Stages

Cytokinins from terminal buds of Euphoria longana during different growth stages

1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu
Keyword(s):  
Growth Stages ◽  
Terminal Buds ◽  
Different Growth Stages

Effects of Meteorological Factors at Different Growth Stages on Yield Traits of Maize (Zea maysL.) in Heilonggang Basin

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Growth Stages ◽  
Yield Traits ◽  
Different Growth Stages

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

Mesophyll conductance variability of rice aquaporin knockout lines at different growth‐stages and growing environments

2021 ◽  
Author(s):  
Xianhong Huang ◽  
Zhixin Wang ◽  
Jianliang Huang ◽  
Shaobing Peng ◽  
Dongliang Xiong
Keyword(s):  
Growth Stages ◽  
Mesophyll Conductance ◽  
Different Growth Stages
Sign in / Sign up

Export Citation Format

Share Document