Morphological and physiological characteristics of abnormal berry development in Vitis amurensis

2021 ◽  
Author(s):  
peilei xu ◽  
Xianyan Han ◽  
Jun Ai ◽  
Yiming Yang ◽  
Hongyan Qin ◽  
...  
Keyword(s):  
Acid Invertase ◽  
Vitis Amurensis ◽  
Growth Stages ◽  
Activity Levels ◽  
Berry Development ◽  
Wine Quality ◽  
Grape Berries ◽  
Soluble Acid Invertase ◽  
Berry Size ◽  
Different Growth Stages

Heterogeneity among grape berries directly affects wine quality and restricts the wine-grape industry’s development. To study the heterogeneous development of Vitis amurensis berries, the morphology and physiology of three different types—large berry, medium berry, and live green ovary (LGO)—in the same clusters of wine-making cultivar ‘Shuangfeng’ were monitored at different growth stages from June to September. External differences in berry development were distinguishable at 12 days after full bloom (DAF). The pedicel, berry size, fresh weight, and seed length of the medium berries were intermediate between those of large berries and LGOs. Seeds are crucial for fruit set and normal berry development. The activity levels of soluble acid invertase and cell-wall-bound acid invertase in large berries increased earlier, at 18 DAF, than the accumulation of sugar. Abscisic acid concentrations in medium berries and LGOs were greater than that in large berries at 18 DAF. The greater endogenous indole-3-acetic acid concentration in the medium berries compared with LGOs might protect the former from abscission.

Effects of two training systems on sugar metabolism and related enzymes in cv. Beibinghong (Vitis amurensis Rupr.)

2015 ◽  
Vol 95 (5) ◽  
pp. 987-998 ◽  
Author(s):  
Liyuan Liu ◽  
Lijun Nan ◽  
Xianhua Zhao ◽  
Zhenxing Wang ◽  
Hailong Nan ◽  
...  
Keyword(s):  
Sucrose Synthase ◽  
Sugar Metabolism ◽  
Sucrose Phosphate Synthase ◽  
Training System ◽  
Vitis Amurensis ◽  
Sucrose Content ◽  
Grape Berries ◽  
Training Systems ◽  
Soluble Acid Invertase ◽  
Fruit Setting

Liu, L., Nan, L., Zhao, X., Wang, Z., Nan, H. and Li, H. 2015. Effects of two training systems on sugar metabolism and related enzymes in cv. Beibinghong (Vitis amurensis Rupr.). Can. J. Plant Sci. 95: 987–998. Eight individual sugars and four sugar-metabolism-related enzymes were investigated in the development of Vitis amurensis Rupr. ‘Beibinghong’ grape berries. Two different training systems, vertical shoot positioning (VSP) and Y-shaped training system (Y-shape) were applied. Sucrose contents in the two training systems were significantly related to the sucrose phosphate synthase (SPS) and sucrosynthetic activity of sucrose synthase (SS-s) in berries before veraison. The results show that throughout the veraison period, individual sugars, such as glucose, fructose, galactose and lactose, increased. Glucose and fructose were affected by both training systems, mainly at 15 and 16 wk (weeks after fruit setting). Training systems had no significant effects on the rhamnose, arabinose, galactose and maltose contents, and barely had an effect on the lactose content. The VSP training system mainly affected the sucrose content during the harvest period, while the Y-shape affected sucrose content mainly after 9 wk. During 2011 to 2013, VSP and Y-shape strongly affected the sucrose contents before veraison, and also affected the cleavage activity of sucrose synthase (SS-c) mainly between 5 and 8 wk; however, different training systems barely affected the soluble acid invertase (SAI) activities in whole berry growth. From the perspective of the whole berry development, the results showed that different systems had no significant effects on individual sugars and enzymes.

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.

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