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 Influence of sulphur aerosols on physiological, yield and yield attributing of wheat (Triticum aestivum L.) using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 365-373
Author(s):  
Babithraj Gaddameedi ◽  
Bhagawan Bharali ◽  
Soibam Helena Devi
Keyword(s):  
Total Concentration ◽  
Principal Component ◽  
Triticum Aestivum L ◽  
Yield Potential ◽  
Wheat Crop ◽  
Growth Stages ◽  
Yield Traits ◽  
Spike Initiation ◽  
Total Diversity ◽  
Different Growth Stages

Among several constrains curtailing the yield potential of a crop, lack of proper mineral nutrition in particular sulphur management, a nutrient that is needed in trace amount but essential for plant growth is more alarming. Sulphur is the main source of protein particularly for cereal crop. An experiment was conducted to find out influence of sulphur aerosols on morpho-physiological, yield, and yield traits of wheat. S-aerosols viz., (NH4)2SO4, CaSO4, and K2SO4: @ 300 ppm each (?30 kg N ha-1) along with a control were misted on the plants, on sunny days in the afternoon (after 2–3 P.M.) at three different growth stages i.e. seedling, maximum tillering and spike initiation stages. Therefore, a total concentration of each S-aerosols was 900 ppm ? 0.9%.Genotypes (viz., GW-322, GW-366, GW-273, GW-173, JW-336) were raised both under Pot culture (Expt.1) and field  (Expt.2) observations recorded are : LA, LAI, SLW, Tiller numbers, No. of seed per spike, length of spike, spike weight, TW, BY, EY, HI. The investigation was carried out aiming to test the hypothesis that foliar fed Sulphur aerosols influence economic yield of wheat crop positively. The genotype GW-366 was the most responsive in physiological traits and GW-273 for yield traits under the influence of foliar fertilization with S-aerosols. Among the S-aerosols, (NH4)2SO4 was the most effective in the work. The results in this experiment are contribution of Sulphur aerosols using PCA towards total diversity.

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

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
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