EVALUATING THE EFFECT OF LIGHT SPECTRUM ON PHYSIOLOGY AND SECONDARY METABOLISM OF OKRA (ABELMOSCHUS ESCULENTUS) AT DIFFERENT GROWTH STAGES BY USE OF NON-INVASIVE FLUORESCENCE AND REFLECTANCE SPECTROSCOPY

2020 ◽  
Vol 19 (2) ◽  
pp. 73-98
Author(s):  
Banah Florent Degni ◽  
Cisse Théodore Haba ◽  
wilfried Gauthier Dib ◽  
Adolphe Yatana Gbogbo ◽  
Jérémie Thouakesseh Zoueu ◽  
...  
Keyword(s):  
Secondary Metabolism ◽  
Reflectance Spectroscopy ◽  
Abelmoschus Esculentus ◽  
Growth Stages ◽  
Light Spectrum ◽  
Non Invasive ◽  
Different Growth Stages ◽  
Effect Of Light

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 A global non-invasive methodology for the phenotyping of potato under water deficit conditions using imaging, physiological and molecular tools

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
M. Musse ◽  
G. Hajjar ◽  
N. Ali ◽  
B. Billiot ◽  
G. Joly ◽  
...  
Keyword(s):  
Water Stress ◽  
Water Deficit ◽  
Soil Conditions ◽  
Growth Stages ◽  
Dry Matter Content ◽  
Tuber Growth ◽  
Non Invasive ◽  
Potato Plants ◽  
Significant Difference

Abstract Background Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping. Results This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes. Conclusions This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.

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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