scholarly journals THE CHARACTERISTICS OF SAGO FROND SAP FROM TWO SELECTED GROWTH STAGES; ANGKAT PUNGGUNG AND UPONG MUDA PALMS

2021 ◽  
Vol 40 (3) ◽  
pp. 43-53
Author(s):  
Nurazureen Matnin ◽  
Dayang Salwani Awang Adeni ◽  
Muhammad Norhelmi Ahmad ◽  
Nurashikin Suhaili
Keyword(s):  
Reducing Sugars ◽  
Fermentation Medium ◽  
Growth Stages ◽  
Acidic Ph ◽  
Outer Circle ◽  
Sugar Component ◽  
Sago Palm ◽  
Different Growth Stages ◽  
Fermentation Substrate

Sago frond is produced in abundance upon harvesting of the sago palms for starch extraction, hence need to be utilized and developed into beneficial products. In this study, the sap which contains sugars and starch is obtained by roller crushing the skinned frond for use as fermentation medium. Fronds from different growth stages (namely Angkat punggung and Upong muda) and different positions within the rosette (inner and outer circle) of the sago palm were studied. Based on the results, the outer circle frond of Upong muda palm gave the highest volume of sap at 290mL/kg which equivalent to 1600 mL/frond. On top of that, sago frond sap has an acidic pH, with glucose as major sugar component and contained various kinds of minerals like calcium, potassium and manganese. All fronds from different growth stages contain glucose between 28-68 g/L and xylose 21-29 g/L, respectively. After 21 days of storage, it can be concluded that the amount of reducing sugars and starch in all samples obtained from different growth stages remained almost unchanged from the original. Subsequently after this study, both fresh and stored sago frond sap can be used as a fermentation substrate without any modification.

scholarly journals Physicochemical Properties of Starches Obtained from Various Parts of Sago Palm Trunks at Different Growth Stages.

1999 ◽  
Vol 46 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Tomoko Hamanishi ◽  
Tamao Hatta ◽  
Foh-Shoon Jong ◽  
Setsuko Takahashi ◽  
Keiji Kainuma
Keyword(s):  
Physicochemical Properties ◽  
Growth Stages ◽  
Sago Palm ◽  
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.

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