Bisphenol A effects on the chlorophyll contents in soybean at different growth stages

2017 ◽  
Vol 223 ◽  
pp. 426-434 ◽  
Author(s):  
Liya Jiao ◽  
Hezhou Ding ◽  
Lihong Wang ◽  
Qing Zhou ◽  
Xiaohua Huang
Keyword(s):  
Bisphenol A ◽  
Growth Stages ◽  
Chlorophyll Contents ◽  
Different Growth Stages

scholarly journals Effects of Silicon on Photosynthetic Characteristics of Maize (Zea maysL.) on Alluvial Soil

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Zhiming Xie ◽  
Fengbin Song ◽  
Hongwen Xu ◽  
Hongbo Shao ◽  
Ri Song
Keyword(s):  
Photosynthetic Efficiency ◽  
Alluvial Soil ◽  
Optimal Dose ◽  
Photosynthetic Characteristics ◽  
Growth Stages ◽  
Total Chlorophyll ◽  
Chlorophyll Contents ◽  
Maize Leaves ◽  
Different Growth Stages ◽  
Photosynthetic Ability

The objectives of the study were to determine the effects of silicon on photosynthetic characteristics of maize on alluvial soil, including total chlorophyll contents, photosynthetic ratePn, stomatal conductancegs, transpiration rate (E), and intercellular CO2concentrationCiusing the method of field experiment, in which there were five levels (0, 45, 90, 150, and 225 kg·ha−1) of silicon supplying. The results showed that certain doses of silicon fertilizers can be used successfully in increasing the values of total chlorophyll contents,Pn, andgsand decreasing the values ofEandCiof maize leaves, which meant that photosynthetic efficiency of maize was significantly increased in different growth stages by proper doses of Si application on alluvial soil, and the optimal dose of Si application was 150 kg·ha−1. Our results indicated that silicon in proper amounts can be beneficial in increasing the photosynthetic ability of maize, which would be helpful for the grain yield and growth of maize.

scholarly journals Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5055 ◽  
Author(s):  
Yahui Guo ◽  
Hanxi Wang ◽  
Zhaofei Wu ◽  
Shuxin Wang ◽  
Hongyong Sun ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Grain Weight ◽  
Support Vector ◽  
Growth Stages ◽  
Yield Prediction ◽  
Chlorophyll Contents ◽  
Learning Machine ◽  
Long Term Observation ◽  
Mean Square Errors ◽  
Different Growth Stages

The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

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
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