Correlation Analysis of Rice Seed Setting Rate and Weight of 1000-Grain and Agro-Meteorology over the Middle and Lower Reaches of the Yangtze River, China

2007 ◽  
Vol 6 (4) ◽  
pp. 430-436 ◽  
Author(s):  
Hai-yan ZHAO ◽  
Feng-mei YAO ◽  
Yong ZHANG ◽  
Bin XU ◽  
Jing YUAN ◽  
...  
2013 ◽  
Vol 4 (1) ◽  
Author(s):  
Shuangcheng Li ◽  
Wenbo Li ◽  
Bin Huang ◽  
Xuemei Cao ◽  
Xingyu Zhou ◽  
...  

2019 ◽  
Vol 39 (15) ◽  
Author(s):  
翟天林 ZHAI Tianlin ◽  
王静 WANG Jing ◽  
金志丰 JIN Zhifeng ◽  
祁元 QI Yuan

2020 ◽  
Vol 12 (2) ◽  
pp. 287 ◽  
Author(s):  
Yang Zhong ◽  
Aiwen Lin ◽  
Lijie He ◽  
Zhigao Zhou ◽  
Moxi Yuan

It is important to analyze the expansion of an urban area and the factors that drive its expansion. Therefore, this study is based on Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night lighting data, using the landscape index, spatial expansion strength index, compactness index, urban land fractal index, elasticity coefficient, the standard deviation ellipse, spatial correlation analysis, and partial least squares regression to analyze the spatial and temporal evolution of urban land expansion and its driving factors in the Yangtze River Economic Belt (YREB) over a long period of time. The results show the following: Through the calculation of the eight landscape pattern indicators, we found that during the study period, the number of cities and towns and the area of urban built-up areas in the YREB are generally increasing. Furthermore, the variations in these landscape pattern indicators not only show more frequent exchanges and interactions between the cities and towns of the YREB, but also reflect significant instability and irregularity of the urbanization development in the YREB. The spatial expansion intensity indices of 1992–1999, 1999–2006, and 2006–2013 were 0.03, 0.16, and 0.34, respectively. On the whole, the urban compactness of the YREB decreased with time, and the fractal dimension increased slowly with time. Moreover, the long axis and the short axis of the standard deviation ellipse of the YREB underwent a small change during the inspection period. The spatial distribution generally showed the pattern of “southwest-north”. In terms of gravity shift, during the study period, the center of gravity moved from northeast to southwest. In addition, the Moran's I values for the four years of 1992, 1999, 2006, and 2013 were 0.451, 0.495, 0.506, and 0.424, respectively. Furthermore, by using correlation analysis, we find that the correlation coefficients between these four driving indicators and the urban expansion of the YREB were: 0.963, 0.998, 0.990 and 0.994, respectively. Through the use of partial least squares regression, we found that in 1992-2013, the four drivers of urban land expansion in the YREB were ranked as follows: gross domestic product (GDP), total fixed asset investment, urban population, total retail sales of consumer goods.


2021 ◽  
Vol 12 ◽  
Author(s):  
Yixin Guo ◽  
Shuai Li ◽  
Zhanguo Zhang ◽  
Yang Li ◽  
Zhenbang Hu ◽  
...  

The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.


2004 ◽  
Vol 88 (8) ◽  
pp. 59-64
Author(s):  
Changyu Shao ◽  
Qinger Deng

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