There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal compo...There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.展开更多
Paddy field management is complicated and labor intensive.Correct row detection is important to automatically track rice rows.In this study,a novel method was proposed for accurate rice row recognition in paddy field ...Paddy field management is complicated and labor intensive.Correct row detection is important to automatically track rice rows.In this study,a novel method was proposed for accurate rice row recognition in paddy field transplanted by machine before the disappearance of row information.Firstly,Bayesian decision theory based on the minimum error was used to classify the period of collected images into three periods(T1:0-7 d;T2:7-28 d;T3:28-45 d),and resulting in the correct recognition rate was 97.03%.Moreover,secondary clustering of feature points was proposed,which can solve some problems such as row breaking and tilting.Then,the robust regression least squares method(RRLSM)for linear fitting was proposed to fit rice rows to effectively eliminate interference by outliers.Finally,a credibility analysis of connected region markers was proposed to evaluate the accuracy of fitting lines.When the threshold of credibility was set at 40%,the correct recognition rate of fitting lines was 96.32%.The result showed that the method can effectively solve the problems caused by the presence of duckweed,high-density inter-row weeds,broken rows,tilting(±60°),wind and overlap.展开更多
Black box functions, such as computer experiments, often have multiple optima over the input space of the objective function. While traditional optimization routines focus on finding a single best optimum, we sometime...Black box functions, such as computer experiments, often have multiple optima over the input space of the objective function. While traditional optimization routines focus on finding a single best optimum, we sometimes want to consider the relative merits of multiple optima. First we need a search algorithm that can identify multiple local optima. Then we consider that blindly choosing the global optimum may not always be best. In some cases, the global optimum may not be robust to small deviations in the inputs, which could lead to output values far from the optimum. In those cases, it would be better to choose a slightly less extreme optimum that allows for input deviation with small change in the output;such an optimum would be considered more robust. We use a Bayesian decision theoretic approach to develop a utility function for selecting among multiple optima.展开更多
文摘There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.
基金This work was financially supported by the Key-Area Research and Development Program of Guangdong Province(Grant No.2019B020221002)and the National Key Research and Development Program of China(Grant No.2017YFD0701105)The authors also acknowledge the anonymous reviewers for their critical comments and suggestions for improving the manuscript.
文摘Paddy field management is complicated and labor intensive.Correct row detection is important to automatically track rice rows.In this study,a novel method was proposed for accurate rice row recognition in paddy field transplanted by machine before the disappearance of row information.Firstly,Bayesian decision theory based on the minimum error was used to classify the period of collected images into three periods(T1:0-7 d;T2:7-28 d;T3:28-45 d),and resulting in the correct recognition rate was 97.03%.Moreover,secondary clustering of feature points was proposed,which can solve some problems such as row breaking and tilting.Then,the robust regression least squares method(RRLSM)for linear fitting was proposed to fit rice rows to effectively eliminate interference by outliers.Finally,a credibility analysis of connected region markers was proposed to evaluate the accuracy of fitting lines.When the threshold of credibility was set at 40%,the correct recognition rate of fitting lines was 96.32%.The result showed that the method can effectively solve the problems caused by the presence of duckweed,high-density inter-row weeds,broken rows,tilting(±60°),wind and overlap.
文摘Black box functions, such as computer experiments, often have multiple optima over the input space of the objective function. While traditional optimization routines focus on finding a single best optimum, we sometimes want to consider the relative merits of multiple optima. First we need a search algorithm that can identify multiple local optima. Then we consider that blindly choosing the global optimum may not always be best. In some cases, the global optimum may not be robust to small deviations in the inputs, which could lead to output values far from the optimum. In those cases, it would be better to choose a slightly less extreme optimum that allows for input deviation with small change in the output;such an optimum would be considered more robust. We use a Bayesian decision theoretic approach to develop a utility function for selecting among multiple optima.
