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Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Predicting Grain Protein Content of Winter Wheat |
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2014 |
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22nd European Symposium on Artificial Networks, Computational Intelligence and Machine Learning. Bruges, Belgium, 2014-04-23 to 2014-04-25 |
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MA @ admin @ |
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2631 |
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Author |
Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Bayesian methods for predicting and modelling winter wheat biomass |
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2014 |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2629 |
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Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Bayesian methods for predicting LAI and soil moisture |
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2012 |
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CropM |
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11th International Conference on Precision Agriculture. Indianapolis (USA), 2012-07-15 to 2012-07-18 |
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MA @ admin @ |
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2627 |
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Author |
Mansouri, M. |
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Title |
Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods |
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2013 |
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112-137 |
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CropM |
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IGI Global |
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Hershey PA |
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Masegosa, P.; Villacorta, C.; Cruz-Corona, S.; Garcia-Cascales, M.; Lamata, J.; Verdegay, A. |
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Exploring Innovative and Successful Applications of Soft Computing |
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MA @ admin @ |
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2625 |
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Author |
Mansouri, M.; Destain, M.-F. |
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Title |
Predicting biomass and grain protein content using Bayesian methods |
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Journal Article |
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Year |
2015 |
Publication |
Stochastic Environmental Research and Risk Assessment |
Abbreviated Journal |
Stoch. Environ. Res. Risk Assess. |
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29 |
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4 |
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1167-1177 |
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Keywords |
crop model; particle filter; prediction; ensemble kalman filter; parameter-estimation; particle filters; decision-support; state estimation; model; nitrogen; navigation; tracking; systems |
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Abstract |
This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. |
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English |
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1436-3240 1436-3259 |
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CropM |
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Call Number |
MA @ admin @ |
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4664 |
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