Scientia Agricultura Sinica ›› 2013, Vol. 46 ›› Issue (11): 2220-2231.doi: 10.3864/j.issn.0578-1752.2013.11.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Parameters Optimization of Rice Development Stages Model Based on Individual Advantages Genetic Algorithm

 ZHUANG  Jia-Xiang, JIANG  Hai-Yan, LIU  Lei-Lei, WANG  Fang-Fang, TANG  Liang, ZHU  Yan, CAO  Wei-Xing   

  1. 1.College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095
    2.National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095
  • Received:2012-11-05 Online:2013-06-01 Published:2013-01-18

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Abstract: 【Objective】 Fast and accurate estimation of crop growth model parameters is the basis of the crop system simulation.【Method】In this paper, a newly improved genetic algorithm, named individual advantage genetic algorithm (IAGA), is proposed and applied to the field of the parameters evaluation of the rice development stages model. Firstly, the individual advantage operator was introduced into the genetic algorithm, thus improved the mutation operator and the update strategy of population. Secondly, two rice development stages models, RiceGrow and ORYZA2000, were coupled with IAGA in a way of total embedment, and realized automatic estimation of the parameters in the models. At last, a series of comparative experiments were carried out to verify the effectiveness of IAGA with multi-year field trial data of Shanyou63, and other four rice varieties in Xuzhou, Gaoyao, etc.【Result】The experimental verification results which cover RMSE＜3.05 d, NRMSE＜3.19%, MDA＜2.41 d, R2＞0.9877, indicated that the accuracy of the model parameters obtained by IAGA was pretty high. The amount of data used for the parameters estimation had little effect on the results. The maximum NRMSE of the fitting results increased from 2.58% to 3.08% when the amount of data used for the parameters estimation from three years to six years was changed. More accurate model parameters were obtained when we select the data of every other year, including the maximum and minimum value of the whole growth period. Compared with the shuffled complex evolution algorithm, genetic simulated annealing algorithm and standard particle swarm algorithm, IAGA could obtain more accurate model parameters.【Conclusion】The IAGA can achieve automatic determination of rice development stages model parameters, therefore it provides an effective and new method for estimating parameters for crop growth model quickly and accurately.

Key words: rice , development stages model , parameters optimization , genetic algorithm , RiceGrow , ORYZA2000

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