数字农科院2.0

Genetic algorithm based approach to optimize phenotypical traits of virtual rice

文献类型： 外文期刊

作者： Ding, Weilong;Xu, Lifeng;Wei, Yang;Wu, Fuli;Zhu, Defeng;Zhang, Yuping;Max, Nelson

作者机构：

关键词： Functional-structural model;Genetic algorithm;Plant type;Optimal design

期刊名称： JOURNAL OF THEORETICAL BIOLOGY

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年卷期：

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收录情况： SCI

摘要： How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Gtammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. (C) 2016 Elsevier Ltd. All rights reserved.

分类号： Q1

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