title: Genetic algorithm based approach to optimize phenotypical traits of virtual rice.
abstract: 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 Grammar). 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.
authors.affiliation: College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China. Electronic address: wlding@zjut.edu.cn.
authors.forename: Weilong
authors.initials: W
authors.lastname: Ding
authors.affiliation: College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China. Electronic address: lfxu@zjut.edu.cn.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Xu
authors.affiliation: College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wei
authors.affiliation: College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China.
authors.forename: Fuli
authors.initials: F
authors.lastname: Wu
authors.affiliation: State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China.
authors.forename: Defeng
authors.initials: D
authors.lastname: Zhu
authors.affiliation: State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China.
authors.forename: Yuping
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, University of California, Davis 95616, USA.
authors.forename: Nelson
authors.initials: N
authors.lastname: Max
mesh_terms: D065638:Absorption, Radiation; D000465:Algorithms; D003198:Computer Simulation; D056084:Genetic Fitness; D008027:Light; D009929:Organ Size; D012275:Oryza / Q000033:anatomy & histology / Q000235:genetics* / Q000528:radiation effects; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results
keywords: Functional-structural model; Genetic algorithm; Optimal design; Plant type