Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Genetic algorithm based approach to optimize phenotypical traits of virtual rice.

Journal of theoretical biology - 21 Aug 2016

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

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Computer Simulation
Share to Genetic Fitness

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Organ Size

Share to Quantitative Trait, Heritable

Share to Reproducibility of Results

Source

Identifiers

PMID: 27179460
DOI: 10.1016/j.jtbi.2016.05.006

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Genetic algorithm based approach to optimize phenotypical traits of virtual rice.PMID 27179460Read ↗

Quantitative descriptions of rice plant architecture and their application.Semantic proximity Read ↗

Integration of Crop Growth Models and Genomic Prediction.Semantic proximity Read ↗

A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding.Semantic proximity Read ↗

Harnessing on Genetic Variability and Diversity of Rice (Oryza sativa L.) Genotypes Based on Quantitative and Qualitative Traits for Desirable Crossing Materials.Semantic proximity Read ↗

Characterization of functional trait diversity among Indian cultivated and weedy rice populations.Semantic proximity Read ↗

The 3D dynamic visualization simulation of rice plant based on morphological structure model and the application in phenotypic calculation.Semantic proximity Read ↗

Methods for predicting superior genotypes under multiple environments based on QTL effects.Semantic proximity Read ↗

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.Semantic proximity Read ↗

Functional mapping of tillering QTLs using the Wang-Lan-Ding model and a SSSL population.Semantic proximity Read ↗

Review on phenotype traits analysis of Rice plants by using Machine Learning TechniquesSemantic proximity Read ↗

Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing rice productivity? A simulation analysis.Semantic proximity Read ↗

Evaluation of genotypic variation in leaf photosynthetic rate and its associated factors by using rice diversity research set of germplasm.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.