An Agent-Based Model to Associate Genomic and Environmental Data for Phenotypic Prediction in Plants

Authored by Sebastien Alameda, Jean-Pierre Mano, Carole Bernon, Sebastien Mella

Date Published: 2016

DOI: 10.2174/1574893611666160617094329

Sponsors: European Union

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

Background: One of the means to increase in-field crop yields is the use of software tools to predict future yield values using past in-field trials and plant genetics. The traditional, statistics-based approaches lack environmental data integration and are very sensitive to missing and/or noisy data. Objective: In this paper, we show that a cooperative, adaptive Multi-Agent System can overcome the drawbacks of such algorithms. Method: The system resolves the problem in an iterative way by a cooperation between the constraints, modelled as agents. Results: Results show that the Agent-Based Model gives results comparable to other approaches, without having to preprocess or reconcile data. Conclusion: This collective and self-adaptive search of a solution functions like a heuristic to efficiently explore the solution space and is therefore able to consider both genetic and environmental data.

Tags

selection maize Prospects