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:
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Model Documentation:
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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