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

[ Vol. 11 , Issue. 5 ]


Sebastien Alameda, Jean-Pierre Mano, Carole Bernon and Sebastien Mella   Pages 515 - 522 ( 8 )


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.


Adaptation, environmental data, genomics, multi-agent systems, phenotypic prediction.


IRIT, Université de Toulouse, 118 Route de Narbonne - 31062 Toulouse Cedex 09, France.

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