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.