Abstract
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. In this paper, we show how using a cooperative, adaptive Multi-Agent System can overcome the drawbacks of such algorithms. The system resolves the problem in an iterative way by a cooperation between the constraints, modelled as agents. Results show a good convergence of the algorithm. Complete tests to validate the provided solution quality are still in progress.
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Alameda, S., Bernon, C., Mano, JP. (2014). Agent-Based Model for Phenotypic Prediction Using Genomic and Environmental Data. In: Saez-Rodriguez, J., Rocha, M., Fdez-Riverola, F., De Paz Santana, J. (eds) 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014). Advances in Intelligent Systems and Computing, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-319-07581-5_1
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DOI: https://doi.org/10.1007/978-3-319-07581-5_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07580-8
Online ISBN: 978-3-319-07581-5
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