1. An Agent-Based Model to Associate Genomic and Environmental Data for Phenotypic Prediction in Plants
- Author
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Jean-Pierre Mano, Sebastien Mella, Sebastien Alameda, Carole Bernon, Brennus Analytics (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Systèmes Multi-Agents Coopératifs (IRIT-SMAC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Brennus Analytics (Paris, France), and Université Toulouse III - Paul Sabatier (UT3)
- Subjects
Phenotypic Prediction ,Système multi-agents ,Biology ,Machine learning ,computer.software_genre ,Biochemistry ,Multi-Agent Systems ,Environmental data ,03 medical and health sciences ,Software ,Genetics ,Ingénierie assistée par ordinateur ,Adaptation ,Molecular Biology ,Noisy data ,030304 developmental biology ,2. Zero hunger ,Agent-based model ,0303 health sciences ,business.industry ,030302 biochemistry & molecular biology ,Genomics ,[INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering ,Modélisation et simulation ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Biotechnology ,Computational Mathematics ,[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] ,Environmental Data ,Artificial intelligence ,business ,computer - Abstract
International audience; 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 that 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 that the Agent-Based Model gives results comparable to other approaches, without having to preprocess data.
- Published
- 2016
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