Learning Crop Management by Reinforcement: Gym-DSSAT

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UDC.coleccionInvestigación
UDC.conferenceTitleAIAFS 2023
UDC.departamentoEnxeñaría de Computadores
UDC.grupoInvComputer Graphics & Visual Computing (XLab)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
dc.contributor.authorGautron, Romain
dc.contributor.authorPadrón, Emilio J.
dc.contributor.authorPreux, Philippe
dc.contributor.authorBigot, Julien
dc.contributor.authorMaillard, Odalric-Ambrym
dc.contributor.authorHoogenboom, Gerrit
dc.contributor.authorTeigny, Julien
dc.date.accessioned2026-04-29T08:25:34Z
dc.date.available2026-04-29T08:25:34Z
dc.date.issued2023-02
dc.descriptionM. de Castro, R. R. Osorio, Y. Torres and D. R. Llanos, "Accelerating Scientific Model Optimization with a Pipelined FPGA-Based Differential Evolution Engine", 2025 IEEE 33rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Fayetteville, AR, USA, 2025, pp. 283-283, doi: 10.1109/FCCM62733.2025.00055.
dc.description.abstract[Abstract]: We introduce gym-DSSAT, a gym environment for crop management tasks, that is easy to use for training Rein- forcement Learning (RL) agents. gym-DSSAT is based on DSSAT, a state-of-the-art mechanistic crop growth simulator. We modify DSSAT so that an external software agent can in- teract with it to control the actions performed in a crop field during a growing season. The RL environment provides pre- defined decision problems without having to manipulate the complex crop simulator. We report encouraging preliminary results on a use case of nitrogen fertilization for maize. This work opens up opportunities to explore new sustainable crop management strategies with RL, and provides RL researchers with an original set of challenging tasks to investigate.
dc.description.sponsorshipThe authors acknowledge the PDI team, in particular Karol Sierocinski, for their help. They also thank the DSSAT development team, especially Cheryl Porter for her continuous support. We acknowledge the Consultative Group for International Agricultural Research (CGIAR) Platform for Big Data in Agriculture and we especially thank Brian King. Ph. Preux, and O-A. Maillard acknowledge the support of the Métropole Européenne de Lille (MEL), ANR, Inria, Université de Lille, through the AI chair Apprenf number R-PILOTE-19-004-APPRENF. We acknowledge the support of the AIDA team at CIRAD and the outstanding working environment provided by Inria in the Scool research group. Emilio J. Padrón’s work was partially supported through the research projects PID2019-104184RBI00 funded by MCIN/AEI/10.13039/501100011033, and ED431C 2021/30, ED431F 2021/11 and ED431G 2019/01 funded by Xunta de Galicia. We thank Debabrota Basu for his help in the writing of this paper.
dc.description.sponsorshipXunta de Galicia; ED431C 2021/30
dc.description.sponsorshipXunta de Galicia; ED431F 2021/11
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01
dc.identifier.citationR. Gautron, E. J Padrón, P. Preux, J. Bigot, O.-A. Maillard, G. Hoogenboom, and J. Teigny, "Learning Crop Management by Reinforcement: gym-DSSAT", proceedings of the 2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS 2023), 2023, Washington DC, United States / Online
dc.identifier.urihttps://hdl.handle.net/2183/48133
dc.language.isoeng
dc.publisherAssociation for the Advancement of Artificial Intelligence
dc.relation.hasversionR. Gautron, E. J Padrón, P. Preux, J. Bigot, O.-A. Maillard, G. Hoogenboom, and J. Teigny, "Learning Crop Management by Reinforcement: gym-DSSAT" [poster], 2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS 2023), 2023, Washington DC, United States / Online
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104184RB-I00/ES/DESAFIOS ACTUALES EN HPC: ARQUITECTURAS, SOFTWARE Y APLICACIONES
dc.relation.urihttps://aiafs-aaai.github.io/papers/
dc.rights© 2023
dc.rights.accessRightsopen access
dc.subjectReinforcement learning
dc.subjectCrop management
dc.subjectSimulator
dc.subjectDSSAT
dc.titleLearning Crop Management by Reinforcement: Gym-DSSAT
dc.typeconference output
dspace.entity.typePublication
relation.isAuthorOfPublicationbdccb1db-e727-4b63-b2ca-1941cc096c00
relation.isAuthorOfPublication.latestForDiscoverybdccb1db-e727-4b63-b2ca-1941cc096c00

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