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Comparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization : a case study at a site in Portugal

2022Journal article Open access
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dc.contributor.authorAlibabaei, Khadijeh
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorAssunção, Eduardo
dc.contributor.authorAlirezazadeh, Saeid
dc.contributor.authorLima, Tânia M.
dc.contributor.authorSoares, V.N.GJ.
dc.contributor.authorCaldeira, J.M.L.P.
dc.date.accessioned2022-06-27T08:26:00Z
dc.date.available2022-06-27T08:26:00Z
dc.date.issued2022
dc.description.abstractPrecision irrigation and optimization of water use have become essential factors in agricul- ture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase productivity, reduce production costs, and maxi- mize the return on investment. Efficient water application techniques are essential prerequisites for sustainable agricultural development based on the conservation of water resources and preservation of the environment. In a previous work, an off-policy deep reinforcement learning model, Deep Q-Network, was implemented to optimize irrigation. The performance of the model was tested for tomato crop at a site in Portugal. In this paper, an on-policy model, Advantage Actor–Critic, is implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model Advantage Actor–Critic reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high production in Portugal, such as fruit, cereals, and wine, which also have large water requirements.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationALIBABAEI, Khadijeh [et al.] (2022) - Comparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization : a case study at a site in Portugal. Computers. DOI 10.3390/computers11070104
dc.identifier.doi10.3390/computers11070104pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/8001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationThis work was funded by FCT/MCTES through national funds and, when applicable, co- funded by EU funds under project UIDB/50008/2020pt_PT
dc.subjectagriculturept_PT
dc.subjectdeep learningpt_PT
dc.subjecton-policy deep reinforcement learningpt_PT
dc.subjectirrigation optimizationpt_PT
dc.titleComparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization : a case study at a site in Portugalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue7pt_PT
oaire.citation.startPage104pt_PT
oaire.citation.titleComputerspt_PT
oaire.citation.volume11pt_PT
person.familyNameGaspar
person.familyNameCaldeira
person.givenNamePedro Dinis
person.givenNameJoão
person.identifiera4GD8aoAAAAJ
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id5B19-E130-E382
person.identifier.ciencia-idA91B-85B8-C27E
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-8057-5474
person.identifier.orcid0000-0001-5830-3790
person.identifier.ridN-3016-2013
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id27067580500
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationa17d4ff5-1ff3-4dcc-b180-319e7ff3961d
relation.isAuthorOfPublication8eebc97c-5334-4f29-b7ee-71c4c436aa69
relation.isAuthorOfPublication.latestForDiscovery8eebc97c-5334-4f29-b7ee-71c4c436aa69

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