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A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

2022-01-28Journal article Open access
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dc.contributor.authorAlibabaei, Khadijeh
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorLima, Tânia M.
dc.contributor.authorCampos, Maria Do Rosario Castiço De
dc.contributor.authorGirão, Inês
dc.contributor.authorMonteiro, Jorge
dc.contributor.authorLopes, Carlos M.
dc.date.accessioned2022-03-25T16:42:45Z
dc.date.available2022-03-25T16:42:45Z
dc.date.issued2022-01-28
dc.description.abstractDeep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.pt_PT
dc.description.sponsorshipThis work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/rs14030638pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/12116
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCentre for Mechanical and Aerospace Science and Technologies
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAgriculturept_PT
dc.subjectDeep Learningpt_PT
dc.subjectSmart Farmpt_PT
dc.subjectSupport Decision-Making Algorithmspt_PT
dc.titleA Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activitiespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Mechanical and Aerospace Science and Technologies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00151%2F2020/PT
oaire.citation.issue3pt_PT
oaire.citation.startPage638pt_PT
oaire.citation.titleRemote Sensingpt_PT
oaire.citation.volume14pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAlibabaei
person.familyNameGaspar
person.familyNameLima
person.familyNamecampos
person.familyNameGirão
person.givenNameKhadijeh
person.givenNamePedro Dinis
person.givenNameTânia
person.givenNamemaria do rosario castiço de
person.givenNameInês
person.identifier1710267
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id771E-3B60-A936
person.identifier.ciencia-idAF16-B452-E272
person.identifier.ciencia-idE41F-18DD-8BEF
person.identifier.orcid0000-0002-2319-8211
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-7540-3854
person.identifier.orcid0000-0003-0496-079X
person.identifier.orcid0000-0001-7201-0548
person.identifier.ridN-3016-2013
person.identifier.ridV-5052-2017
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id48661120000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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