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dc.contributor.author
Urbieta, Mario Matías
dc.contributor.author
Urbieta, Martin Cesar
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Pereyra, Mauro
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Laborde, Tomas
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Villarreal, Guillermo
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Delpino, Mariana
dc.date.available
2024-01-11T12:02:48Z
dc.date.issued
2023-06
dc.identifier.citation
Urbieta, Mario Matías; Urbieta, Martin Cesar; Pereyra, Mauro; Laborde, Tomas; Villarreal, Guillermo; et al.; A scalable offline AI-based solution to assist the diseases and plague detection in agriculture; Taylor & Francis Ltd; Journal of Decision Systems; 6-2023; 1-18
dc.identifier.issn
1246-0125
dc.identifier.uri
http://hdl.handle.net/11336/223298
dc.description.abstract
Early detection of diseases and pests is a key factor in eradicating or minimizing the damage that {these} may cause.In this work, a comprehensive solution is presented that is based on the compositing of existing cloud solutions and mobile tools to detect in-situ issues.The platform presented was used for the detection of powdery mildew and Cladosporium diseases in tomatoes.The results of using the approach to carry out this task were more than satisfactory since it managed to correctly detect the symptoms, having mAP of 0.41 in at least some of these symptoms. We analyzed the performance of our dataset on the one hand and the combination of PlantDoc dataset on the other hand. This shows that the platform can be used in the agriculture sector, as an additional tool for detecting diseases and pests in order to combat the problem and reduce its consequences.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Taylor & Francis Ltd
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
AGRICULTURE
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AGRICULTURE
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AND CLADOSPORIUM
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CLOUD
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MACHINE-LEARNING
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MOBILE
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POWDER MOULD
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TOMATO
dc.subject.classification
Ciencias de la Computación
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
A scalable offline AI-based solution to assist the diseases and plague detection in agriculture
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-01-09T14:58:17Z
dc.identifier.eissn
2116-7052
dc.journal.pagination
1-18
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Urbieta, Mario Matías. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
dc.description.fil
Fil: Urbieta, Martin Cesar. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
dc.description.fil
Fil: Pereyra, Mauro. Universidad Nacional de La Plata. Facultad de Informática; Argentina
dc.description.fil
Fil: Laborde, Tomas. Universidad Nacional de La Plata. Facultad de Informática; Argentina
dc.description.fil
Fil: Villarreal, Guillermo. Universidad Nacional de La Plata. Facultad de Informática; Argentina
dc.description.fil
Fil: Delpino, Mariana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomía; Argentina
dc.journal.title
Journal of Decision Systems
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/12460125.2023.2226381
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/12460125.2023.2226381
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