A scalable offline AI-based solution to assist the diseases and plague detection in agriculture
| cic.institucionOrigen | Laboratorio de Investigación y Formación en Informática Avanzada (LIFIA) | |
| cic.isFulltext | SI | |
| cic.isPeerReviewed | true | |
| cic.lugarDesarrollo | Laboratorio de Investigación y Formación en Informática Avanzada (LIFIA) | |
| cic.parentType | Artículo | |
| cic.version | Publicada | |
| dc.date.accessioned | 2023-06-26T14:16:06Z | |
| dc.date.available | 2023-06-26T14:16:06Z | |
| dc.identifier.uri | https://digital.cic.gba.gob.ar/handle/11746/11958 | |
| dc.title | A scalable offline AI-based solution to assist the diseases and plague detection in agriculture | en |
| dc.type | Artículo | |
| dcterms.abstract | Early detection of diseases and pests is a key factor in eradicating or minimising the damage that these may cause. In this work, a comprehensive solution is presented that is based on the composition 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 analysed 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. | en |
| dcterms.creator.author | Urbieta, Matías | |
| dcterms.creator.author | Urbieta, Martín | |
| dcterms.creator.author | Pereyra, Mauro | |
| dcterms.creator.author | Laborde, Tomás | |
| dcterms.creator.author | Villarreal, Guillermo | |
| dcterms.creator.author | Del Pino, Mariana | |
| dcterms.identifier.other | DOI:10.1080/12460125.2023.2226381 | |
| dcterms.isPartOf.series | Journal of Decision Systems | |
| dcterms.issued | 2023-06-22 | |
| dcterms.language | Inglés | |
| dcterms.license | Attribution 4.0 International (BY 4.0) | |
| dcterms.publisher | Taylor & Francis Group | |
| dcterms.subject | Agriculture | en |
| dcterms.subject | Cloud | en |
| dcterms.subject | Machine learning | en |
| dcterms.subject | Mobile | en |
| dcterms.subject | tomato | en |
| dcterms.subject | powder mould | en |
| dcterms.subject | cladosporium | en |
| dcterms.subject.materia | Ciencias de la Computación e Información |