CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques

AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:32:18Z
Fecha de publicación2024-01-01
ResumenAround the world, citrus production and quality are threatened by diseases caused by fungi, bacteria, and viruses. Citrus growers are currently demanding technological solutions to reduce the economic losses caused by citrus diseases. In this context, image analysis techniques have been widely used to detect citrus diseases, extracting discriminant features from an input image to distinguish between healthy and abnormal cases. The dataset presented in this article is helpful for training, validating, and comparing citrus abnormality detection algorithms. The data collection comprises 953 color images taken from the orange leaves of Citrus sinensis (L.) Osbeck species. There are 12 nutritional deficiencies and diseases supporting the development of automatic detection methods that can reduce economic losses in citrus production.es_ES
Doihttps://doi.org/10.1016/j.dib.2023.109908es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/4133
Idiomaenes_ES
EditorialELSEVIERes_ES
RelaciónData in Briefes_ES
URL relacionadohttps://doi.org/10.1016/j.dib.2023.109908es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteData in Brief
Palabra claveCitrus diseaseses_ES
Palabra claveOrange leaveses_ES
Palabra claveCitrus sinensis (L.) osbeckes_ES
Palabra claveImage analysises_ES
Palabra claveMachine learninges_ES
Palabra claveDeep learninges_ES
TítuloCitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniqueses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorGomez-Flores, Wilfrido
AutorGarza-Saldana, Juan Jose
AutorVarela-Fuentes, Sostenes Edmundo
AutorGomez-Flores, Wilfridoes_ES
AutorGarza-Saldana, Juan Josees_ES
AutorVarela-Fuentes, Sostenes Edmundoes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
URL relacionadahttps://doi.org/10.1016/j.dib.2023.109908
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen52es_ES

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