Optimized Hybrid Feature Space for High-Efficiency Citrus Disease Diagnosis: A Fusion of Handcrafted Blue-Green-Red Color Moments and Deep Convolutional Descriptors

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:42Z
Fecha de publicación2026-01-01
ResumenAccurate and timely diagnosis of citrus diseases is essential for reducing economic losses in global agriculture. Although deep learning models provide high diagnostic accuracy, their computational demands often hinder deployment on resource-limited edge devices. To overcome this challenge, this study proposes an optimized hybrid framework for phytopathological classification. The methodology combines handcrafted descriptors (Blue-Green-Red “BGR” color statistical moments) with hierarchical spatial abstractions derived from a pre-trained Visual Geometry Group 16-layer (VGG16) deep architecture. An initial high-dimensional feature space was created by concatenating 360 handcrafted statistical descriptors and 12,800 deep textural features. By implementing a Wrapper-Greedy Stepwise selection strategy, this original space was reduced by over 96%. The resulting Elite Model identifies 12 and 18 critical attributes across two independent, transcontinental datasets (Mexico and Pakistan, respectively), effectively capturing both subtle chromatic anomalies and complex structural lesions. Experimental benchmarking confirms that this parsimonious hybrid approach delivers robust classification accuracy ranging from 87.30% to 95.23%, significantly outperforming unimodal architectures. Ultimately, this framework provides a highly efficient, interpretable, and scalable solution for real-time disease monitoring in precision agriculture.es_ES
Doihttps://doi.org/10.3390/agriculture16060711es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1695
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónAgriculturees_ES
URL relacionadohttps://doi.org/10.3390/agriculture16060711es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteAgriculture
TítuloOptimized Hybrid Feature Space for High-Efficiency Citrus Disease Diagnosis: A Fusion of Handcrafted Blue-Green-Red Color Moments and Deep Convolutional Descriptorses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorTello-Leal, Edgar
AutorMacías-Hernández, Bárbara A.
AutorRubio-Tinajero, Sarahi
AutorHernandez-Resendiz, Jaciel David
AutorRamirez-Alcocer, Ulises Manuel
AutorTello-Leal, Edgares_ES
AutorMacías-Hernández, Bárbara A.es_ES
AutorRubio-Tinajero, Sarahies_ES
AutorHernandez-Resendiz, Jaciel Davides_ES
AutorRamirez-Alcocer, Ulises Manueles_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número6es_ES
Rango de páginas711es_ES
URL relacionadahttps://doi.org/10.3390/agriculture16060711
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen16es_ES

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