Grape Disease Detection Network Based on Multi-Task Learning and Attention Features

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:26Z
Fecha de publicación2021-01-01
ResumenThe disease-free growth of a plant is highly influential for both environment and human life. However, there are numerous plant diseases such as viruses, fungus, and micro-organisms that affect the growth and agricultural production of a plant. Grape esca, black-rot, and isariopsis are multi-symptomatic soil-borne diseases. Often, these diseases may cause leaves drop or sometimes even vanishes the plant/plant vicinity. Hence, early detection and prevention becomes necessary and must be treated on time for better grape growth and productivity. The state-of-the-art either involve classical computer vision techniques such as edge detection/segmentation or regression-based object detection applied over UAV images. In addition, the treatment is not viable until detected leaves are classified for actual disease/symptoms. This results in increased time and cost consumption. Therefore, in this paper, a grape leaf disease detection network (GLDDN) is proposed that utilizes dual attention mechanisms for feature evaluation, detection, and classification. At evaluation stage, the experimentation performed over benchmark dataset confirms that disease detection network could be fairly befitting than the existing methods since it recognizes as well as detects the infected/diseased regions. With the proposed disease detection mechanism, we achieved an overall accuracy of 99.93\% accuracy for esca, black-rot and isariopsis detection.es_ES
Doihttps://doi.org/10.1109/jsen.2021.3064060es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5266
Idiomaenes_ES
EditorialIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCes_ES
RelaciónIEEE Sensors Journales_ES
URL relacionadohttps://doi.org/10.1109/jsen.2021.3064060es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteIEEE Sensors Journal
Palabra claveDiseaseses_ES
Palabra claveFeature extractiones_ES
Palabra clavePipelineses_ES
Palabra claveSensorses_ES
Palabra claveProposalses_ES
Palabra claveObject detectiones_ES
Palabra claveComputer architecturees_ES
Palabra claveSmart sensinges_ES
Palabra clavesensores_ES
Palabra claveagriculturees_ES
Palabra claveplant diseasees_ES
Palabra claveapplicationses_ES
TítuloGrape Disease Detection Network Based on Multi-Task Learning and Attention Featureses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorDwivedi, Rudresh
AutorDey, Somnath
AutorChakraborty, Chinmay
AutorTiwari, Sanju
AutorDwivedi, Rudreshes_ES
AutorDey, Somnathes_ES
AutorChakraborty, Chinmayes_ES
AutorTiwari, Sanjues_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número16es_ES
Rango de páginas17573-17580es_ES
URL relacionadahttps://doi.org/10.1109/jsen.2021.3064060
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen21es_ES

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