Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

A new method for prediction of Vigna mungo millet disease based on deep learning

Type de record:

paper
Créateur:
RagChaBhaS.
Éditeur:
Elsevier BV
Hôte:

Visit

doi.org

Languages

Duala

Licenses

https://www.elsevier.com/tdm/userlicense/1.0/https://www.elsevier.com/legal/tdmrep-licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/

Similaires

Detection and Classification of Yellow Mosaic Disease in Vigna mungo using Convolutional Neural Network Deep Learning ModelsA NEW METHOD BASED ON A DEEP LEARNING MODEL FOR AUTOMATIC RECOGNITION OF CELLS IN CYTOLOGICAL IMAGESVigna mungo (L.) Hepper VI064545Vigna mungo (L.) Hepper VI035069Vigna mungo (L.) Hepper, VIG 1765Assessing a mobile-based deep learning model for plant disease surveillance

Detection and Classification of Yellow Mosaic Disease in Vigna mungo using Convolutional Neural Network Deep Learning Models

The yield of the black gram crop is negatively impacted by Yellow Mosaic Disease (YMD). Both quantit

A NEW METHOD BASED ON A DEEP LEARNING MODEL FOR AUTOMATIC RECOGNITION OF CELLS IN CYTOLOGICAL IMAGES

A NEW METHOD BASED ON A DEEP LEARNING MODEL FOR AUTOMATIC RECOGNITION OF CELLS IN CYTOLOGICAL IMAGES

Poster presented at the Deep Learning Indaba 2022 by Amin KHOUANI

Vigna mungo (L.) Hepper VI064545

Plant Genetic Resource.
Taxonomy: Vigna mungo (L.) Hepper
Common name(s): Black gram
Other

Vigna mungo (L.) Hepper VI035069

Plant Genetic Resource.
Taxonomy: Vigna mungo (L.) Hepper
Common name(s): Black gram
Other

Vigna mungo (L.) Hepper, VIG 1765

PGRFA accession of IPK Gatersleben Plant Genetic Resource Taxon: Vigna mungo (L.) Hepper Accession n

Assessing a mobile-based deep learning model for plant disease surveillance

Convolutional neural network models (CNNs) have made major advances in computer vision tasks in the