Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection: Detection of Wheat Yellow Rust Severity Levels using Deep Learning Model - Couverture souple

Kumar, Deepak; Kukreja, Vinay

 
9786204210339: Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection: Detection of Wheat Yellow Rust Severity Levels using Deep Learning Model
Couverture souple
ISBN 10 : 6204210335 ISBN 13 : 9786204210339
Editeur : LAP LAMBERT Academic Publishing, 2021

Synopsis

In many regions of the world, wheat quality and yield losses have been increased due to wheat rust diseases. The identification of yellow rust disease along with the percentage of tissues damaged by the rust disease in terms of severity levels is very important and usually it is achieved through experienced evaluators or computer vision techniques. With the help of computer vision techniques, the cost and time should be minimized. This study presents classification model for wheat yellow rust with different severity levels of disease. It is achieved through STARGAN and Convolutional neural network (CNN). The STARGAN is proposed in this study for data augmentation. After conducting several experiments with parameters such as different epochs, batch sizes, learning rate, and dropout rate this study achieves 94.07% classification accuracy to classify wheat yellow rust from the wheat normal plant. During severity measurement, CNN achieved 94.3% validation accuracy of wheat yellow rust at high severity level.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2021
Langue
anglais
ISBN 10 
6204210335
ISBN 13 
9786204210339
Reliure
Broché
Nombre de pages
84
Coordonnées du fabricant
non disponible
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