Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection

Language: English

Published by LAP LAMBERT Academic Publishing Okt 2021, 2021

6204210335 / 9786204210339

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 84 pp. Englisch.…

Seller Inventory # 9786204210339

Title
Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection
Author
Deepak Kumar
Publisher
LAP LAMBERT Academic Publishing Okt 2021
Publication year
2021
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6204210335
ISBN 13
9786204210339
Item weight
143 grams
Dimensions
220x150x6 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

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First item£ 19.78£ 19.78
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BuchWeltWeit Ludwig Meier e.K.

Germany