Hybrid Deep Learning Model by Kumar Deepak (8 results)
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 55.02
£ 2.97 shippingShips within U.S.A.Quantity: 4 available
Condition: New.
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
£ 32.10
£ 60.02 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: Neu. Hybrid Deep Learning Model for Wheat Yellow Rust Disease Detection | Detection of Wheat Yellow Rust Severity Levels using Deep Learning Model | Deepak Kumar (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204210339 | Verantwortliche Person für die EU: preigu GmbH &… Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Language: English
Published by LAP LAMBERT Academic Publishing Okt 2021, 2021
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 35.24
£ 19.72 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. 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 seve…rity 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.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 54.24
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 57.42
£ 8.53 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND.
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 30.25
£ 42.00 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar DeepakDeepak Kumar is presently pursuing a Ph.D. in Computer Science & Engineering (CSE) from Chitkara university, Punjab, India. Dr. Vinay Kukreja is presently working as an Associate professor a…t Chitkara University, Punjab, .
Language: English
Published by LAP LAMBERT Academic Publishing Okt 2021, 2021
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 35.24
£ 51.44 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 36.11
£ 52.06 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
