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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Deepak 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 at Chitkara University, Punjab, India. His research areas are machine learning, deep learning and agile software development.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: 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 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. Bestandsnummer des Verkäufers 9786204210339
Anzahl: 2 verfügbar
Anbieter: Books Puddle, New York, NY, USA
Zustand: New. Bestandsnummer des Verkäufers 26400894964
Anzahl: 4 verfügbar
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Zustand: New. Print on Demand. Bestandsnummer des Verkäufers 395482155
Anzahl: 4 verfügbar
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. Bestandsnummer des Verkäufers 18400894974
Anzahl: 4 verfügbar
Anbieter: moluna, Greven, Deutschland
Zustand: 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 at Chitkara University, Punjab, . Bestandsnummer des Verkäufers 523782315
Anzahl: Mehr als 20 verfügbar
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786204210339
Anzahl: 1 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786204210339
Anzahl: 1 verfügbar
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 120773571
Anzahl: 5 verfügbar