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Article

Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure

1
Big Data and HPC Center of Excellence, Department of Software Engineering, Addis Ababa Science and Technology University, P.O. Box 16417, Addis Ababa 999047, Ethiopia
2
Draper Hall, University of Albany, 135 Western Avenue, Albany, NY 12201, USA
3
Debre Zeyit Agricultural Research Institute, Debre Zeyit 999047, Ethiopia
*
Authors to whom correspondence should be addressed.
Current address: Addis Ababa, Ethiopia and Albany, ESA.
These authors contributed equally to this work.
Academic Editor: Pietro Zanuttigh
Received: 26 April 2021 / Revised: 7 June 2021 / Accepted: 10 June 2021 / Published: 2 July 2021
(This article belongs to the Special Issue Multimedia Indexing and Retrieval)
Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy. View Full-Text
Keywords: wheat disease; agriculture; computer vision; deep learning model wheat disease; agriculture; computer vision; deep learning model
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MDPI and ACS Style

Aboneh, T.; Rorissa, A.; Srinivasagan, R.; Gemechu, A. Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure. Technologies 2021, 9, 47. https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9030047

AMA Style

Aboneh T, Rorissa A, Srinivasagan R, Gemechu A. Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure. Technologies. 2021; 9(3):47. https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9030047

Chicago/Turabian Style

Aboneh, Tagel, Abebe Rorissa, Ramasamy Srinivasagan, and Ashenafi Gemechu. 2021. "Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure" Technologies 9, no. 3: 47. https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9030047

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