Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure
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, de...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/9/3/47 |
| _version_ | 1797517053728391168 |
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| author | Tagel Aboneh Abebe Rorissa Ramasamy Srinivasagan Ashenafi Gemechu |
| author_facet | Tagel Aboneh Abebe Rorissa Ramasamy Srinivasagan Ashenafi Gemechu |
| author_sort | Tagel Aboneh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:09:28Z |
| format | Article |
| id | doaj.art-41a375598ba741b18838511c9f4d8649 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-10T07:09:28Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-41a375598ba741b18838511c9f4d86492023-11-22T15:30:02ZengMDPI AGTechnologies2227-70802021-07-01934710.3390/technologies9030047Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU InfrastructureTagel Aboneh0Abebe Rorissa1Ramasamy Srinivasagan2Ashenafi Gemechu3Big Data and HPC Center of Excellence, Department of Software Engineering, Addis Ababa Science and Technology University, P.O. Box 16417, Addis Ababa 999047, EthiopiaBig Data and HPC Center of Excellence, Department of Software Engineering, Addis Ababa Science and Technology University, P.O. Box 16417, Addis Ababa 999047, EthiopiaDraper Hall, University of Albany, 135 Western Avenue, Albany, NY 12201, USADebre Zeyit Agricultural Research Institute, Debre Zeyit 999047, EthiopiaDiseases 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.https://www.mdpi.com/2227-7080/9/3/47wheat diseaseagriculturecomputer visiondeep learning model |
| spellingShingle | Tagel Aboneh Abebe Rorissa Ramasamy Srinivasagan Ashenafi Gemechu Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure Technologies wheat disease agriculture computer vision deep learning model |
| title | Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure |
| title_full | Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure |
| title_fullStr | Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure |
| title_full_unstemmed | Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure |
| title_short | Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure |
| title_sort | computer vision framework for wheat disease identification and classification using jetson gpu infrastructure |
| topic | wheat disease agriculture computer vision deep learning model |
| url | https://www.mdpi.com/2227-7080/9/3/47 |
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